Posted by Max Greenwald · Co-founder & CEO, Warmly
Today, we’re announcing that Warmly has entered into an agreement to be acquired by HubSpot.
Six years ago, Carina Boo, Alan, and I started Warmly with an idea about how go-to-market could work better. We never imagined it would lead us here.
The world was different. AI agents weren’t running go-to-market workflows. MCP wasn’t a protocol. The phrase “inbound agent” didn’t exist yet.
Engineering had become cohesive, scalable, and extensible. Go-to-market was not. Its tools and context were siloed, its data was often stale, and teams struggled to coordinate the right buyer experience in real time. We believed go-to-market could become just as rigorous if it had the right building blocks.
So we set out to answer the 4 foundational questions systematically:
Who is the buyer? What do they care about? When do they care? How do we help?
Our first building block was intent. Warmly became best known for person-level website intent, helping companies identify more than half of the visitors who never filled out a form.
But insight without action has limited value. So we built Inbound Agent to turn intent into personalized conversations, meetings, and follow-up. Every interaction added context and learnings that improved the next inbound interaction.
Then TAM Agent took that loop beyond the website, helping teams engage ideal-fit buyers before they arrived. Inbound informed outbound. Outbound created new inbound. Every interaction made the next one more relevant.
The deeper we went into AI, the clearer it became: as execution gets easier to scale, judgment becomes more valuable. Marketing comes back to its most fundamental question: how do we actually help buyers and earn their attention?
HubSpot has spent more than twenty years building an integrated platform for the customer journey. Its hubs, data, workflows, and products were designed to work together rather than forcing customers to stitch together dozens of disconnected systems.
What stood out to us was not only the breadth of the platform, but the coherence underneath it.
When we got to know Yamini, Jared, Angela, and the broader HubSpot team, the fit became clear. HubSpot’s starting point has always been the customer, not the technology. We share a belief that the next generation of GTM will be built by people and agents working as one system. Agents carry context and learning from outcomes, and people bring judgment, taste, and empathy.
“The gap between building demand and winning deals is one of the hardest problems in GTM, and Warmly has cracked it in a way that directly benefits HubSpot customers. We’re excited to welcome the team to HubSpot.”
— Angela DeFranco, GM, VP of Product, HubSpot
What this means for Warmly customers
Today, your existing contract, pricing, account team, product experience, and integrations remain unchanged. Warmly’s agents will continue running as they do.
We remain focused on serving you and delivering on our commitments.
Longer term, our ambition is to make Warmly even more powerful for our customers by connecting our context and agent capabilities across HubSpot’s customer platform.
A thank you
To the customers, investors, advisors, partners, and supporters we’ve had the privilege to know and work alongside: thank you. Your trust, candor, and belief gave us the room to learn, adapt, and become the company we are today. We’re grateful for everything we’ve built together and honored to keep serving this community.
To the Warmly team, past and present: you took on one of the hardest and fastest-moving problems in B2B and built something real. You also had the courage to reinvent Warmly as the market changed, without losing sight of the customer. This milestone belongs to you.
What comes next
I keep coming back to the responsibility in front of us.
AI is moving faster than any one of us can fully absorb. As agents become part of more marketing, sales, and customer experiences, they are going to shape how millions of people interact with businesses.
Those experiences can become dramatically more useful or dramatically more intrusive.
We have the privilege and responsibility to help guide this toward something better: experiences that arrive with context, help instead of interrupt, and earn the trust they ask for.
That work is bigger than what any one company can do alone. We’re honored to continue it with HubSpot and to build the next chapter the way we always have.
Signing off as we’ve always done, but this time with more excitement than ever.
Warmly, Max
June 30, 2026
The GTM Brain: Why the Next Trillion-Dollar Platform Will Own Decisions, Not Data
Time to read
The Infinite Execution Paradox
Something strange has happened in go-to-market.
We've built tools that send millions of emails. Products like Salesforce, Clay, Parallel, and Exa can index entire markets in hours. LLMs generate near-infinite personalized copy, scripts, and chat conversations. Generic orchestration tools wire events together in minutes.
Execution has become effectively infinite.
And yet, pipeline is harder than ever to build. Response rates are cratering. Buyers are drowning in noise. The best SDRs spend their days sifting through false positives while real opportunities slip through the cracks.
The paradox is this:we've solved the wrong problem.
We built infrastructure for doing more. We should have built infrastructure for knowing what to do.
The bottleneck to get pipeline is no longer execution. It's decision quality. Given virtually unlimited execution capacity, how do we allocate finite resources (e.g. human time, inbox space, ad dollars, brand capital) to the right accounts, at the right time, with the right plays?
This is the question Warmly was built to answer.
Part I: Meet the Third Wave of Enterprise AI
The First Wave: 2010-2024: Static Workflows, Human-Driven Decisions
The first wave digitized GTM but didn't automate judgment. Salesforce gave us a place to store customer records. HubSpot automated email sequences. Outreach systematized sales cadences. Gong recorded calls. Each tool solved a specific workflow problem. But the human remained the reasoning layer. Software stored data and executed predefined rules. Humans decided which accounts to prioritize, what to say, when to reach out, and how to respond. The tools were productivity multipliers, not decision-makers. This wave created enormous value. It also created enormous complexity. The average enterprise GTM stack now has 30+ tools, each with its own data silo, its own logic, its own view of the customer. Humans became the integration layer, manually stitching context across systems that were never designed to talk to each other. The First Wave asked: "How do we help humans do GTM faster?"The Second Wave would ask a different question.
The Second Wave (2024-2025): Static Data, Information Retrieval
The initial wave of enterprise AI applications targeted verticals with a critical characteristic: there is a right answer and it doesn't change much.
Take these AI vertical applications as examples:
Legal AI: Harvey raised $300M and is valued at over $1B. Why? Legal precedent is static. Case law from 1954 still applies today. The corpus is fixed. The task is retrieval and synthesis over documents that were written decades ago.
Code AI: Cursor has become the fastest-growing developer tool in history. Why? Programming languages have formal grammars. Code either compiles or it doesn't. Tests either pass or fail. There's a verifiable ground truth.
Medical AI: Perhaps the most developed category, with tens of billions flowing into companies tackling physician burnout, operational inefficiencies, and diagnostic accuracy. Abridge alone is worth more than most public SaaS companies. Anatomy doesn't change, drug interactions are catalogued, clinical guidelines are documented.
Customer Support AI: Sierra is winning this category by building AI agents that handle inquiries, resolve issues, and escalate appropriately. The problem space is bounded: product documentation is fixed, common issues follow patterns, resolution paths are well-defined.
Recruiting AI: Mercor is winning here through AI that screens candidates, conducts assessments, and matches talent to roles. Job requirements are structured, skills are enumerable, candidate evaluation follows established frameworks.
These companies are creating enormous value. But notice what they all have in common: The answer key exists. The corpus is stable. The task is pattern matching.
The AI doesn't need to learn how medicine, law, coding, support, or recruiting works in real-time. It needs to retrieve and reason over information that was true yesterday and will be true tomorrow.
This is why RAG (Retrieval-Augmented Generation) became the dominant architecture. It works beautifully when:
The answer exists somewhere in your corpus
The corpus doesn't change much
Retrieval quality is the binding constraint
But what happens when none of these conditions hold?
The Third Wave (2026-2027): Dynamic Environments, Continuous Learning
The next frontier of AI isn't information retrieval. It's dynamic reasoning in environments where the answer key is always changing.
Consider GTM (Warmly’s space):
Your ICP shifts as your product evolves
Competitive positioning changes quarterly
Buyer personas vary by segment and market condition
What worked last quarter may not work this quarter
Every deal is different, and every company's GTM motion is unique
Bluntly put: the world changes.
The "right" answer depends on context that didn't exist six months ago. The model needs to learn continuously from outcomes, not just retrieve from documents.
The companies that win this wave will build something fundamentally different: not AI that retrieves answers, but AI that develops judgment through continuous interaction with dynamic environments.
What Palantir Understood First
Before LLMs, Palantir competed with Snowflake and Databricks on data infrastructure. The market saw them as enterprise data platforms, expensive, complex, government-focused.
Post-LLMs, Palantir no longer believes they have any competitors.
Why? Because they made a different architectural bet.
Snowflake and Databricks optimized for SQL and query throughput: get raw data into tables, run fast analytical reads, ship dashboards and models on top. They built infrastructure for answering questions about data.
Palantir built an ontology, a world model where data is represented as objects, relationships, and properties. Nouns, verbs, adjectives. Named entities, typed relationships, constraints. Not tables and joins, but the way humans actually think about their domain.
When LLMs arrived, this ontology became the perfect interface. Models don't want a trillion rows. They want a structured, language-shaped substrate: something you can linearize into a coherent prompt, traverse, and act on.
The results speak for themselves with Palantir: 30%+ year-over-year growth accelerating, 50%+ growth in U.S. Commercial, one of the fastest-growing enterprise software stocks.
The market is recognizing something important: ontology beats query optimization when AI is the consumer of your data. It allows AI to reason over your business the way humans actually think about it—not as tables and joins, but as entities, relationships, and meaning.
Palantir proved the thesis. Now the question is: who builds the ontology for each vertical?
The Event Clock: what happened, in what order, with what reasoning
We've already built trillion-dollar infrastructure for the State Clock:
Salesforce knows the deal is "Closed Lost"
Snowflake knows your ARR
HubSpot knows the contact's email.
The Event Clock barely exists.
Consider what your CRM actually knows about a lost deal:
The state: Acme Corp, Closed Lost, $150K, Q3 2025
What's missing: You were the second choice. The winner had one feature you're shipping next quarter. The champion who loved you got reorganized two weeks before the deal died. The CFO had a bad experience with a similar vendor five years ago, information that came up in the third call but never made it into any system.
This pattern is everywhere in GTM:
The CRM says "closed lost." It doesn't say you were one executive meeting away from winning until their CRO got fired.
The opportunity shows a 20% discount. It doesn't say who approved the deviation, why it was granted, or what precedent it set.
The sequence shows 47% reply rate. It doesn't say that every reply came from companies with a specific tech stack you've never documented.
The account is marked "churned." It doesn't say the champion left, the new VP has a competing vendor relationship, and the budget got reallocated to a different initiative.
The reasoning connecting observations to actions was never treated as a recordable table in a spreadsheet. It lived in heads, Slack threads, deal reviews that weren't recorded, and the intuitions of reps who've since left.
The Fragmentation Tax
Every organization pays a hidden cost for this missing layer. We call it the fragmentation tax: the expense of manually stitching together context that was never captured in the first place.
Different functions use different tools, each with its own partial view of the same underlying reality:
Sales lives in Salesforce
Marketing lives in HubSpot or Marketo
Support lives in Zendesk or Intercom
Product lives in Amplitude or Mixpanel
Leadership lives in spreadsheets and dashboards
When a rep needs the full picture of an account, they open six tabs, cross-reference timestamps, ping three colleagues on Slack, and piece together a narrative that will be forgotten by next week.
The fragmentation tax compounds. As organizations scale, the tax grows faster than headcount. As AI automation scales, the tax becomes the binding constraint, because agents inherit the fragmentation of the systems they query.
Why This Matters for AI Agents
This gap didn't matter when humans were the reasoning layer. The organizational brain was distributed across human heads, reconstructed on demand through conversation.
Now we want AI systems to make decisions, and we've given them nothing to reason from.
We're asking models to exercise judgment without access to precedent. It's like training a lawyer on verdicts without case law. The model can process information, but it can't learn from how the organization actually makes decisions.
Data warehouses were built to answer "what happened?" They receive data via ETL after decisions are made. By the time data lands in a warehouse, the decision context is gone.
Systems of record were built to store current state. The CRM is optimized for what the opportunity looks like now, not what it looked like when the decision was made. When a discount gets approved, the context that justified it isn't preserved.
AI agents need something different. They need the event clock, the temporal, contextual, causal record of how decisions actually get made.
The Memory Problem: Why LLMs & RAG Can't Do This Alone
Even the most powerful AI models have a fundamental limitation: they can't remember.
LLMs process text in units called tokens. Every model has a maximum context window—the total number of tokens it can “see” at once. GPT-5.2 supports ~400K tokens, Claude 3.5 Sonnet supports ~200K tokens, and Google’s Gemini 2.0 supports up to 1 million tokens.
This sounds like a lot. It isn't.
A single week of GTM activity for a mid-market company might include:
50,000 website visits with behavioral data
10,000 email sends and responses
500 call transcripts (averaging 5,000 words each)
2,000 CRM activity records
1,000 Slack threads about deals
Thousands of enrichment data points
That's easily 10-50 million tokens, 100x more than even the largest context windows.
When context overflows, models either drop it (truncation) or compress it (summarization). Both approaches degrade the model's ability to learn from precedent, recognize patterns, make consistent decisions, and build institutional memory.
RAG (Retrieval-Augmented Generation) is the standard workaround: store documents externally, retrieve relevant chunks, stuff them into context. RAG works for static domains like legal research.
RAG fails for GTM because:
The "relevant" context isn't obvious in advance. When deciding whether to prioritize Account A vs Account B, the retrieval problem is as hard as the decision problem itself.
Temporal relationships matter. RAG retrieves documents, not timelines. It can't answer "did her engagement increase or decrease over the past month?"
Identity resolution isn't automatic. RAG finds documents mentioning "S. Chen" and "Sarah" and "@sarah" and "schen@acme.com" but doesn't know they're the same person.
Synthesis requires structure. Reasoning requires knowledge graphs, not document chunks.
Features must be computed, not retrieved. "This account has 3+ buying committee members who visited the pricing page in the last 7 days" isn't a document to retrieve. It's a computation over structured data.
This is why the GTM Brain exists.
We're not replacing LLMs, we're giving them persistent, structured memory they can't build themselves.
They call this the context graph: a living record of decision traces stitched across entities and time, so precedent becomes searchable.
This insight is exactly right. But it's incomplete.
You can't capture decision traces without first solving the operational context problem inherent to GTM: identity resolution, entity relationships and temporal state (the substrate that makes decision graphs possible).
And you can't build a general-purpose context graph that works for every domain. The companies that win will build domain-specific world models that encode how their particular category actually works.
For GTM, that means building a system that understands not just what happened, but why buyers buy, why deals die, what signals predict action, and how to allocate scarce resources against infinite opportunities.
That's what Warmly is building: the GTM Brain made up of a Context Graph.
Part II: Background - The Need For a New Kind of Learning in GTM
There's a reason Tesla's Full Self-Driving is the most instructive analogy for what we're building.
Like GTM, driving is a domain where:
The environment is dynamic and unpredictable
There's no static answer key, every situation is unique
Decisions must be made in real-time under uncertainty
The "right" action depends on context that changes constantly
Human judgment is the baseline to beat
Tesla's approach to this problem offers a blueprint for building AI systems in dynamic domains.
Imitation Learning: Watching Humans Do the Work
Tesla's breakthrough wasn't building better sensors or more powerful computers. It was imitation learning at scale.
Here's how it works:
Observe: Millions of Tesla vehicles capture how humans actually drive, like steering inputs, braking patterns, lane changes, reactions to unexpected events.
Model: Neural networks learn to predict what a human driver would do in any given situation. Not what a rule-based system says they should do, but what they actually do.
Simulate: Tesla built a massive simulation environment where AI can practice billions of driving scenarios without risk.
Refine: When the AI makes a mistake in the real world, that edge case gets added to the training data. The system continuously improves.
Verify: Before deploying updates, Tesla runs shadow mode, the AI makes decisions in parallel with human drivers, and the system compares outcomes.
The key insight: you don't program driving rules. You learn them from the accumulated experience of millions of human drivers.
World Models: The Simulation Advantage
Tesla calls this learned representation a "world model." In GTM, we call ours the Context Graph: a living record of entities, relationships, decision traces, and temporal facts that enables AI to reason about your market the way experienced sellers actually think about it.
The world model enables simulation, prediction, and crucially, counterfactual reasoning. What would have happened if I had braked earlier? If I had changed lanes? This is how the system learns from near-misses, not just crashes.
The GTM Parallel
A GTM Brain using a context graph would apply the same architecture:
Observe: Ingest every signal from the GTM environment like website visits, email engagement, CRM activities, call transcripts, product usage. Watch what human sellers do in response to these signals.
Model: Learn the patterns that predict success. Which signals indicate buying intent? Which messaging resonates with which personas? Which accounts look like your best customers?
Simulate: Before committing resources to an account, simulate the likely outcomes. What's the probability of conversion? What's the expected deal size? What's the optimal engagement strategy?
Refine: When deals close or die, capture the outcome and feed it back into the models. Learn from every success and failure.
Verify: Run the AI's recommendations in shadow mode against human judgment. Measure accuracy. Improve.
Just as Tesla doesn't hand-code driving rules, we don't hand-code GTM rules. We learn them from the accumulated experience of thousands of deals.
Why This Approach Wins
The imitation learning approach has three structural advantages over rule-based systems:
It handles complexity that can't be specified. Driving has millions of edge cases. You can't write rules for all of them. But you can learn from how humans handle them. GTM is the same. Every company has unique dynamics. Every buyer is different. Every deal is shaped by context that's impossible to anticipate. The only way to handle this complexity is to learn from experience.
It improves continuously. Rule-based systems are static. Learning systems improve with use. Every mile driven makes Tesla's AI better. Every deal processed makes the GTM Brain smarter. The system compounds.
It captures tacit knowledge. The best human sellers have intuition that they can't articulate. They "just know" which deals are real and which are tire-kickers. They "just know" when a champion is losing internal support.
This tacit knowledge is embedded in their behavior, even if they can't explain it. Imitation learning captures it by observing what experts do, not what they say.
Part III: Enter the GTM Brain (from LLM + RAG to Context Graph) - what Warmly.ai is building
Beyond RAG: A GTM-Native Context Graph
What the market needs is not an AI wrapper. Not another chatbot or "AI for your CRM." It needs a stateful GTM decision system that:
Ingests millions of buyer signals (website, CRM, product, intent, social)
Resolves people and companies across tools using a proprietary identity graph
Builds a temporal context graph of your market, an ontology of entities, relationships, and facts custom-tailored to each company
Computes mathematically grounded probabilities and expected values for every account and contact
Exposes a Human Strategy Layer so leaders can steer the system
Decides what to do next (or not do) under real constraints
Orchestrates agents across web research, enrichment, buying committee mapping, inbound, outbound, and response
Continuously improves itself via backtesting and simulation
It replaces both the GTM software stack and a large portion of the GTM people stack (e.g. SDRs, RevOps, Marketing Ops, researchers, analysts) with a single, self-learning engine.
The OODA+L Architecture
The GTM Brain operates as a closed-loop system:
Observe: Raw GTM events and data flow in continuously, like website visits, CRM activities, email engagement, product usage, intent signals, people moves, firmographics.
Orient: The system maintains a world model, an ontology of companies, contacts, signals, segments, and plays. Features are computed. Models generate predictions.
Decide: The Policy Layer maps state to actions (including the decision to do nothing) under real constraints (e.g. inbox limits, ad budgets, AE capacity, brand fatigue)
Act: Agents execute work such as research, enrichment, buying committee mapping, inbound chat, outbound sequences, response handling.
Learn: Every outcome feeds back into the system. Models improve. Policies adapt. The world model expands.
The Architecture Split
This system has a clear separation of concerns that makes it structurally different from pure LLM approaches:
LLMs narrate: Recommendations, messaging angles, next best action (probabilistic)
Summary stores remember: "Account XYZ looks like ABC; historically, do this" (persistent)
This matters because LLMs are expensive and weak at real-time sifting across huge context windows. They can't build and condense a world model in real-time. But they're brilliant at reasoning over a world model that's already built.
The GTM Brain stores exactly the right context, primitives that model to the GTM world, rather than asking an LLM to reconstruct context from scratch every time.
Why "GPT Wrappers" Can't Do This
It’s a bit in the weeds but here is the architectural problem that separates production systems from demos using base LLMs:
GPT wrappers try to build context at inference time leading to what we call the “inference time trap”. The agent queries multiple systems, stitches together data, reasons over it, and generates a response, all in one request. This approach has fatal flaws:
Token consumption: Every request rebuilds context from scratch. Costs explode.
Latency: Minutes to assemble context before reasoning can start. Real-time use cases become impossible.
Hallucination: Model fills gaps when data is missing. 80% accuracy isn't acceptable for GTM decisions.
Inconsistency: Different context windows produce different answers. Same question, different day = different prioritization.
No learning: Context is discarded after each request. Can't improve from outcomes.
Our approach: pre-compute, store, serve
The context is computed, stored, and summarized ahead of time. When an agent needs to act, whether responding to a chat message or deciding which account to prioritize, it queries pre-computed state, not raw data. The reasoning happens at the edge, fast, with the right context already in memory.
You can't vibe-code this. A weekend hackathon can build a demo that queries your CRM and generates a personalized email. It cannot build an identity graph that resolves millions of signals to canonical entities, a temporal fact store that tracks state changes with validity periods, real-time streaming infrastructure, async job systems for multi-step workflows, summary stores that compress years of history, and feedback loops that connect outcomes to model updates.
This is production infrastructure. It takes years to build and battle-test. But the result is the difference between a prototype that breaks and hallucinates and a production system that closes deals.
The Architecture Difference
How does this compare in practice?
1. Decision Quality
Scenario: Rep asks "Who should I focus on today?"
Normal LLM: "Here are your 47 open opportunities sorted by close date."
GTM Brain: "Focus on Acme Corp. Why: 3 buying committee members visited pricing this week. They look like Omega Inc right before they closed. Beta Inc can wait—their champion is OOO until Thursday."
Impact: Reps spend 80% less time researching, 3x more time in conversations that convert.
2. Learning
Scenario: Deal with TechStart Inc just went "Closed Lost"
Normal LLM: Status updated to "Closed Lost." Nothing else changes. Next similar deal makes the same mistakes.
GTM Brain: System captures: "Lost because champion left 2 weeks before close." Six months later, flags a new deal: "Warning: Champion at CloudCo just updated LinkedIn to 'Open to Work'—same pattern as TechStart loss. Expand to other stakeholders now."
Impact: Deal-killing patterns surface 6 months faster than manual analysis. Mistakes made once are never repeated.
3. Cross-System Temporal Context
Scenario: Rep switching between Gong, CRM, email, and LinkedIn
Normal LLM: Each tool shows siloed data. "Wait, is the Sarah from that Gong call the same Sarah who emailed me?" Rep opens 6 tabs, spends 15 minutes piecing it together.
GTM Brain: "Sarah Chen: CFO at Acme. Timeline: Attended webinar (March 3) → Visited pricing (March 10) → Scheduled demo (March 12) → Joined call (March 15) → Asked about SOC2. She's your champion."
Scenario: Account has 100,000 website visits, 5,000 emails, 200 calls over 2 years
Normal LLM: Tries to retrieve raw data. 100,000 visits = millions of tokens. Context window explodes. Falls back to: "Acme has shown interest in your product."
GTM Brain: Compacts into ontological format (~500 tokens) that preserves everything an agent needs to execute flawlessly.
What GTM Brain stores instead of raw data:
Account: Acme Corp: Series B Fintech, 180 employees, SF-based
Buying Committee: Sarah Chen (CFO, Champion), Mike Torres (CTO, Evaluator), Lisa Park (VP Sales, End User)
Intent Signals: Sarah: Pricing 12x, ROI calc 3x. Mike: API docs 8x, Security 5x, asked about SOC2
Intent Score: 87/100 (↑34% this month)
Stage: Evaluation. Similar accounts: 73% convert in 45 days
Impact: 100,000 raw events → 500 tokens. Fits in context.
5. Temporal Reasoning
Scenario: Rep asks "Should we re-engage TechCorp?"
Normal LLM: "TechCorp is a closed-lost opportunity from 6 months ago."
GTM Brain: "Yes, re-engage. When you lost them in Q2, they had 50 employees and couldn't afford enterprise pricing. They now have 180 employees and just raised Series C. The blocker (budget) is resolved. Your champion Alex is still there."
Impact: Lost deals automatically resurface when conditions change. Pipeline you thought was dead comes back to life.
6. Event Clock
Scenario: Deal marked "Closed Lost" — what actually happened?
Normal LLM (State Clock only): "Acme Corp: Closed Lost. Amount: $150K. Close Date: Q3 2025."
GTM Brain (State + Event Clock): "Lost to Competitor X. You were second choice—they had API webhooks (you're shipping next quarter). Champion Mike got reorged 2 weeks before close. New VP had prior relationship with Competitor X's CEO. Lesson: Identify single-threaded deals earlier."
Impact: Every loss becomes a lesson. Every win becomes a playbook. Institutional knowledge compounds instead of walking out the door.
7. Feedback Loop
Scenario: Your team runs 10,000 sequences this quarter
Normal LLM: Data goes to OpenAI/Anthropic. Their models get smarter. Yours stays the same.
GTM Brain: Every reply, open, click, and conversion feeds back into YOUR model. "Sequences mentioning competitor X convert 30% better in enterprise." Your system improves. Theirs doesn't see your data.
Impact: Your GTM intelligence is proprietary. Competitors can copy features. They can't copy the accumulated decision intelligence inside your context graph.
The 100% Precision Primitive
Here's the insight that separates Warmly from everything else in the market:
When you want to automate GTM for B2B, it can't be 80% right. It needs to be 100% right, or at least better than a human, for full automation to be possible.
For example, GTM workflows are pipelines. Each step depends on the previous step being correct. If you have five steps in your automation, identity resolution, company enrichment, ICP matching, intent scoring, message personalization, and each is 80% accurate, your end-to-end accuracy isn't 80%. It's:
0.8 × 0.8 × 0.8 × 0.8 × 0.8 = 32.8%
And 32.8% sucks!
Two-thirds of your fully automated outreach is wrong in some meaningful way.
Now consider what "wrong" means at each step:
Wrong Email: Email bounces or reaches wrong person
Wrong enrichment: They changed jobs and work at a different company now
Wrong ICP match: They work at a government company when you don’t sell to government
Wrong Intent: You de-anonymized the wrong person
Wrong personalization: You sent generic outreach messages that get instantly dropped in the spam folder
This is why every primitive must work at production quality before composition is possible.
Production demands 99% or more, and that last stretch can take 100x more work.
So we build primitives, identity resolution, buying committee mapping, signal scoring, account prioritization, entity extraction, temporal reasoning, each designed to accomplish specific tasks with near-perfect precision.
These primitives are then composed into end-to-end workflows. When each component works at production quality, the whole system can run autonomously.
Part IV: The GTM Brain Advantage - The 3 Layers
The ultimate vision for the GTM Brain is to become what Palantir built for government intelligence: a context graph where data is represented the way humans actually reason about it, as entities, relationships, and temporal facts, not tables and joins.
The GTM Brain follows a three-layer architecture:
Content Layer (Evidence): Immutable source documents, the evidence trail. Emails, call transcripts, website sessions, CRM activities. Content is never edited, merged, or deleted. It's the canonical record of what was captured.
Entity Layer (Identity): What content mentions, such as people, organizations, places, products, events. This is where identity resolution happens. "Mike Torres" in an email, "M. Torres" in a meeting transcript, and "@miket" in Slack become the same person.
Fact Layer (Assertions): What content asserts, such as temporal claims about the world. Not just "the account is in-market" but "the account started showing intent on March 15" and "the intent signal weakened on August 3 when their budget got frozen." Each fact has a validity period, a status, and links to the entities and content it references.
This three-layer architecture enables something traditional GTM tools can't do: simulation.
Want to know what would happen if you changed your ICP? Run a simulation over the ontology.
Want to understand why a certain segment isn't converting? Query the fact layer for patterns.
Want to predict which accounts will close this quarter? The model already has the features, it's been tracking them continuously.
The GTM Brain becomes a simulator for organizational physics: how decisions unfold, how buyer journeys progress, how signals predict outcomes.
This is what experienced sellers have that new hires don't, a mental model of how deals actually work. The GTM Brain makes that model explicit, queryable, and continuously improving.
How does this look in practice?
The GTM Brain is the system you use every day to answer the fundamental question: "Who do I target right now, and what do I say?"
Every morning, it tells each rep: "Here are the five accounts you should focus on today, ranked by expected value. Account A has three people on the buying committee actively researching solutions. Here's who they are, what they've looked at, and what message will resonate."
Every week, it tells leadership: "Here's how the strategy is performing. Outbound to Series B fintechs is converting 40% better than Series A. The 'ROI calculator' play is underperforming, here's why. Three accounts are at risk of churning, here's what's happening and what to do about it."
Every quarter, it answers: "What would happen if we shifted focus to enterprise? If we doubled outbound volume to healthcare? If we changed the ICP to include companies with 200+ employees?"
Current GTM tools create work. They require reps to manually log activities, update stages, write notes, and maintain data hygiene. The "user experience" is actually a data entry job disguised as software.
Warmly collapses this complexity.
Part V: Why Now? The Confluence of Forces
The "Services as Software" Shift
AI will rewrite software economics by delivering outcomes rather than selling seats.
The old model: Pay $X per user per month for access to a tool. Hire people to use the tool. Hope they use it well.
The new model: Pay $X per outcome delivered. The software does the work. Humans supervise and handle exceptions.
The Infrastructure is Finally Ready
Building the GTM Brain required infrastructure that didn't exist three years ago:
LLMs capable of reasoning: GPT-4, Claude 3, and Gemini 2 can understand context, make judgments, and generate quality output.
Efficient inference: Test-time compute and model optimization have made it economically viable to run complex reasoning at scale.
Identity resolution at scale: Graph databases, entity resolution algorithms, and data infrastructure can now handle the matching problem.
The Buyer Has Changed
B2B buyers don't want to talk to sales reps anymore. They want to self-serve. They want to research independently. They want to engage on their own timeline.
But they also want personalization. They want to feel understood. They want to interact with vendors who know their business, their challenges, their context.
These demands are contradictory, unless you have a system that can deliver personalization at scale without human intervention.
The Incumbents Can't Adapt
Traditional GTM systems were built for a world where humans did the work and software stored the records. Their architectures optimize for:
Current state storage (not temporal reasoning)
Human-driven workflows (not autonomous agents)
Feature expansion (not outcome delivery)
Per-seat pricing (not value capture)
Rebuilding these systems for an AI-native world would require gutting their core architecture. They can add AI features at the edges, but they can't become AI-native without breaking everything that makes them work.
This is the classic innovator's dilemma. The incumbents are too successful to change.
The Coexistence Reality
To be clear: CRMs survive. They remain the system of record for state, the canonical customer record, the opportunity pipeline, the contact database.
What we're building is different: the system of record for events.
We're not asking companies to rip out their CRM. We're adding the layer that makes the CRM, and every other tool in the stack, actually intelligent.
Part VI: The Compounding Intelligence Moat
1. Hard-to-Copy: The Context Graph Moat
The GTM Brain's defensibility comes from how the context graph is built and what accumulates inside it: decision traces.
Every time the system decides to prioritize an account, reach out to a contact, or hold back on an action, it generates a context trace: what inputs were gathered, what features were computed, what policy was applied, what outcome resulted.
This enables the question that makes learning possible: "Given what we knew at that time, was this the right decision?"
Do this thousands of times. The weights get updated. Historical and in-production performance converge. Eventually the model achieves 90%+ accuracy. Unlike a CRM which can be copied over and ripped out, this model is proprietary to Warmly and thus can’t be ripped out unless you want to start over. At that point, why would you rip it out?
These traces form a context graph, a structured, replayable history of how context turned into action. Over time, this graph becomes:
A world model of your market: Which signals predict buying intent? Which messaging resonates with which personas? Which accounts look like your best customers?
A source of precedent: When a similar situation arises, the system can query how it was handled before. What worked? What didn't?
A simulation engine: Before taking action, the system can run counterfactuals.
Competitors can copy features. They can't copy the accumulated decision intelligence that lives inside the system.
2. Real-time identity graph (hard, expensive, and operational)
People visit a website for 8 seconds and move on.
If they can't get their questions answered, if the chatbot is slow, if no one reaches out, if the experience feels generic, they have another P1 priority to fulfill. They're gone. Speed to lead isn't a nice-to-have. It's the entire game.
Why most systems fail
Most GTM infrastructure is batch-processed. Data lands in a warehouse overnight. Reports run in the morning. By the time you know someone was on your pricing page, they've signed with a competitor.
LLMs make this worse, not better. They have natural latency: seconds to process, reason, and respond. Asking an LLM to compute context at inference time (pulling history, resolving identity, evaluating signals, checking policy) adds more seconds before reasoning even starts.
In a world where attention spans are measured in single digits, inference-time context assembly is a losing architecture.
The real-time architecture
Warmly pre-computes and stores buyer state so agents can access it instantly.
When someone lands on your site, the context is already there: who they are, what company, their engagement history, their intent signals, what play to run. The work happened before they arrived.
Real-time chat. Immediate rep routing. Instant email trigger. Phone call with full context. All of these require data and primitives to be structured ahead of time, not assembled on demand.
The data network effect
But real-time infrastructure is just the foundation. The real moat is what improves with scale.
Website de-anonymization is a probabilistic game. You're triangulating sparse signals (IP ranges, cookie data, firmographic patterns, behavioral fingerprints) to resolve an anonymous visitor to a real person at a real company.
Accuracy improves with data volume. The more visitors you see across more customer sites in more industries, the better your resolution models become. False positives drop. Confidence scores improve. Edge cases get handled.
Every new Warmly customer contributes to this flywheel:
- Their website visitors add signals to our identity graph
- Our improved accuracy helps them convert more visitors
- Better outcomes attract more customers
- The product gets better for everyone as the network grows
Competitors starting from scratch don't just lack our infrastructure. They lack our data. You can't buy your way to data volume. You earn it customer by customer, visitor by visitor, over years.
The ontology discovery effect
There's a second network effect hiding in how we structure data.
There are infinite ways to model a GTM context graph. What primitives matter? How do you represent a buying committee? Which signals predict readiness? How do you compress 100,000 website visits into queryable state?
This is the art.
Each company we onboard teaches us something new about how to structure the ontology. The primitives that matter for a Series B fintech differ from an enterprise healthcare company. A product-led growth motion requires different signals than an outbound-heavy sales team.
The more companies we serve, the better we understand how to model GTM for everyone. We've mapped the territory. Competitors starting from scratch have to rediscover these primitives one by one.
The primitive stores
The result is a set of pre-computed stores that our agents query in real-time:
Buying Committee Store: Who's involved, their roles, their engagement
Intent Store: Temporal signal patterns, page-level behavior, engagement velocity
Lookalike Store: Which accounts match your best customers
Enrichment Store: Company and contact data, refreshed and validated
Outcome Store: What happened and what we learned
These stores feed the agents that execute:
- AI Chat Agent
- Buying Committee Agent
- Scoring Agent
- Enrichment Agent
- Lookalike Agent
- Web Research Agent
- Email/LinkedIn Copy Agent
Each agent operates on pre-computed context. They don't rebuild the world at runtime. They query it instantly.
Why this compounds
Moat #1 (the Context Graph) captures what your organization learns. This moat captures what Warmly learns across every organization.
Decision traces are proprietary to each customer. But de-anonymization accuracy, entity resolution quality, and ontology design improve for everyone as the network grows.
3. The Ground-Truth Data Moat
This is the unsexy moat that nobody in AI wants to talk about.
Moat #2 is about data getting better with scale. This moat is about data staying correct over time. They're different problems.
The core tension
LLMs are probabilistic: confident when wrong, hard to debug. But the data that feeds them must be deterministic, auditable, and correct.
Send an email to the wrong person? Brand damage. Route a lead to the wrong rep? Territory conflict. Show the wrong company data in chat? Lost credibility.
AI systems are only as good as the data they reason over. Garbage in, garbage out, except now the garbage gets delivered at scale, instantly, with confidence.
Why data rots
All data degrades. People change jobs. Companies get acquired. Email addresses go stale. Third-party providers have their own quality issues.
The half-life of B2B contact data is roughly 2 years. Half your database is wrong within 24 months, even if it was perfect when you collected it.
The moat isn't having clean data. The moat is keeping data clean as reality shifts underneath you.
The validation loop
The hardest part: you often don't know if data is wrong until months later.
You resolve an anonymous visitor to "Sarah Chen at Acme Corp." Was that correct? You might not know until she fills out a form, sales gets a response (or bounce), or the deal closes and you see who was actually involved.
The feedback loop is long. You have to build systems that learn from delayed ground truth: updating confidence scores, retraining models, surfacing systematic errors.
We've built these loops. Every conversion, every bounce, every "wrong person" response feeds back into our data quality systems.
Why this is a moat
Competitors can build AI features quickly. They can't quickly build:
- Years of learning which data sources lie and when
- Production-hardened systems for managing degradation
- Validation loops that connect outcomes to data quality
- Institutional knowledge of where things break
Data quality isn't a feature you ship. It's a discipline you practice every day. The companies that skip this step build impressive demos that fall apart in production.
We've done the unglamorous work. That's the moat.
Conclusion: The Decision Layer
The Story So Far
The problem: AI made GTM execution infinite, but pipeline is harder than ever to build. We solved the wrong problem, we built infrastructure for doing more, not for knowing what to do.
The opportunity: The next trillion-dollar platforms will create a context graph that makes precedent searchable. But you need domain-specific world models.
The solution: The GTM Brain, a stateful decision system that ingests signals, resolves identities, builds a world model, computes expected values, decides what to do, executes through agents, and learns from outcomes.
The vision: Every morning, it tells each rep what to do. Every week, it tells leadership what's working. Every quarter, it simulates strategic alternatives. The operating system for revenue. And it can plug seamlessly into any agentic framework.
The Bet Warmly is Making
The debate right now is whether AI will transform enterprise software or just add features to existing categories.
Our answer: the next trillion-dollar platforms will be systems of record for decisions, not just data.
Traditional systems of record store current state. The GTM Brain stores decision intelligence: the reasoning that connects data to action, the traces that capture how choices were made, the world model that enables simulation and prediction.
CRMs don't go away. Warehouses don't go away. But neither of them can do what we do: capture the event clock, build the world model, and make AI agents actually intelligent about your business.
What We're Building
This is what Warmly is building. Not another tool in the GTM stack. Not a chatbot for your CRM. Not "AI features" bolted onto existing workflows.
The GTM Brain, a single, self-learning engine that:
Sees every signal across your entire GTM surface area
Resolves identities and builds the entity graph
Captures decision traces and accumulates precedent
Reasons over context that no other system can see
Decides what to do (including when to do nothing)
Executes through specialized agents
Learns from every outcome
Gets smarter every day
The companies that build this infrastructure will have something qualitatively different. Not agents that complete tasks, organizational intelligence that compounds. That simulates futures, not just retrieves pasts. That reasons from learned world models rather than starting from scratch every time.
The Path Forward
We're building the GTM Brain to help B2B teams automate their GTM motion at scale and achieve their potential as a business. And like Tesla learned with driving, like Palantir learned with intelligence analysis, the key is not to program the rules. It's to learn them from the accumulated experience of thousands of practitioners.
In many expert domains (for example law or medicine), the core world model is relatively stable and deeply codified. If you can gather the right evidence, the “correct” decision framework changes slowly.
Go-to-market is different: - the market shifts constantly, - buyer behavior changes by segment and quarter, - channel economics move quickly, - and small context changes can flip what the best next action should be.
That means the challenge is not only “answer correctly once.” The challenge is to continuously maintain the organization-specific world model and make good decisions as conditions move.
This harness exists to do exactly that: 1. build and maintain a living world model for each organization, 2. enforce safe, auditable decision execution, 3. learn from outcomes and human corrections, 4. compound decision quality as models and data improve.
This is the strategic moat: not just automation, but a continuously improving, organization-specific GTM decision system.
0) Comprehensive overview (all pieces together)
This is the full runtime + memory + governance map.
Comprehensive System Overview
What this means in one sentence
Signals come in, the system decides whether to act, acts safely through guardrails, measures outcomes, and learns back into a shared GTM brain.
1) End-to-end operating loop
Signal to Trusted Action
Every signal follows the same loop:
Signal intake A trigger arrives: web behavior, chat, CRM update, intent surge, or scheduled run.
Action triage The first decision is: act now, later, or not at all.
Context retrieval If action is needed, the system pulls relevant context from shared memory.
Decision boundary The system chooses a candidate next action.
negative outcomes (bounce, no response at scale) reduce trust.
Pattern learning Repeated human corrections create policy patterns (for example, “skip this domain class” or “reconsider this persona class”).
End-to-end example: blocked outreach -> human approval -> policy update
Scenario: A target account visits pricing, chat reveals urgency, agent drafts a 3-step outreach sequence.
Agent proposes execution for outreach.
Trust gate evaluates and holds execution (score below threshold).
Batch enters human review queue with full rationale.
Human edits one message, approves two contacts, rejects one contact.
Approved actions execute; rejected path is canceled.
Decision Trace records:
original decision,
trust-gate reason,
human override,
final execution outcome.
Outcomes arrive (reply + one meeting booked).
Learning writeback updates:
trust score for similar action type,
reusable examples from approved/performing messages,
policy hints from rejection reasons.
Next similar account starts with improved defaults and less review friction.
4) Inbound + TAM as one coordinated system
Sales and Marketing Journey
Inbound and TAM are separate lanes, but they run on one shared memory substrate.
Why this matters for executives
Without a shared brain, teams optimize locally and conflict globally. With a shared brain, all lanes learn from the same outcomes.
Practical journey
Marketing captures high-intent activity.
Inbound agent qualifies and captures objections.
Shared account context updates instantly.
TAM chooses next best committee actions using updated context.
Safety-gated execution runs only eligible actions.
Outcomes write back to the same account memory.
Future inbound and TAM behavior both improve from that result.
5) Canary Model Rollout
Canary Model Upgrade Example
What it is
A canary model rollout is a controlled live test lane for model or policy upgrades before full rollout.
Why it exists
A model can look better in a demo but still hurt production quality. Canary rollout prevents that.
When it is used
Any time the decision engine changes in a meaningful way:
model version change,
prompt/policy logic update,
tool-routing behavior change,
risk-threshold adjustment.
How it works in plain terms
Create candidate New model/prompt configuration is prepared.
Golden dataset baseline check Candidate must pass offline checks against known-correct labeled examples.
Split live traffic Small live slice is split between current system (control) and new system (variant).
Compare both sides Evaluate quality, safety, and business metrics side-by-side.
Gate decision
If variant is better or safely equivalent -> promote.
If variant regresses safety or business outcomes -> hold/rollback.
Golden Dataset (What It Is, in Plain Language)
Golden dataset = a hand-validated set of examples where we know the correct answer with high confidence.
For GTM, this includes: - whether the company truly matches ICP criteria, - whether a title maps to the correct buying persona, - whether a detected behavior is a real intent signal (not noise), - whether the recommended action is policy-safe for that context.
It is the baseline contract the model must satisfy before touching live traffic.
Marketing example: web scrape -> labeling -> canary
Scenario: A prospect account is scraped from website + social + CRM context. The system must decide if this should enter a high-priority outbound motion.
Golden dataset labels (known-correct examples):
Company type label Example: “B2B SaaS, 200-1000 employees, North America” = ICP Tier 1.
Persona label Example: “Director of Revenue Operations” = Approver persona for this play.
Signal label Example: “Visited pricing + compared competitor page in same session” = high-intent signal.
Action label Example: “Generate personalized outreach + suppress paid retargeting for 48h” = correct first action.
How the rollout works:
New model is scored on this golden dataset first.
If it misses critical labels (ICP/persona/signal/action), it does not proceed.
If it passes, it enters canary on a small live slice.
Live metrics then validate real-world behavior (reply rate, trust blocks, duplicates, meeting quality, spend efficiency).
Only after both baseline correctness and live safety/KPI pass does full rollout happen.
End-to-end marketing example
You launch a new “pricing-page follow-up” messaging model.
10% of eligible traffic enters the upgrade test.
Half uses current messaging (control), half uses new messaging (canary variant).
Over a fixed window, compare:
reply quality,
meeting creation,
trust-block rates,
duplicate/cooldown incidents,
spend per useful outcome.
Result:
if variant increases meetings without safety regressions, promote to broader traffic.
if variant improves replies but causes higher trust blocks, keep it in test and revise.
This lets leadership move fast on model gains without risking production quality.
6) Learning system
Learning System
What it is
Learning is the mechanism that turns outcomes into better future decisions.
The three learning levels
Turn-level Was each individual message/action good and policy-safe?
Sequence-level Was the ordering/timing/channel mix good across multiple steps?
Business-level Did this path create meetings, pipeline, and revenue efficiently?
End-to-end marketing example
Scenario: a target account visited pricing, then engaged chat, then entered nurture + TAM outreach.
Turn level The first follow-up email gets a reply but low sentiment score. System marks that pattern as partially effective.
Sequence level Analysis shows better outcomes when chat follow-up happens before paid retargeting, not after. System updates sequencing preference.
Business level Two sequence variants are compared:
Variant A: lower reply rate but higher meeting-to-pipeline conversion.
Variant B: higher reply rate but weak downstream conversion. System prioritizes Variant A for similar accounts.
Policy/trust update High-performing patterns are promoted. Poor patterns are deprioritized or blocked for similar contexts.
Next cycle Future campaigns start with improved sequence defaults automatically.
Net effect: the system compounds commercial quality over time instead of repeating mediocre playbooks.
7) Budget and token optimization (operating model)
This harness is not only an accuracy system; it is also a cost-optimization system.
What is being optimized
token spend,
tool-call spend,
channel spend,
human review time,
cost per qualified outcome,
cost per meeting/pipeline dollar.
How optimization works
Progressive disclosure for context Start with fast/cheap memory, go deeper only when needed.
Action gating Don’t execute expensive actions when trust/safety is insufficient.
Canary economics checks Promotion requires not just quality safety, but healthy cost efficiency.
Outcome-weighted budget allocation Budget shifts toward sequences/channels with stronger downstream conversion, not vanity engagement.
Visibility loop in UI Operators can see spend, decisions, and outcomes in one place and adjust thresholds/policies.
Executive view
This turns GTM automation into a measurable optimization function: maximize qualified business outcomes under safety and budget constraints.
8) Visibility and control (not a black box)
UI Control Plane and Runtime
A core design principle: agent behavior must be inspectable and controllable.
Control Center UI gives
policy and trust controls,
autonomy/approval settings,
experiment + upgrade-test status,
safety + budget dashboards,
rollout controls.
Decision Trace UI gives
what action was selected,
why it was selected,
what evidence/context was used,
what policy state applied,
what happened after execution.
9) Extensibility layer: API + MCP tool surface
Extensible GTM Harness API + MCP Layer
The harness is designed to be an extensible GTM runtime, not a closed app.
Think of it as a GTM-specialized agent platform: - broad action capability like a general agent runtime, - constrained by GTM-specific trust, policy, and execution controls.
How external systems connect
External systems (internal copilots, workflow engines, CRM apps, and other agent systems) connect through:
REST API For operational workflows, dashboards, approvals, and reporting.
MCP tool API For agent-native tool calling from chat/assistant environments.
Both routes converge into the same harness core, so behavior stays consistent and auditable.
External systems can orchestrate user-facing workflows while this harness remains the governed decision + memory backend.
New channels and actions can be added as tools without redesigning the whole system.
Every external integration inherits the same trust gates, traceability, and learning loops.
10) Practical rollout path
Phase 1: Instrumented control
Connect core signal sources.
Turn on traceability + trust gates.
Keep autonomy narrow until observability is stable.
Phase 2: Unified learning
Run inbound + TAM on the same memory substrate.
Attach outcomes to decisions consistently.
Activate turn/sequence/business learning loops.
Phase 3: Scaled autonomy
Use canary model rollout for all major model/policy changes.
Expand autonomous scope only where quality + safety + economics pass.
11) Final framing
This is not a chatbot layer. It is a governed GTM decision system.
The strategic value is:
one shared world model,
safe and auditable execution,
continuous outcome-linked improvement,
and explicit budget optimization at scale.
That is what creates durable compounding advantage for enterprise GTM operations.
Last Updated: March 2026
March 10, 2026
Drift Is Shutting Down: Best Drift Alternative for 2026 | Warmly
Time to read
Look, if you're here because you just found out Drift is shutting down, I'll skip the preamble.
This is what we're doing for Drift customers:
We'll match your remaining Drift contract price. You were paying $10K? Pay us $10K. You were paying $30K? Pay us $30K. You get our full inbound suite: AI chat, popups, visitor identification, intent signals. Everything Drift did and a bunch of things Drift never could.
We have former Drift employees on our team. They'll handle your entire migration for free. Offboarding from Drift, onboarding to Warmly, rebuilding your flows. The whole thing. You'll be live in days.
If you want to know what actually happened, and why I think this moment is bigger than just swapping chat vendors, keep reading.
TL;DR: Drift is sunsetting in 2026 after years of declining investment under Vista Equity. Clari + Salesloft named 1mind as Drift's exclusive AI successor, but 1mind is a narrower product than Drift was (no de-anonymization, no intent data, no outbound). Warmly is a full-stack Drift alternative that covers inbound chat, visitor identification, intent signals, outbound email and LinkedIn, and buying committee mapping in a single platform. We're offering free migration and contract price matching for all Drift customers.
I Watched Drift Die
I've been building in this space for four years. I remember when Drift was the most exciting company in B2B SaaS.
They didn't just build a chatbot. They invented a category. Conversational marketing. Their sales team was closing $6K deals live through the product, posting Zoom links directly in chat and getting buyers on a call in minutes. Revenue went from $6M to $47M in two years. David Cancel and Elias Torres built something genuinely special. Every B2B website had that little blue Drift icon in the corner and the playbooks to capture and convert leads were elegant.
Then Vista Equity showed up in 2021 with a $1B valuation.
From that point on, everything that made Drift great got slowly strip-mined. The SMB customers who built Drift's early growth? Abandoned. Pricing floor raised to $30K/year, labeled, hilariously, as the "Small Business" tier.
> ![IMAGE: Screenshot of Drift's pricing page showing the $2,500/month "Small Business" tier] R&D investment dried up. The product got harder to use, not easier. Features that were promised never shipped.
Then September 2025 happened. A massive OAuth token breach compromised over 700 organizations, including Cloudflare, Palo Alto Networks, and Zscaler. Drift went offline. That's what happens when you milk a product instead of investing in it.
And now, March 6, 2026: Clari + Salesloft officially sunsets Drift. Drift end of life, confirmed. They didn't just kill the product. They picked your replacement for you.
I'm not writing this to dunk on Drift. That product deserved better than what Vista did to it. And the thousands of companies who built their inbound pipeline on Drift deserved better than being told their conversational marketing platform is reaching end of life, with a replacement they didn't choose.
This is what PE does to software. They acquire a product, stop investing in it, raise prices, and try to exit at a higher multiple. They're not in it to build something great. They're in it to extract. Salesloft, Clari, Drift, all under Vista's portfolio, now partnering with 1mind and pitching it as a unified system. But these are separate products built by separate teams on separate architectures at separate times. That's not a platform. That's a roll-up with a partnership announcement on top.
The "Successor" They Picked For You: Warmly vs 1mind vs Drift
So, 1mind. The "exclusive AI successor to Drift."
I want to be fair here because Amanda Kahlow is a serious operator. She built 6sense. She knows this space. And 1mind is genuinely AI-native. These aren't scripted decision trees with a language model bolted on. Their "Superhumans" can qualify leads, run live product demos, handle objections, even join video calls as a ride-along SE. The HubSpot numbers are real: 88% buyer engagement, 78% increase in free trials, 25% more closed-won deals.
If your only need is a smarter inbound chatbot, 1mind is legit.
But Salesloft isn't telling you the full picture.
1mind doesn't know who's on your website until they type something into the chat. No visitor de-anonymization. No person-level identification. Someone lands on your pricing page, browses for 45 seconds, and leaves. 1mind never knew they existed.
1mind has no intent data. It can't tell you that three people from the same company have been researching your category across the web this week. It only sees what happens inside its own conversations.
1mind can't do outbound. No email sequences. No LinkedIn outreach. No multi-channel follow-up after someone ghosts the chat.
No buying committee mapping. No TAM nurturing. No cross-channel orchestration.
But the part that nobody is saying out loud: as a Drift replacement, 1mind is actually a narrower product than Drift was. Better at what it does, absolutely. But it does less. Drift at least had email capture, basic routing, some integrations. 1mind is singularly focused on the inbound conversation. It's a valid product. It's just not a Drift replacement. It's a Drift subset.
There's also the Frankenstein problem. The "Drift successor" pitch is that 1mind feeds signals into Salesloft Cadences and Clari forecasts. On paper that sounds like a unified system. In reality you're looking at four different products (Clari, Salesloft, 1mind, and whatever's left of Drift) built by different teams on different architectures, now stitched together through partnership integrations. That's not a unified context graph. That's an API layer on top of legacy platforms. If you've ever tried to get clean data flowing between three or four tools that weren't built to talk to each other, you know how this plays out.
And then there's a pricing problem nobody is talking about. 1mind doesn't publish pricing, but they have about 60 enterprise and mid-market customers (HubSpot, Samsara, Nutanix, ZoomInfo). These are big logos. Drift built its early growth on SMB companies paying $30K or less. The "exclusive successor" may not even be in the same pricing universe as the customers being displaced.
Warmly vs. the Clari + Salesloft + 1mind Stack
Warmly is an AI-powered revenue orchestration platform that combines visitor de-anonymization, intent data, AI chat, outbound automation, and buying committee mapping into a single system. Founded in 2022, Warmly serves SMB and mid-market B2B companies as a comprehensive Drift alternative and conversational marketing replacement.
The "combined stack" column is important. Even if you buy Salesloft for outbound AND 1mind for inbound AND Clari for forecasting, you still don't get de-anonymization, intent data, buying committee mapping, or a unified data layer. You get three separate products passing data through integrations. Warmly does it all in one system because it was built that way from the ground up. In a direct comparison: Warmly offers visitor de-anonymization, web-wide intent data, and outbound automation that 1mind does not provide. 1mind offers AI video call ride-along capabilities that Warmly does not yet have. Drift offered rule-based chat and basic email capture but lacked AI-native conversations, intent data, and de-anonymization. For teams looking for a Drift chatbot replacement that goes beyond chat, Warmly covers the most ground in a single platform.
The Chatbot Paradigm Already Died. Most People Just Haven't Noticed.
Drift was built for a world where buyers went to your website to get answers. That world is disappearing.
In 2026, your buyers are doing their research on ChatGPT, Perplexity, Claude, and Gemini before they ever visit your site. They're asking AI to compare vendors, summarize pricing, pull up case studies. The smart ones are hooking up MCP servers and having agents do the evaluation for them. By the time someone actually lands on your website, they've already done most of their homework.
So what do they want when they get there? Not a chatbot. We've heard this from our own customers over and over: people don't want to talk to a bot. They don't even want to talk to a human yet. They want to browse the pricing page, look at product diagrams, read a case study, and book a meeting on their own terms. They'll talk to a person when they're ready. Not when a chat widget pops up and asks "How can I help you today?"
Go look at 1mind's website. It's just a chatbot. The entire experience is a conversation interface. That works for a demo. It doesn't work for how real B2B buyers actually buy.
And this is where the inbound-only model completely falls apart. Most visitors browse, maybe hit 2-3 pages, and leave without ever opening the chat. With 1mind, those visitors are ghosts. You don't know who they were, what they looked at, or what they cared about.
With Warmly, we de-anonymize them the moment they land. We know who they are, what company they're from, which pages they visited, how long they spent on each one. That's real buying intent. Even if they never type a single message into a chat box, we've captured signal that you can act on. Retarget them with an ad. Add them to a sequence. Flag them for your sales team. Route their info into your CRM so the next time they show up, your rep has full context.
If the only visitors you're capturing are the ones who voluntarily chat, you're missing 95%+ of the intent on your own website. That's the fundamental problem with the chatbot paradigm. It was built for a world where people wanted to chat. That world doesn't exist anymore.
The Real Problem: Context, Not Execution
When you hire a great salesperson, they don't just sit at their desk waiting for leads to walk in. Over months, they build up knowledge. Which personas respond to which messaging. Which objections come up at certain deal stages. Which signals mean a deal is real versus a tire-kicker looking for a free POC. That accumulated context is the actual value of your team. Not the ability to send emails or have conversations. The ability to know what to do and when.
That's the gap in every AI GTM tool right now. They can all execute. They can send a million emails. They can chat around the clock. Execution is effectively infinite in 2026. But decision quality (knowing WHO to engage, WHAT to say, WHICH channel to use, and WHEN to do it) is almost zero. Because the agents have no context. No memory. No understanding of your specific market.
If LLMs are next-word predictors, then what we need in GTM are next-best-action predictors. Agents that look at the full sum of everything they know about an account, every past interaction, every signal, every outcome from similar deals, and predict the right thing to do next. That's what humans do. We're all just running on accumulated context and making our best guess. The difference is whether your agent has six months of organizational knowledge or six seconds of a chat transcript.
We started building Warmly four years ago because I saw this problem coming. Chatbots were always going to hit a ceiling because they could only see one channel (your website) and they had no memory between sessions. And the thing that Salesloft, Clari, and 1mind still don't have is the data layer underneath all of it. The intent signals. The identity resolution. The enrichment. The conversion data across every channel. That's not execution software. That's the foundation you need before AI agents can make good decisions. We've been building that foundation for four years. They haven't started.
So we built something different. A system that:
Knows who's on your site before they say a word. Our de-anonymization runs across 20+ data providers. When someone hits your pricing page, we already know their name, company, role, and engagement history. 1mind waits for them to type hello.
Tracks buying intent across the web. Not just your website. Across the entire internet. We pull signals from 6sense, Bombora, Clearbit, and our own proprietary data. We can tell you when a buying committee is forming at a target account before they've ever visited your site.
Does outbound too. Email. LinkedIn. Ads. After someone chats on your site, the system doesn't just hope they come back. It follows up on the right channel, with the right message, at the right time. And it can reach accounts proactively. The 97% that haven't visited yet.
Remembers everything and learns from outcomes. Every deal won. Every deal lost. Every email that got a reply and every one that didn't. We've been collecting and training on intent data and conversion signals since 2022. That's four-plus years of compounding intelligence across every channel, not just conversations.
Gets the full buyer journey. In B2B, the gap between first touch and closed-won can be 3, 6, 12 months. You need a system tracking everything from the first anonymous page view to the signed contract so it can learn what actually works. Chat-only data is a sliver of that picture.
We call this the Context Graph, a living memory of your market that makes every agent smarter over time. It's the difference between a day-one SDR who doesn't know your business and a two-year veteran who has instincts about every account.
Is Warmly perfect? No. 1mind's video call ride-along capability is something we don't have yet. If that's your number one use case, genuinely, go with 1mind. But if what you need is a system that understands your entire market, not just the conversations that happen to occur in a chat widget, I don't think it's close.
The Receipts
Cendyn was a Drift customer. Their words, not mine: it had become "overly complex, expensive, and difficult to manage." Custom playbooks across dozens of pages. A maintenance nightmare.
They switched to Warmly in days. Immediately got something Drift never offered: real-time visibility into exactly who was visiting their site. Passed security review without issues, which matters given what happened with Drift's breach.
Ryan Shapiro, their Director of Global Business Development:
"What we're being able to utilize right now with Warmly for the cost that we paid for Drift is already making up for in the difference."
He's not alone. Beehiiv identified 2,500 ICP leads in three weeks. Caddis saw a 500% conversion increase in their first week. Pump.co closed $20K in revenue before their first week was up.
Why We're Different (And Why It Matters Who You Build On)
I know how this looks. Competitor writes blog post when rival shuts down. Tale as old as SaaS.
But I want to be direct about something. When you're choosing who to build on top of, you're choosing their incentive structure. PE-backed companies are optimizing for the next exit. They raise prices, cut R&D, and consolidate products to juice multiples. That's what happened to Drift. That's what's happening across this entire Clari + Salesloft portfolio.
We're VC-backed and building toward a billion-dollar company. The only way we get there is by building something so good that customers stay for years and tell everyone they know. I'm not being noble about this. It's just math. Our incentives are aligned with yours in a way that PE incentives never will be. We have to innovate. We have to be at the frontier. Taking three steps back and ten steps forward for our customers is the only path that works for us.
I genuinely think this is a defining moment. Not because Drift is dying (products die all the time) but because the chatbot paradigm is dying. And every Drift customer now has a choice: replace their chatbot with another chatbot, or upgrade to something that was never possible before.
The migration offer stands:
We match your Drift contract price
Free migration handled by our team (including former Drift employees)
Full inbound suite plus outbound, intent data, de-anonymization, and buying committee mapping
Clari + Salesloft announced the Drift sunset on March 6, 2026. No hard end date has been confirmed. Drift had previously gone offline in September 2025 following an OAuth security breach that compromised over 700 organizations including Cloudflare, Palo Alto Networks, and Zscaler.
What is 1mind?
1mind is an AI sales engagement platform founded by Amanda Kahlow (who previously built 6sense). It deploys AI "Superhumans" on websites, in products, and on video calls to qualify leads and deliver live demos. Clari + Salesloft named 1mind as Drift's exclusive AI successor in March 2026. 1mind focuses on inbound qualification and AI-powered demos. It does not offer visitor de-anonymization, outbound automation, or intent data infrastructure.
What is the best Drift alternative in 2026?
For AI-powered inbound demos and video call engagement, 1mind is strong. For a comprehensive alternative covering inbound chat, visitor de-anonymization, intent signals, outbound email and LinkedIn, buying committee mapping, and cross-channel orchestration, Warmly provides the broadest capability set starting at $15K/year, with a migration offer that matches your existing Drift contract pricing.
How do I migrate from Drift to Warmly?
Warmly provides free migration support for Drift customers, including hands-on assistance from former Drift employees on the Warmly team. Typical setup takes days. Warmly will match your existing Drift contract pricing. Visit warmly.ai/drift-migration or email drift-migration@warmly.ai.
Is Warmly cheaper than Drift?
Drift's minimum was $30,000/year with enterprise tiers reaching six figures. Warmly's inbound plan starts at $15,000/year. Through the Drift migration offer, Warmly will match whatever you were paying Drift. If your Drift contract was $10K, your Warmly contract will be $10K for equivalent or greater capability.
What does Warmly do that Drift didn't?
Warmly provides visitor de-anonymization (identifying anonymous website visitors using 20+ data providers), web-wide intent data from sources like 6sense, Bombora, and Clearbit, outbound automation across email and LinkedIn, buying committee mapping, and a unified Context Graph that connects all signals into a single data layer. Drift offered rule-based chat, email capture, and meeting booking but lacked AI-native conversations, identity resolution, and cross-channel orchestration.
What happened to Drift? Why is Drift being discontinued?
Vista Equity Partners acquired Drift in 2021 at a $1B valuation. After the acquisition, Drift's R&D investment declined, pricing increased (minimum $30K/year), and SMB customers were deprioritized. In September 2025, a major OAuth security breach compromised over 700 organizations. In March 2026, Clari + Salesloft (both Vista portfolio companies) officially announced Drift's sunset, naming 1mind as the exclusive AI successor. The Drift sunset follows a common PE pattern of acquiring software, reducing investment, and consolidating products.
How does Warmly compare to 1mind for Drift replacement?
Warmly and 1mind take different approaches. 1mind excels at AI-powered inbound conversations, including live product demos and video call ride-along capabilities. Warmly covers a broader surface: visitor de-anonymization, intent data, AI chat, outbound email and LinkedIn, buying committee mapping, and cross-channel orchestration in a single platform. 1mind sees visitors only when they engage in chat. Warmly identifies visitors the moment they land on your site. For teams that need more than inbound chat replacement, Warmly provides a more comprehensive Drift alternative.
Last Updated: March 2026
March 9, 2026
6sense Review: Is It Worth It in 2026? [In-Depth]
Time to read
Are you trying to figure out if 6sense is the right sales intelligence software for your marketing and sales team?
In this honest 6sense review, I’ll go over the platform's features, usability, data quality, integrations, and even customer support to help you make an informed decision.
TL;DR
Range of features: 8/10. 6sense offers a good range of features for sales and marketing teams, most notably their dynamic audience-building and visitor identification software. Despite that, the platform does not provide contact-level data of website visitors and does not have live engagement features.
User interface and usability: 5/10. The platform has a steep learning curve that has been confirmed by multiple users of the software. Building automations with the tool’s orchestrator is not easy in the beginning, either.
Data quality: 7/10. There are negative reviews regarding intent data inaccuracies and duplication issues, but there are still some customers who are satisfied with the tool’s keyword research intent data and other signals.
Integrations: 7/10. 6sense has a good range of native integrations, partnerships, and a whole bunch of other integrations that could already be in your sales stack – but some users have reported difficulties setting up the integrations.
Customer support: 8/10. The platform offers best-in-class account management and customer success, but some of the customer support reps have shown poor product knowledge, according to G2 reviews.
Pricing model: 5/10. 6sense does not have transparent pricing, and 3rd party data shows that their average cost is higher than alternatives on the market – making it too expensive for SMEs. The tool has a free plan but with only 50 credits to spend monthly.
Average rating for 6sense: 6.6. No pun intended.
6sense Overview
6sense is an AI-powered ABM platform that combines B2B data with intent signals to automate your ABM workflows.
The platform can identify the accounts that are most likely to buy so your sales reps can react in time.
The tool has gained recognition for its advertising capabilities, which help you build retargeting campaigns based on its intent data.
I think of 6sense as a platform that is ideal for medium-to-large enterprises looking to Identify surging accounts on their website so their sales team can reach out to them and nurture relationships.
💡 Since this review aims to analyze 6sense in good detail, I’ll be giving my unbiased ratings on the platform’s features, user interface, data quality, integrations, and customer support.
Let’s dive deeper into the software’s sales features: 👇
6sense’s Core Features
1. Get access to in-depth B2B intent data
6sense collects intent signals from multiple sources (the platform claims it analyzes 500B+ intent signals monthly), such as website visits, keyword searches, and engagement patterns of prospects.
This data helps sales teams better understand what their target accounts are researching, allowing them to pinpoint the ones most likely to convert.
Sales teams can then create relevant and highly personalized campaigns accordingly to capture the demand.
2. Lead prioritization dashboards
6sense’s lead prioritization dashboards provide your sales reps with a personalized, 360-degree view of all their deals, accounts, leads, etc.
As a result, your sales reps can identify opportunities in real-time and gain a deep understanding of which accounts to focus on and how.
Moreover, all the essential information is constantly updated, meaning that you’ll always have the most relevant and accurate data.
3. Dynamic audience building
6sense provides your team with 80+ segmentation filters that let you quickly define your ICP and identify accounts that best fit it.
It combines proprietary data with your CRM data and the intent signals it picked up to create detailed account lists that enable you to prioritize high-value accounts.
The software dynamically adjusts account lists by moving accounts to different audience segments based on relevant real-time factors such as changes in their buying stage, annual revenue, keywords they are researching, etc.
4. Marketing orchestration
6sense’s platform lets you create dynamic, always-on campaigns that react to buyer behavior and automatically update audiences as they move through the buying journey.
Your team can automate data enrichment, contact acquisition, audience building, and regular syncs between your sales platforms to save time and keep lead data up-to-date.
Rating: 8/10.
6sense offers its customers a comprehensive range of features for sales teams, such as its visitor identification software, lead prioritization, and dynamic audience building for advertising.
However, the solution can only identify companies visiting your website and not actual stakeholders (i.e., who exactly is visiting your website).
The platform also does not have real-time engagement features, such as some 6sense alternatives on the market (e.g., an AI chat) and has no integration with for automated outreach (only with LinkedIn Ads).
For example, customers of the platform are struggling to identify who is engaged with their company when dealing with larger accounts.
‘’We also do not have contact level reporting of who is actually engaged with our company, which can be challenging when engaging larger accounts.’’ - G2 Review.
6sense’s User Interface: Is It Easy To Use?
6sense’s platform does have a lot of functionality when it comes to automations, reporting, and marketing automations – and all of that comes with a slight learning curve for even seasoned sales professionals.
The software’s numerous features are packed into a rather clunky interface, which even satisfied customers of the platform are criticizing in their G2 reviews.
‘’What I dislike about 6sense Revenue AI for Marketing is that it can be complex to navigate initially, requiring a learning curve to fully leverage all its features.’’ - G2 Review.
When it comes to the platform’s automations with their orchestrations, customers of the platform also note that it has been more difficult for them than they originally suspected.
‘’Orchestrations and workflows with 6sense data were more difficult to implement than expected.’’ - G2 Review.
Rating: 5/10.
As reviewers have pointed out, you’d need to spend some time learning how the platform works so you can fully utilize its marketing and sales features.
In fact, when I opened their G2 profile, ‘’learning curve’’ and ‘’difficulty’’ were part of the 4 most common complaints of customers.
6sense’s Data Quality
6sense gives you access to buyer intent and engagement data with bonus predictive analytics into which stage of the buying journey your prospects are.
But just how accurate is this data?
I was able to find 6sense customers on G2 who have reported issues with the platform’s predictive analytics and intent data, which is described as ‘’directional, and not a crystal ball.’’
‘’From an intent perspective, it is not the end all be all or the crystal ball. It is directional. Also, I do find that the predictive model is generous. For example, if an account hits one of our campaign landing pages, it is suddenly in the 'purchase stage', and we often find that rarely means that an account is ready to send us a purchase order!’’ - G2 Review.
I also found some customers who complained about the tool’s data duplication issues, which has resulted in them having trouble with data reliability.
‘’Data can be a bit cumbersome, and we've had problems with data reliability and creation of duplicates.’’ - G2 Review.
On the positive side, there are customers of 6sense who have massively improved their sales pipeline by tapping into intent data, such as keywords and categories.
‘’6sense has been great in helping us identify segments based on key intent features such as keywords and categories. As a product marketer, it is really important we can identify the ICP and make sure the revenue org can clearly engage with their target accounts based on the topics and keywords they are searching for.’’ - G2 Review.
Rating: 7/10.
Despite 6sense’s negative reviews regarding their intent data inaccuracies and duplication issues, there are still some users who are satisfied with the tool’s keyword research intent data and other signals.
My problem with 6sense, similar to platforms like ZoomInfo, is that the tool’s visitor identification software reveals only companies and not individuals.
Good luck prospecting accounts like ‘’Microsoft’’ or ‘’Meta’’ landing on your website.
6sense’s Integrations
The platform integrates with various sales, marketing, and productivity platforms to centralize and maximize your existing sales tech stack so you can create more engaging campaigns with deeper insights.
The platform offers native integration with their partners: Outreach, Gong, Salesforce, and Salesloft, on top of their integrations with tools like Bombora for 3rd party intent signals.
You can also expect to sync your data with other productivity platforms, including LeanData for AI-powered insights and Reachdesk for perfectly-timed gifting to prospects.
However, customers of the platform have noted that integrating the tool with their existing systems has been challenging for them.
‘’Additionally, integrating it with existing systems can sometimes be challenging.’’ - G2 Review.
Rating: 7/10.
Despite 6sense’s native integrations, partnerships, and a whole bunch of other integrations that could already be in your sales stack – I gave the platform’s integrations a 7 instead of an 8 due to the difficulty of integrating some of the tools with 6sense.
6sense’s Customer Support
Even though 6sense does not disclose what customer support you can expect on its pricing page, I was able to get a good idea of the tool’s customer support from G2 reviews.
Users of 6sense are generally positive about the platform’s level of customer support, noting that they were assigned a customer success manager who has been helping them and responding quickly to their requests.
‘’Even though the tool can be quite daunting because of its depth, having a CSM that can help support us is so helpful. Definitely helpful to just send a quick note and get a response right away.’’ - G2 Review.
This positive perception has been confirmed by both mid-market and enterprise customers of the platform, claiming that they’ve had a good experience with customer support.
‘’We have had great customer support and feel like 6sense is always willing to dive in and collaborate to come up with new ideas or solutions we need.’’ - G2 Review.
Despite that, another Enterprise customer of 6sense mentions that the tool’s regular customer support can be a hit or miss with less product knowledge than their customer success team.
‘’Also, while 6Sense's customer success team is top-notch, their customer support is a bit more hit or miss. Some support reps are real product experts and zero in on solutions to issues quickly. Some… not so much.’’ - G2 Review.
Rating: 8/10.
Even though most users are satisfied with the level of customer support that they were provided on 6sense – it seems like some customer support reps do not have the necessary product knowledge to handle more complex issues.
6sense’s Pricing Model: Does It Provide A Good Value For Money?
6sense offers a free plan that provides:
50 credits/month.
Buyer Discovery.
Contact & Company Data.
Alerts.
List Management.
Chrome Extension.
If you need more, you can upgrade to one of three plans:
Team: Includes everything in Free plus:
Technographics
Psychographics
Web, CRM, and SEP Apps
Add to CRM/SEP
Dashboards
Growth: Everything in Team plus:
6sense Intent (Keywords)
3rd Party Intent
Corporate Hierarchy
Prioritization Dashboards
Enterprise: Everything in Growth plus:
Predictive AI Model
AI Recommended Actions
CRM & MAP Activity
6sense doesn’t disclose prices on its website, so you’ll have to contact its sales for more details.
However, Vendr also provides some helpful insights into 6sense’s pricing policy:
Customers are required to enter into a 2-year agreement with 6sense, a commitment that yields high retention.
In addition, it's important to highlight that potential costs may arise due to usage/overages, upgrades, or downgrades.
Finally, according to Vendr’s data, the average 6sense contract value is$56,762/year.
Rating: 5/10.
The tool does not disclose its pricing, but we were able to find that 6sense is on the higher end of the pricing range when compared to other alternatives on the market.
I do like the fact that the platform has a free plan, but you’ll run out of the 50 credit allowance in a matter of days if not hours.
How Does 6sense Compare To Alternatives On The Market?
💡 Check out our in-depth comparison of 6sense vs. ZoomInfo, where we cover the 2 sales intelligence giants in more detail.
What Are Customers Saying About 6sense?
Throughout this 6sense review, I’ve been showing you some of the users’ opinions on the platform – but let’s dive a bit deeper.
TL;DR: 6sense boasts a highly responsive support team, good segmenting accuracy, and a configurable interface. Despite data, customers of the platform report issues with 6sense’s data exports, limited persona targeting and the cost of the software.
Moreover, its persona targeting is highly limited compared to alternatives on the market.
What users love about 6sense:
Good segmenting accuracy with an interface that can be customized.
Ability to view account engagement and gain insights into their current buying stage.
Highly responsive customer support team and excellent account management.
There were some restrictions in ways you could filter or parse out data for particular programs, which made it, at times, difficult to pull and analyze data. Though it was nice having an optimization team to help, it would've likely been more convenient if there were tools in the platform to help with optimization ideas so you didn't need to join a call when looking for optimization help. - G2 Review.
The export of segments to platforms such as Google Ads and LinkedIn Ads is limited, and getting lots of them for exporting more segments is really expensive. - G2 Review.
Common complaints about 6sense:
Persona targeting is described as limiting with no role or title-based matching.
Limited export of segments to platforms like Google Ads and LinkedIn Ads.
The platform has been described as expensive and not affordable for smaller businesses.
Considering the high degree of configurability for account and intent attributes, persona targeting is shockingly limiting, with no true role or title-based matching. Targeting enterprise companies based solely on persona makes for a lot of waste. Other tools are doing exact title matching and have been for a few years. We've had to purchase additional tools to help with this layer, which means we can't launch ads through 6sense either. - G2 Review.
Verdict: Is 6sense Really Worth It?
So far, I've rated 6sense:
Range of features: 8/10.
User interface and usability: 5/10.
Data quality: 7/10.
Integrations: 7/10.
Customer support: 8/10.
Pricing model: 5/10.
Which gives me an average rating of 6.6/10 for 6sense.
To summarize:
6sense is the ideal choice if you:
✅ Are looking for good B2B intent data coverage, especially in the US region.
✅ Need a reliable marketing automation solution.
✅ Want a platform that can integrate with your advertising channels to create highly targeted campaigns.
6sense isn’t the best option if you:
❌ Are looking for a more budget option, as 6sense is more expensive than some of the other alternatives on the market.
❌ Need more website visitor identification functionality, such as contact-level data.
❌ Need more first-party intent data to identify and reach out to warm prospects.
Looking For A 6sense Alternative?
Despite 6sense’s range of features, good range of integrations and a large library of B2B intent data, some customers are still finding faults with the product’s data quality, pricing model, and ease of use.
Enter Warmly (that’s us) – a signal-based revenue orchestration platform that provides a wide range of features designed to:
Capture warm leads that visited your website (first-party data).
Identify the hottest prospects from those visitors.
Automatically engage and nurture them, helping your sales reps to successfully convert them.
Let’s get a closer look at the functionality that makes Warmly an attractive alternative to 6sense for sales teams: 👇
Our platform can identify both companies and the individual profiles that are browsing your website.
As a result, your reps can focus their efforts on the right stakeholders from the get-go.
With Warmly, you’ll get another ace up your sleeve.
Once it identifies website visitors, the platform proceeds to enrich each visitor with:
B2B data (email address, company name, phone number, industry, size, technographic, etc.).
First-party intent data (details of your visitors’ website sessions, such as visited pages, recurring visits, etc.).
Third-party intent data (insights into visitors’ entire digital buyer’s journey, including visits to competitors’ websites, job change intent, etc.).
This combination of first- and third-party intent data enables you to identify the leads that are most likely to buy right now as Warmly picks up the buying signals they’ve left throughout the web—on your website and beyond.
Your sales reps will be able to recognize who your hottest leads are right now and design tailored strategies for reaching out to them.
Why Is Intent Data Important?
Trying to sell a water bottle to someone who just walked out of their office isn’t the same as selling a water bottle to a runner who just finished a workout.
Understanding intent signals and recognizing them in time lets you:
Qualify and score leads with greater precision and easily identify the hottest leads.
Reach out when their interest level is at its peak.
Create a hyper-personalized approach for each lead based on the contextual insights you have at hand, like which pages they visited, what they searched for, etc.
This helped Behavioral Signals shorten its sales cycle by 50-90% and source nearly $7M in its pipeline.
Platforms that omit intent data from their offering slow your lead generation efforts from the get-go, leaving you without one of the most powerful weapons in sales reps’ arsenals.
2. Build Targeted Lead lists With Coldly
We at Warmly have recently included a static B2B database, Coldly, to help users build highly targeted lead lists more efficiently.
Coldly holds data on 200M+ accounts and contacts worldwide, in addition to having more than 25 built-in B2B data filters and the option for creating customized filters to fit specific business needs and industries.
Moreover, since the data is refreshed daily, you can have peace of mind that all your essential B2B data is accurate, relevant, and up-to-date.
You can do anything from building contact lists to automatically enriching your CRM contacts without spending hours on this.
3. Streamline Sales Engagement Processes
Warmly’s automation features are by far one of the things most customers love about the platform.
Warmly uses intent signals it picks up to build complex workflows on top of them, enabling you to reach out to all the right leads at the best possible time.
It provides several automation features, including:
Orchestrator, which lets you automate email outreach. The Orchestrator is highly configurable, meaning you can set up all the important parameters as you see fit, including:
The action that triggers the workflow (lead matching your ICP lands on your website or a website visitor comes back to your pricing page).
Filters that define which companies and individuals should be included in the workflow (these include everything from company size and industry to job position and seniority, etc.).
The course that the workflow should take (send a contextual email, a personalized DM, or a connection request).
This capability solves for time-to-lead, while SDRs can take over ones that respond and provide a personalized experience.
The AI-powered prospector prevents quality leads from falling through the cracks simply because you didn’t have a sales rep on hand.
💡Note: You will need to be on one of Warmly’s paid plans to gain access to AI Prospector. For ARC, the ROI was 200% over 6 months.
AI Chat, which is an AI-driven chatbot that can be trained to:
Qualify leads.
Answer their questions.
Book meetings.
Offer relevant collaterals.
Engage leads.
Lead routing, which lets you set up real-time Slack notifications that will alert the adequate sales rep whenever a lead matching certain criteria lands on your website or takes a high-intent action.
The combination of these automation features ensures that:
Every high-value lead will be engaged.
Your sales reps will have more time to focus on things that matter than most rather than act as surveillance guards on your website 24/7.
4. Live Video Chat
Engaging your leads while they’re still hot can make the difference between a successfully closed deal and an opportunity that is forever lost.
With Warmly, you won’t have to worry about that, as its Live Video Chat feature lets you engage leads while they’re still visiting your website.
In the “Warm Calls” section of Warmly’s dashboard, your sales team can see who’s visiting your website right now and monitor their session in real-time.
Once you detect a high-intent lead based on Warmly’s insights or a visitor matching your ICP, you can engage them in a video chat immediately.
Alternatively, you can rely on automated lead routing to notify reps whenever a high-value lead ends on your website.
This allows you to monitor their website interactions and hop on a call when they assess the time is right.
Micro: Starts at $333/month when billed annually and adds unlimited seats, 5,000 monthly visitors revealed, first-party intent signals, alerts, and access to Warmly’s extensive database of B2B contact data.
Starter: Starts at $12,000/year, everything in Micro, plus 10,000 monthly visitors, third-party signals, AI Chat, and CRM syncs.
Business: Starts at $19,000/year for up to 10,000 visitors or $28,000/year for up to 75,000 visitors, everything in Starter, plus second-party signals, sales orchestration, and lead routing.
Enterprise: Starts at $30,000/year, lets you identify a custom number of visitors, includes everything in Business, plus custom signals and warm calling.
How Does Warmly Compare to 6sense?
6sense is an excellent option for your business if you are looking for predictive analytics, keyword identification and site heatmaps.
Warmly, on the other hand, shines in finding and engaging prospects who are looking to buy and are likely to respond to your outreach.
Our platform might not have predictive analytics into where customers are in their buying journey or intent data from keywords, but we offer:
Website de-anonymization signals at the contact level.
Automated email retargeting.
Automated DMs retargeting.
Live session replays.
Chat video messaging so you can contact your prospects as they are browsing through your pages (e.g., pricing page).
Find Out Who Your Warmest Leads Are With Warmly
If you’re looking for a purely ABM platform with solid intent data, 6sense might be the way to go for your organization.
The platform’s capabilities of detecting buyer intent on your website are significantly limited compared to Warmly's, as they do not reveal contact-level information.
This limitation also affects 6sense’s marketing automations, which cannot be fine-tuned to engage only the warmest prospects—which your company is mostly interested in.
With Warmly, all that changes.
Our revenue orchestration platform helps you to identify website visitors (to the contact level), detect the hottest leads among them, and engage them via automated outreach sequences perfectly tailored to each lead.
You’ll be able to identify that Joe from Microsoft has been browsing your pricing page and then reach out to him – or why not right on your pricing page with a chatbot or video chat?
If you want to be able to do that, then you can try Warmly for free to start building a steady, warm pipeline in minutes.
Or, book a demo with our team for a personalized tour of all of Warmly’s capabilities.
Best GTM Tools: Optimize your GTM strategy with some of the best GTM software on the market today.
Best Sales Intelligence Tools: Look beyond popular options like Apollo and Zoominfo to find the optimal solution for your use case.
Frequently Asked Questions
Is 6Sense worth it?
6Sense is worth it for teams that need its core features. Review the detailed analysis above to see if it matches your use case. Consider alternatives if you need features 6Sense doesn't offer.
What are the pros and cons of 6Sense?
Key pros include the features highlighted in this review. Common cons include limitations around certain use cases. See the detailed breakdown above for a complete analysis of strengths and weaknesses.
What is the best 6Sense alternative?
The best alternative depends on what you need. Warmly is ideal for inbound lead conversion with website visitor identification. Other alternatives may better suit outbound prospecting or data enrichment needs.
How does 6Sense compare to competitors?
See the comparison section above for detailed feature-by-feature analysis. Key differentiators include pricing, data quality, integrations, and specialized capabilities for different use cases.
Who should use 6Sense?
6Sense works best for the use cases described in this review. Teams with different needs—like identifying anonymous website visitors or engaging leads in real-time—may want to explore alternatives like Warmly.
February 19, 2025
Revenue AI in 2026: The Definitive Market Landscape (From Workflow Hell to Agent Intelligence)
Time to read
Revenue AI is the category of artificial intelligence tools that help B2B sales and marketing teams find, prioritize, and engage buyers. It includes everything from data enrichment and intent signals to AI SDRs, conversation intelligence, and autonomous orchestration platforms.
Here's the thing nobody in this space wants to admit: the $8.8 billion revenue AI market has a dirty secret. Most of these tools are just workflow automation with an AI label slapped on top. They connect Step A to Step B, maybe generate an email draft, and call it "intelligent." That's not intelligence. That's a fancy spreadsheet.
I've spent the last 18 months building autonomous GTM agents at Warmly. We run 9 AI agents in production every day. I've seen what actually works, what's marketing fluff, and where the real frontier is. This guide is the honest assessment I wish someone had written for me when we started.
This is part of a 4-post series on Autonomous GTM Infrastructure:
Best for enterprise ABM with complex sales orgs:6sense - predictive analytics leader, ~$55K-$200K/year, 5x consecutive Gartner Magic Quadrant Leader. You'll need a dedicated ops team and a 3-6 month implementation runway.
Best for autonomous full-funnel GTM:Warmly - person-level visitor identification, AI agents that act (not just inform), context graph with learning loops. Starts at $10K/year with a free tier. Operational in hours, not months.
Best for outbound-first sales teams on a budget:Apollo - 210M+ contacts, all-in-one sequencing and enrichment, free to $119/user/month. The best value if outbound is your primary motion.
Best for data enrichment power users:Clay --150+ data providers, waterfall enrichment, $134-$720/month. Incredibly powerful if you have a RevOps engineer to maintain the workflows.
Best for conversation intelligence and coaching:Gong - $1,360-$1,600/user/year + platform fee, 3.5B+ sales interactions analyzed. The gold standard for understanding what happens on calls.
Best for revenue forecasting + sales engagement:Clari + Salesloft - merged Dec 2025 into a $450M ARR entity, ~$140-$180/user/month. Building the first "Predictive Revenue System" spanning the full revenue cycle.
The Revenue AI Market Map (2026)
Let's talk numbers first.
The AI-in-sales market hit $8.8 billion in 2025 and is projected to reach $63.5 billion by 2032 at a 32.6% CAGR (PS Market Research). AI venture funding hit $211 billion in 2025, nearly doubling 2024's $114 billion (Crunchbase).
But here's the reality check. McKinsey reports that while 88% of organizations now use AI in at least one function, only 39% see any impact on EBIT. Most under 5% (McKinsey 2025 State of AI). BCG is even more blunt: only 5% of companies create substantial AI value at scale. 60% generate no material value at all (BCG 2025).
Translation: lots of money, lots of adoption, very little actual ROI for most teams.
The fragmentation problem makes this worse. The average B2B company uses 87 different software tools, but only 23% of them directly impact revenue (Netguru). Sales reps spend 65% of their time on non-selling activities. Employees waste 12 hours per week chasing data trapped in silos.
This is the landscape you're buying into. Hundreds of tools. Billions in funding. And most of it doesn't work.
Two structural shifts are happening right now that will reshape this landscape:
1. Gartner created a new category. In December 2025, Gartner published its first-ever Magic Quadrant for Revenue Action Orchestration, formally merging what used to be separate categories: sales engagement, conversation intelligence, and revenue intelligence (Gartner). The market is consolidating from 15+ point solutions to 5-7 integrated platforms.
2. The Clari + Salesloft merger happened. Two of the biggest names merged into a $450M ARR entity in December 2025 (Salesloft). Forrester called it "a bold, high-stakes bid for market dominance." This isn't the last mega-merger we'll see.
The winning stacks in 2026 are 5-7 integrated platforms, not 15-20 disconnected point solutions. Organizations with well-integrated tech stacks are 42% more likely to boost sales productivity (Highspot).
The Three Eras of Revenue AI
Understanding where the market came from explains where it's going. And honestly, most teams are still buying tools from an era that's already ending.
Era 1: Contact Databases (2015-2020)
The promise: More data = more pipeline.
ZoomInfo and Clearbit gave sales teams access to contact data at scale. Platforms competed on database size (ZoomInfo: 210M+ professionals) and accuracy rates (~95% email deliverability). The value proposition was simple: find decision-maker emails faster than manual research.
The limitation: Static data decays at 25-30% annually. Having a phone number doesn't tell you when to call. Sales teams drowned in data without context for prioritization.
Era 2: Intent and Workflow Orchestration (2020-2024)
The promise: Right accounts at the right time, connected through smart workflows.
6sense, Demandbase, and Bombora introduced intent signals and predictive analytics. The focus shifted from "who exists" to "who's buying." Meanwhile, Clay emerged as the "Zapier for data enrichment," and Outreach/Salesloft made multi-step sequences the default playbook.
The limitation: Company-level intent only. 6sense can tell you Acme Corp is researching your category, but not which of their 500 employees is doing the research. Clay requires 4-6 weeks to master and a RevOps engineer to maintain. And at $55K-$200K/year for 6sense, the technology stayed inaccessible to mid-market teams.
Era 3: Agent Intelligence (2024-Present)
The promise: AI that does the work, not just informs it.
This is where things get interesting. Foundation Capital's thesis captures it perfectly: enterprise value is migrating from "systems of record" (Salesforce, Workday) to "systems of agents." The new competitive advantage isn't the data itself. It's the context graph: a living record of decisions, relationships, and outcomes that agents can reason over.
What makes Era 3 different:
World models, not databases. Instead of static contact records, Era 3 platforms maintain a temporal representation of your market: companies, people, activities, and outcomes. The system knows what was true when past decisions were made.
Long-horizon agents. These aren't chatbots. They reason in loops: evaluate results, adjust strategies, continue working toward objectives without being prompted each step. They maintain persistent memory across weeks and months.
Decision traces, not logs. Every decision (reach out, hold off, escalate) gets captured with full context. This transforms exceptions into training data.
Work-based economics. Pricing shifts from seats to outcomes. As BCG notes, companies using seat-based pricing for AI products see 40% lower gross margins than those using outcome-based models.
The key insight: Most teams are still buying Era 2 tools for Era 3 problems. If you're evaluating revenue AI in 2026, ask yourself: "Does this platform have a world model that learns from outcomes, or just a database that tells me who to call?"
Why Workflow Tools Are Hitting a Ceiling
I'll be direct about our thesis. In a world of agent abundance, workflow tools will become obsolete. Not tomorrow. But the direction is clear.
Here's why.
The judgment problem. Clay, Zapier, and Make are brilliant at connecting A to B. If this trigger fires, run these steps. That's powerful for deterministic workflows. But GTM isn't deterministic. Should you email or LinkedIn message this VP? Both might be valid. The answer depends on her LinkedIn engagement score, your email bounce history with this domain, what similar personas responded to, the time of day, and whether your SDR already had a conversation with someone else at the company yesterday. That's judgment, not a workflow.
The coordination problem. Multi-channel GTM means email needs LinkedIn needs ads needs chat. One failure breaks the chain. When Agent A sends an email and Agent B sends a nearly identical LinkedIn message two hours later, that's not an edge case. That's the default outcome when tools don't share context. We've seen it happen in our own system. It's why we built the agent harness.
The memory problem. Clay doesn't know that John reports to Sarah. Zapier doesn't know the email it sent last week contributed to a closed deal this month. Make doesn't learn from outcomes. These tools are pipes, not brains. They have no persistent memory, no entity relationships, no learning flywheel.
The cost problem. Clay's hidden costs are real. Platform fees ($134-$720/month) plus credits plus the tools Clay connects to plus the RevOps engineer maintaining the workflows. We've seen total cost of ownership reach $40K-$80K/year for serious Clay deployments. At that point, you're paying workflow-tool prices for workflow-tool limitations.
This doesn't mean Clay is bad. It's genuinely powerful for what it does. But it's Era 2 technology. And if you believe GTM is heading toward agents that make judgment calls with full context, you need a different architecture.
What Replaces Them: The Agent Harness
Think about it this way. You wouldn't deploy a fleet of microservices without Kubernetes. You wouldn't run a data pipeline without Airflow. But somehow, we're deploying fleets of AI agents with nothing but prompts and prayers.
That's where the agent harness comes in.
An agent harness is the infrastructure layer between your AI agents and the real world. It does three things: gives agents shared context, ensures they don't collide through coordination, and enforces constraints that prevent them from going rogue.
This parallels what Anthropic built with Claude Code. Their design principles directly map to what we're building for GTM:
Progressive disclosure. Claude Code doesn't dump the entire codebase into context. It searches for what it needs. Our GTM agents do the same. They query the context graph for relevant information, not everything that exists. Raw data is pre-digested into computed columns that reduce token consumption by 10-100x while improving decision quality.
Trust earned, not configured. Claude Code starts with limited permissions and earns broader access. Our agents start at Level 1 (human approves every action). Over time, as they demonstrate good judgment, they progress to Level 2 (override window, acts if no human intervenes) and eventually Level 3 (fully autonomous). You don't set a "freedom dial" on day one. Trust builds through demonstrated results.
Capabilities-driven tool evolution. When a better model comes out, Claude Code gets smarter. Same principle. Swap in a newer LLM, and the emails get better, the research gets deeper, the decisions get more nuanced. The harness stays the same. The trust gates stay the same. Better model, same guardrails, better work.
How Warmly's Architecture Actually Works
Here's a concrete example. A VP of Sales visits your pricing page at 2pm on a Tuesday.
Without an agent harness: Your intent tool fires an alert. It goes into a Slack channel with 200 other alerts. An SDR sees it 4 hours later, spends 15 minutes researching the account, sends a generic email. Maybe.
With the agent harness: The context graph instantly resolves the visitor's identity. It knows she's Sarah Chen, VP of Sales at Acme Corp. The graph shows: ICP Tier 1, closed-lost deal from 6 months ago (reason: timing), her company just hired a new CRO (job change signal), and she has high LinkedIn engagement. The agent evaluates the full context and decides: LinkedIn message first, referencing the timing issue from the previous evaluation. It checks trust gates (within volume limits, quality threshold met, Level 2 override window active). The SDR gets a Slack alert with the full context and the drafted message. If no override in 30 minutes, it sends. Meanwhile, Sarah is added to a LinkedIn Ads audience for awareness reinforcement. Two months later, when this becomes a deal, every touch is attributed back to the decisions that drove it.
That's the difference between "AI that sends emails" and "AI that makes judgment calls with full context."
The Learning Flywheel
This is where the architecture compounds. Decisions lead to outcomes. Outcomes get graded. Grading improves the model. Better model, better decisions. Based on our production experience, approximately 100 graded decisions are needed to reach 90% agreement with human judgment. That means the system can cold-start in about 2-4 weeks.
Four feedback loops compound simultaneously:
Trust builds. Agents that prove themselves get more autonomy. Agents that make mistakes get pulled back.
Rules emerge. Human corrections become automatic policies. "Never contact healthcare on Fridays" started as a one-time fix. Now it's a rule.
Emails teach emails. Every AI-generated email is tracked against engagement. The system learns what resonates with YOUR buyers, not generic benchmarks.
Signals sharpen. The outcome loop measures which signals actually predict meetings. Intent scoring gets more accurate every month.
Every week you run the harness, it gets slightly smarter. That's infrastructure that appreciates rather than depreciates.
The 12 Platforms Defining Revenue AI in 2026
Let's get specific. Here's every major player, what they actually cost, what they're genuinely good at, and where they fall short.
Comparison Table
Platform
Category
Starting Price
Typical Cost
Person-Level ID?
Learning Loop?
Best For
6sense
ABM/Intent
Free (limited)
$55K-$200K/yr
No (company only)
No
Enterprise ABM
ZoomInfo
Data/Intelligence
$15K/yr
$30K-$100K+/yr
Limited (WebSight)
No
Data quality
Gong
Conversation Intel
~$25K/yr
$50K-$150K+/yr
N/A
No
Call coaching
Clari+Salesloft
Rev Forecast + Engagement
~$15K/yr
$50K-$200K+/yr
No
No
Rev forecasting
People.ai
Activity Capture
Custom
Custom
No
No
CRM hygiene
Apollo
All-in-One GTM
Free
$10K-$50K/yr
No
No
Outbound on budget
Clay
Data Orchestration
$134/mo
$8K-$22K+/yr
No
No
Enrichment workflows
Outreach
Sales Engagement
~$100/user/mo
$65K-$150K+/yr
No
No
Enterprise sequences
11x.ai
AI SDR
~$50K/yr
$50K-$60K/yr
No
Limited
AI outbound
Artisan
AI SDR
~$2.4K/mo
$29K-$86K/yr
No
Limited
Budget AI SDR
Demandbase
ABM/Marketing
Custom
$50K-$150K+/yr
No
No
Marketing-led ABM
Warmly
Autonomous Orchestration
Free
$10K-$22K/yr
Yes
Yes
Full-funnel GTM
Now let me break each one down honestly.
6sense: The Enterprise ABM Standard
6sense is genuinely excellent for what it does. Their predictive analytics estimate buying stage 3-6 months before traditional signals appear. They just launched RevvyAI, their most significant update ever, turning the platform into an "AI-powered GTM command center." Five consecutive Gartner Magic Quadrant wins is no joke.
Where it's limited: Company-level identification only. The median buyer pays ~$55K/year, but enterprise contracts run $100K-$200K+ (Vendr). Implementation takes 3-6 months. And the AI recommendations still function as a "black box." 40% of our customers previously used 6sense and switched because they needed person-level identification and couldn't justify the cost for what they were getting.
ZoomInfo maintains the largest B2B database: 210M+ contacts and 100M+ company profiles. Email accuracy (~95%) is the industry benchmark. They've rebranded hard, changing their ticker from ZI to GTM and launching Copilot Workspace with AI agents for account research and outreach.
Where it's limited: $15K-$45K/year starting, with typical enterprise deals at $30K-$100K+. 2024 revenue was $309M but declining (-2% YoY) before a slight recovery to $319M in 2025. Renewal price increases of 10-20% are commonly reported. One of our customers told us: "We had zero to one closed deals from ZoomInfo intent data over 3 years." Another saved $92K/year switching to Warmly ($44K vs. $136K for ZoomInfo).
Gong just launched Mission Andromeda, their most ambitious release, adding 18 AI agents, AI Call Reviewer, and an Account Console. They've analyzed 3.5B+ sales interactions. ARR passed $300M in early 2025, and they raised a $250M Series F at $7.25B valuation.
Where it's limited: Pricing is the #1 complaint. $1,360-$1,600/user/year plus a platform fee ($5K-$50K) plus implementation ($15K-$65K). For a 50-person sales team, you're looking at $80K-$130K in year one. Gong tells you what happened on calls. It doesn't proactively take the next action.
Clari + Salesloft: The Revenue AI Powerhouse
The December 2025 merger created the biggest private revenue AI company: $450M combined ARR, 5,000+ customers, and $10 trillion of revenue under management. Forrester called it "a bold, high-stakes bid for market dominance." They're building the "first Predictive Revenue System."
Where it's limited: Post-merger integration is still underway. Product roadmap clarity is limited. Pricing is enterprise-focused (~$140-$180/user/month for Salesloft, negotiated heavily at scale). If you want proactive autonomous agents, not just forecasting and sequencing, this isn't the right fit yet.
People.ai: The Activity Capture Specialist
People.ai auto-captures email, meetings, and contacts and writes them back to CRM. They just launched MCP integration, connecting AI agents directly to their data layer. $200M raised, $1.1B valuation.
Where it's limited: $63M ARR after 9 years with 100 employees raises questions about growth trajectory. Custom pricing only, no self-serve. Former employees note product struggles. It's an analytics layer, not an action layer.
Apollo: The Value King
Apollo is the fastest-growing sales platform through PLG: $150M ARR (up from $96M in 2023), 500K+ companies on the platform, $1.6B valuation. Free tier is genuinely useful. 210M+ contacts with international coverage that beats most US-focused tools.
Where it's limited: Real costs often reach 2-3x advertised prices ($150-$400/user/month with credit overages). Email accuracy (~85%) is lower than ZoomInfo. No real-time visitor identification. If inbound traffic is a lead source, you'll need to pair Apollo with something else.
Clay grew from $1M to $100M ARR in two years. That's insane. Their waterfall enrichment across 150+ data providers triples match rates (40% to 80%+). Claygent can browse websites and extract custom data points. $3.1B valuation. 10,000+ customers including OpenAI and Anthropic.
Where it's limited: Learning curve is steep (4-6 weeks to productivity). Credit burn is the #1 complaint on G2. No entity relationships, no decision traces, no outcome attribution, no trust gating. It's infrastructure for enrichment, not a system that learns. Every time a data provider changes their API, someone has to debug the workflow.
$301M revenue in 2024, 6,000 customers, the enterprise standard for multi-channel sequences. Kaia provides AI-powered conversation intelligence.
Where it's limited: No public pricing, but expect $100-$150/user/month. CEO transition in 2024. Buggy issues are a consistent G2 complaint. It's a sequence engine, not an intelligent agent. It does what you tell it, exactly how you tell it, without judgment.
Demandbase: The Marketing ABM Platform
Demandbase excels when marketing owns the ABM motion. Their ABX (Account-Based Experience) platform runs coordinated multi-channel campaigns: display ads, content personalization, and sales handoffs from one system. The "air cover" use case is strong. Running display ads to target accounts while sales pursues them creates familiarity that shortens sales cycles.
Where it's limited: Less sales-focused than 6sense. No free tier or mid-market option. Implementation is complex, similar to 6sense timelines. Pricing is enterprise-only ($50K-$150K+/year). If sales is driving your GTM motion and you need rep-level tools, 6sense or Warmly are better fits.
11x.ai: The VC Darling of AI SDRs
11x's "Alice" is the most well-funded AI SDR: $76M raised, a16z and Benchmark backing, $25M ARR (growing 150% quarterly). Claims Alice can replace 10 human SDRs. Enterprise customers include Siemens and ZoomInfo.
Where it's limited: $50K-$60K/year with rigid contracts. Difficulty canceling subscriptions is a common complaint. Narrow channel coverage (mostly email, some LinkedIn). About 30 days of contact history vs. 12-18 months in a context graph. No buying committee modeling. And the fundamental question: does replacing SDRs entirely actually work? The evidence is mixed.
Artisan: The Controversial Challenger
Artisan's "Stop Hiring Humans" campaign got attention (while hiring humans). $46M raised, 250 paying customers, $5M ARR. Ava handles lead sourcing from 300M+ contacts, personalized emails, and LinkedIn automation.
Where it's limited: The reviews are rough. Users report "AI slop" emails, 1,000-1,400+ emails with zero replies, and prospects that lack budget or authority even when meetings are booked. One user found only 3-7 C-level contacts matching their criteria from 3M+ records. Cancellation friction is a recurring complaint. At $2.4K-$7.2K/month, the ROI math gets hard when the output quality is inconsistent.
Warmly: The Context Graph Platform
This is us, so I'll be straightforward about what works and what doesn't.
What works: Person-level visitor identification (up to 40% match rate, vs. company-only for 6sense and ZoomInfo). Our context graph connects 400M+ person profiles across 50+ data sources. 9 AI agents run in production daily, coordinated through trust gates. Setup takes hours, not months. Pricing starts at $10K/year with a free tier.
What the data shows:
AI chat meetings booked growing 52% in 2 months (21 in November -> 32 in January)
AI Inbound Agent converting at 8-10%
Customer company identification rates hitting 91% (vs. 70% average)
AI-generated outreach achieving 45-57% open rates
40% of our customers are replacing 6sense or ZoomInfo
And our most interesting first-party data point: 40% of our inbound now comes through AI tools (ChatGPT, Claude, Perplexity). Buyers are finding us by asking AI, not by searching Google. One of our $32K deals came from someone who literally asked ChatGPT for a recommendation.
Where we're limited: Match rates are strongest in US/UK markets. You need website traffic for the identification to generate value. The learning flywheel takes 2-4 weeks to cold-start. We don't have a built-in dialer. And honestly, AI-generated outbound still converts at lower rates than we'd like. Open rates are great. Conversion? Still a frontier.
I could write a post that says "AI is transforming everything!" and call it a day. But that wouldn't be useful. Here's what's actually hard about revenue AI in 2026.
1. The Cold Start Problem
AI agents need data to learn, but you need agents to generate data. The first month won't be dramatically better than simpler tools. Our learning flywheel needs ~100 graded decisions to reach 90% agreement with human judgment. That's 2-4 weeks of active use. Most teams quit before the flywheel starts spinning.
2. AI Outbound Still Has a Conversion Problem
Here's something we don't love admitting: AI-generated emails get 45-57% open rates but conversion to meetings is still low. The emails are good enough to get opened. They're not yet consistently good enough to get replied to. This is the frontier for everyone in the space, not just us.
3. Attribution Remains Unsolved
We track 148 outcomes across our context graph. But attributing a closed deal back to the specific AI action that started it? That's still more art than science when the sales cycle is 60+ days.
4. The "Went Dark" Problem
42% of lost deals across our customer base come from prospects going dark after discovery calls. No amount of AI fixes a buyer who stops responding. The best we can do is detect the going-dark pattern earlier and try a different channel.
5. Model Costs Are Real
Running Claude Sonnet at production scale for thousands of personalized emails and research queries is not free. The cost per AI-generated email has come down dramatically, but for high-volume outbound, it adds up.
When Revenue AI Is NOT the Answer
Don't buy revenue AI if:
You're pre-product-market-fit. Fix your product first.
You have zero website traffic. Visitor identification needs visitors.
Your sales cycle is under 7 days and purely transactional. Simple automation works fine.
You don't have anyone who will review agent decisions in the first month. Unsupervised AI SDRs will send garbage.
Your team of 5 people doesn't need another $10K+ tool. Spreadsheets and LinkedIn InMail might be enough.
How to Choose: Decision Framework
By Company Stage
Seed / Pre-Revenue: Use Apollo's free tier + LinkedIn Sales Navigator. Don't spend money on tools until you have repeatable revenue.
Series A ($1M-$5M ARR):Warmly free tier or Startup plan for visitor identification + AI chat. Apollo for outbound. You don't need 6sense.
Series B ($5M-$20M ARR): This is where Warmly's full stack shines. Person-level identification, AI agents, context graph. You have enough traffic and enough deals to feed the learning flywheel. Add Gong if your deal sizes justify conversation intelligence.
Series C+ / Enterprise ($20M+ ARR): 6sense makes sense if you have the budget, the ops team, and long enterprise sales cycles. Clari+Salesloft for forecasting and engagement. Warmly for visitor identification and autonomous orchestration alongside your enterprise stack.
By GTM Motion
Pure outbound: Apollo + 11x or Artisan. But honestly, our data shows the hybrid approach (inbound signals triggering targeted outbound) outperforms cold outbound by 3x.
Inbound-first:Warmly is the strongest choice. Person-level visitor ID + AI chat + autonomous follow-up. No one else combines all three in real-time.
Account-based enterprise: 6sense for intent signals + Gong for conversation intelligence + Outreach for sequences. Or consolidate to Clari+Salesloft for the engagement+forecasting combo.
By Budget
Under $500/month: Apollo free tier + Warmly free tier + LinkedIn Sales Navigator.
$500-$2K/month:Warmly Startup ($700/mo) + Apollo Basic ($49/user/mo).
$2K-$5K/month:Warmly Business + dedicated enrichment (Clay or built-in).
$5K-$15K/month: Full Warmly agent stack + Gong or Clari+Salesloft.
$15K+/month: Enterprise stack. 6sense + Gong + Outreach + Warmly for visitor ID. Or consolidate.
What Happens Next (2026-2028)
Consolidation Accelerates
3-4 winners will emerge in each subcategory. The rest get acquired or die. Clari+Salesloft is the first mega-merger. Expect more. Salesforce has 25 PMs and 500 engineers building what sounds like a context graph inside Agentforce. When Salesforce enters a category, independent vendors either get acquired or get squeezed.
Execution Gets Commoditized. Judgment Becomes the Moat.
Sending an email is easy. Writing a decent subject line is easy. Even personalizing the first line based on LinkedIn data is easy. What's hard is deciding WHETHER to email this person, WHEN to do it, WHICH channel to use, and WHAT to say based on everything you know about the account, the buying committee, the competitive situation, and what worked for similar accounts.
That's judgment. And judgment requires context. And context requires a graph. This is why we're building the context graph. The companies that build the best brain win, even if the arms and legs (execution) become commoditized.
Learning Flywheels as Competitive Moats
Here's the thing about a learning flywheel: it compounds. A company that started building their context graph 6 months ago has 6 months of decision traces, outcome attributions, and policy improvements that a new entrant can't replicate. First-party data compounds. This isn't SaaS where you switch tools in a weekend. The longer you run the harness, the smarter it gets.
Multi-Modal Agents Go Live
Voice + email + LinkedIn + ads from a single decision. AI agents that call, email, and message through different channels based on a unified context. We're already building toward this. 2027 is when it goes mainstream.
AI-Driven Discovery Changes Everything
40% of our inbound now comes through AI tools. Buyers are asking ChatGPT and Claude "what's the best tool for X?" instead of searching Google. This means your SEO strategy needs to account for AEO (Answer Engine Optimization). If your brand doesn't show up when someone asks an AI, you're invisible to a growing share of buyers.
FAQs
What are the revenue AI and sales AI tools market trends for Warmly and 6sense in 2025-2026?
The revenue AI market grew to $8.8 billion in 2025, projected to reach $63.5 billion by 2032 at 32.6% CAGR. For 6sense specifically, they continue to dominate enterprise ABM with five consecutive Gartner Magic Quadrant wins and just launched RevvyAI. But they face pressure from platforms offering person-level identification at lower price points. Median 6sense contracts are ~$55K/year (Vendr).
Warmly is building Era 3 architecture: a context graph with autonomous GTM agents, person-level visitor identification (up to 40% match rate), and learning loops that improve from outcomes. Starting at $10K/year, it's capturing mid-market share from teams that can't justify or don't need 6sense's enterprise pricing. 40% of Warmly customers are replacing 6sense or ZoomInfo.
Market-wide: Gartner created the Revenue Action Orchestration category (Dec 2025). Clari and Salesloft merged ($450M ARR). AI VC funding hit $211B. But 40% of agentic AI projects will be canceled by 2027 according to Gartner. The gap between adoption and ROI is the defining tension of 2026.
What are the larger industry trends for revenue AI and sales AI tools?
Four structural shifts define the market:
From intent scores to context graphs. 6sense built its moat on predictive intent scoring. But the market is shifting toward context graphs that capture decision traces across time. Instead of a score, you get a temporal record of every interaction, decision, and outcome that agents can reason over.
From company-level to person-level. 6sense identifies companies. Warmly identifies individuals. Knowing "Acme Corp is researching your category" is less actionable than knowing "Sarah Chen, VP Sales at Acme, visited your pricing page 12 times this week." The industry is moving toward person-level as the standard.
From dashboards to autonomous agents. BCG predicts AI agents will fundamentally transform B2B sales by 2027. 54% of organizations are already deploying AI agents across the sales cycle (Futurum). The shift from "here's what to do" to "I did it" is the defining trend.
From seat-based to work-based pricing. Seat-based pricing dropped from 21% to 15% of companies in 12 months. The economics favor platforms that price on outcomes, not headcount.
How do I evaluate Warmly AI for identifying anonymous website visitors?
Evaluate across five dimensions:
1. Identification depth.Warmly identifies both companies AND individuals (up to 40% person-level match rate). 6sense, ZoomInfo WebSight, and most competitors only identify companies or have limited person-level coverage.
2. Match rate quality. Our customer Pipekit achieved 91% company identification (vs. 70% average) and 14.7% person-level contact identification. Request a proof-of-concept on your actual traffic to measure real rates. Results vary based on traffic quality and geography.
3. Signal context. Beyond identification, Warmly captures the full activity timeline: pages viewed, time spent, return visits, buying committee behavior. This context feeds the AI agents for autonomous outreach.
4. Action capability. Warmly's agents can automatically engage identified visitors via chat, email, or LinkedIn. Most visitor ID tools identify but require manual follow-up.
5. Speed to action. Accounts engaged within 5 minutes of high-intent page visits convert at significantly higher rates than those engaged after 24+ hours. Real-time matters.
What is the best revenue AI platform for mid-market companies?
For mid-market companies (50-500 employees), Warmly offers the strongest combination of Era 3 capabilities and accessible pricing. At ~$55K-$200K/year, 6sense consumes most of a mid-market sales tech budget. Implementation takes 3-6 months with dedicated resources most mid-market teams don't have.
Warmly starts at $10K/year with a free tier including 500 visitors/month. Person-level identification works out of the box (no implementation project). AI agents handle work that would otherwise require SDR headcount. The context graph and learning loop mean the system improves over time.
Apollo is a strong alternative for pure outbound at $49/user/month, but lacks visitor identification and learning loops. Clay is powerful for technical teams building custom enrichment, but the 4-6 week learning curve and ongoing maintenance costs are prohibitive for most mid-market teams.
Are AI agents for sales worth the investment in 2026?
Yes, with the right architecture. AI sales agents deliver measurable ROI when built on context graphs with learning loops. 83% of sales teams using AI report revenue growth vs. 66% without (SPOTIO). Early adopters of AI SDR workflows report up to 40% faster deal cycles and 50% higher lead-to-customer conversion.
But here's the honest answer: most AI agent implementations fail. RAND Corporation reports over 80% of AI projects fail overall. Gartner predicts 40%+ of agentic AI projects will be canceled by 2027. The difference between success and failure isn't the model. It's the infrastructure. Context graphs, trust gates, decision traces, and learning flywheels separate the 5% that work from the 95% that don't.
What's the difference between a context graph and a CRM?
A CRM (Salesforce, HubSpot) is a system of record. It stores current state: this contact works at this company with this deal stage. A context graph is a system of agents. It stores decision traces across time, entity relationships, and reasoning.
Example: Your CRM says "Sarah Chen is VP Sales at Acme Corp. Deal stage: Evaluation." Your context graph says "Sarah visited pricing 12x over 3 weeks. Her CFO visited the ROI page yesterday. Similar accounts at this stage closed at 3.2x rate. Our last outreach failed because we led with features, not outcomes. The AI SDR is holding off on email and will trigger LinkedIn when Sarah returns to site."
How do AI SDRs compare to human SDRs in 2026?
AI SDRs (11x at ~$50K/year, Artisan at $29K-$86K/year) are cheaper than human SDRs ($80K+ salary + benefits + tools + management). But the results are mixed.
What AI SDRs do well: High-volume prospecting, personalized first-touch at scale, 24/7 operation, consistent execution of proven playbooks.
What they struggle with: Genuine relationship building, handling complex objections, creative multi-threading across buying committees, and email quality that feels truly human. Artisan reviews specifically mention "AI slop" and zero-reply campaigns.
Our take: The best results come from AI augmenting humans, not replacing them. Use AI agents for the first touch, research, and qualification. Use humans for relationship building, complex negotiations, and enterprise deals where personal rapport matters.
What is long-horizon reasoning in AI agents?
Long-horizon reasoning means AI agents that pursue goals across extended timeframes, days, weeks, or months, rather than single-turn interactions. These agents maintain persistent memory, evaluate results, adjust strategies, and keep working toward objectives without being prompted each step.
In GTM context: a long-horizon agent can nurture an account from first website visit through closed deal, adapting its approach based on what works. It might start with a LinkedIn connection, move to email when the prospect engages, escalate to a sales rep when buying signals spike, and learn from the outcome to improve future sequences.
Most "AI" in sales tools today is short-horizon. Score this lead. Write this email. Long-horizon agents maintain the full context across the entire buyer journey. That requires a context graph, not just a database.
How much does revenue AI actually cost?
Real pricing across categories:
Category
Platform
Real Annual Cost
Enterprise ABM
6sense
$55K-$200K+
Data/Intelligence
ZoomInfo
$15K-$100K+
Conversation Intel
Gong
$25K-$150K+
Rev Forecast + Engagement
Clari+Salesloft
$15K-$200K+
All-in-One GTM
Apollo
Free-$50K
Data Orchestration
Clay
$1.6K-$22K+
Enterprise Engagement
Outreach
$65K-$150K+
AI SDR
11x
$50K-$60K
AI SDR
Artisan
$29K-$86K
Autonomous Orchestration
Warmly
Free-$22K+
Remember: published prices are usually the floor. Add credits, overages, implementation, and additional seats. Real total cost is often 2-3x the starting price.
What role does agentic AI play in improving sales efficiency?
Agentic AI in sales automates the full loop: identify prospects, research accounts, personalize outreach, send messages, follow up, qualify, and book meetings. Unlike rule-based automation (if X then Y), agentic systems make judgment calls: should I email or message on LinkedIn? Is this the right time? What should I say given what I know about this account?
The efficiency gains are real. Sales teams using AI report +30% productivity, and companies with autonomous AI workflows see up to 40% faster deal cycles (Markets and Markets). But the key is the infrastructure. Agents without a context graph optimize locally while destroying globally. Agents with trust gates and learning loops get better every week.
Which AI tools analyze buyer intent and behavior most accurately?
The most accurate buyer intent analysis layers multiple signal types. No single source gives you the full picture.
For real-time, first-party intent:Warmly offers the highest accuracy by combining website behavior (pages viewed, time spent, return visits), person-level identification, CRM context, and third-party signals from Bombora. The context graph architecture means intent is analyzed with full historical context, not just "this account is hot."
For predictive, third-party intent: 6sense excels at estimating buying stage 3-6 months before explicit signals appear. Best for enterprise accounts with long sales cycles. Limitation: company-level only.
For software purchase intent: G2 Intent shows when target accounts are researching your category or competitors on G2. Narrow but powerful for SaaS companies.
For best accuracy: Layer first-party signals (your website) with third-party signals (Bombora, G2) and person-level identification. Warmly does this by default; most other platforms require manual stitching across tools.
Which platforms will survive the next 3 years?
Prediction time. The platforms most likely to survive are those with:
Proprietary data moats (ZoomInfo's database, Gong's 3.5B interactions)
Network effects (Apollo's PLG flywheel with 500K+ companies)
Learning flywheels that compound over time (context graphs with decision traces)
Pricing models that scale with value, not headcount
The platforms most at risk are those competing purely on features without defensible data advantages. In 3 years, I expect: 6sense and Gong survive as enterprise standards. Apollo survives through PLG dominance. 1-2 of the AI SDR companies (11x, Artisan) get acquired or fail. Clari+Salesloft either becomes a category leader or gets acquired by Salesforce. And context graph platforms like Warmly either prove the thesis or pivot.
Want to see this in action?Book a demo to see Warmly's context graph, person-level identification, and AI agents working together. Or start free with 500 visitors/month and see the data for yourself.
Last updated: March 2026
March 10, 2026
AI Marketing Agents: Use Cases and Top Tools for 2026
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AI marketing agents are quickly becoming the secret weapon behind some of the smartest campaigns out there.
When it comes to marketing agents, we're not just talking about tools that help.
We’re talking about AI that acts - analyzing data, building strategies, generating content, and making decisions in real time.
If that sounds futuristic, well… it kind of is. But the wild part? It’s already happening.
Teams everywhere - from scrappy start-ups to enterprise giants - are starting to rely on AI agents to handle tasks that used to eat up hours.
The kind of work that used to take a full team, such as automated campaign creation, real-time audience segmentation, and instant performance insights, is now being handled by intelligent, always-on digital assistants.
In this article, I’ll walk you through what AI marketing agents actually do, the most powerful ways they’re being used, and which tools are leading the charge.
Let’s begin!
What Are AI Marketing Agents?
AI marketing agents are intelligent, task-oriented digital assistants powered by AI, designed to perform specific marketing functions autonomously or semi-autonomously.
Unlike traditional marketing tools that require constant manual input, these agents can take action, make decisions, and even adapt their behavior based on real-time data and feedback.
Think of them as mini marketers built with code.
They can write email sequences, A/B test landing pages, launch and optimize ad campaigns, track user behavior, and personalize customer experiences across channels.
Some are designed for one specific job (like optimizing subject lines), while others operate as full-blown strategists, capable of managing entire workflows with minimal oversight.
What sets AI marketing agents apart from traditional marketing tools is their ability to learn and improve over time.
They’re built on machine learning, large language models, and other smart tech, so they’re not just following a script. They’re constantly evolving based on what works and what doesn’t.
Today, they’ve already become more than just an isolated experiment. They’re mainstream. Marketing teams everywhere are treating them as an extension of their workforce.
How Are AI Marketing Agents Revolutionizing Marketing in 2026?
In 2026, AI marketing agents aren’t just helping marketers work faster. They’re actually changing the very nature of marketing.
We’re seeing a shift from reactive marketing to proactive, always-on engagement.
AI agents can detect patterns, predict outcomes, and respond in real time - something no human team could do at scale.
They enable hyper-personalization without burning out your content team. They reduce the guesswork in campaign planning. And they’re making it possible for smaller teams to compete with the big players.
From generating data-driven insights in seconds to running personalized ad campaigns across multiple platforms simultaneously, these agents are giving marketers superpowers.
They’re freeing up human brains for strategy, creativity, and connection - the stuff that really moves the needle.
What Are the Different Types of AI Marketing Agents?
AI marketing agents come in different shapes and specialties. Here are a few of the most common types:
Content generation agents - These tools write blogs, ad copy, emails, social posts, and more. Some can even match brand voice and tone on the fly.
Email marketing agents - Think AI that manages your list, crafts personalized emails, optimizes subject lines, and analyzes open and click-through rates—all without constant oversight.
Ad optimization agents - These handle everything from bidding strategy to A/B testing creatives, using real-time performance data to boost ROI.
SEO agents - Tools that research keywords, optimize content, track rankings, and suggest on-page improvements automatically.
Analytics & insights agents - These turn raw data into meaningful insights, surfacing what’s working, what’s not, and what to do next.
Chat & customer support agents - AI-powered chatbots that go beyond scripted responses, delivering helpful, natural-sounding interactions across touchpoints.
Some platforms even offer multi-agent systems - basically a team of AI agents working together across tasks, communicating and sharing data to make smarter decisions as a unit.
Now that we have that covered, let’s look at the most common use cases for AI marketing agents today.
Top 9 Use Cases of AI Marketing Agents
AI marketing agents are being used across nearly every area of modern marketing, but some use cases really stand out in terms of impact, efficiency, and scalability.
Let’s break down some of the most powerful applications today - the ones teams of all sizes are actively benefiting from.
1. AI Agents for Conversational Marketing & Customer Support
Customer support used to mean live agents and long wait times.
Now, AI marketing agents are powering conversational experiences that feel instant, human, and helpful, whether it's through chatbots, email responses, or voice interactions.
These agents can answer product questions, recommend solutions, upsell based on user history, and even troubleshoot basic issues 24/7.
They’re not replacing your support team but enhancing it, handling the repetitive stuff so your human agents can focus on high-impact conversations.
These chatbots are even more handy when it comes to marketing.
Namely, for marketers, this is a goldmine.
Every interaction is an opportunity to engage, delight, or convert.
With AI agents integrated into your website, CRM, and messaging tools, you can turn support into a proactive channel that drives loyalty and sales.
This chatbot agent is powered by an advanced NLP AI model that can be trained to perfectly fit your brand voice, business objectives, and its designated purpose.
You can train it on your relevant data to make sure it will maintain a consistent tone of voice while tackling a wide range of tasks, such as:
Engaging high-intent leads (e.g., leads that visit your pricing page and stay there more than 10s).
Qualifying leads by asking qualificatory questions (e.g., “What brings you here?”).
Offering relevant collaterals (e.g., a whitepaper on the impact of AI tools on marketing to leads who have visited the feature page of an AI-powered tool).
Answering their queries regarding pricing, features, or other relevant matters.
Booking meetings.
Looping in human SDRs when necessary.
Because it is powered by sophisticated AI, Warmly’s AI Chat can handle a much broader spectrum of actions than regular chatbots while ensuring that each lead interaction is contextual, relevant, and personalized.
The result?
Higher engagement, more booked meetings, and more closed deals.
2. AI Agents for Customer Journey Orchestration
This is where things start to feel like magic.
AI agents can now orchestrate entire customer journeys - from the first touchpoint to post-sale engagement - automatically adapting to individual behavior at every step.
It's like having a hyper-attentive and intelligent conductor directing each customer’s unique path through your funnel.
For example, if someone interacts with your website or engages with your content, the agent might follow up with a retargeted email or DM.
Moreover, the agent personalizes each touchpoint based on every lead’s unique data and preferences, ensuring that the messaging will resonate with them.
These AI agents pick up the high-intent leads Warmly detects on your website and add them to hyper-personalized outreach campaigns via SMS, or email.
They leverage the intent and B2B data Warmly has on each lead to personalize every message, going far beyond the usual first names and company names.
Instead, AI SDRs use more nuanced information, such as leads’ interactions with your website, research of competitors’ websites, etc., to craft compelling and engaging messaging.
Rather than rigid funnels, you get fluid, intelligent experiences that guide leads forward based on real-time behavior.
It’s a huge win for both marketers and customers - less friction, more value, higher open and response rates, and more qualified meetings.
3. AI Agents for Content Creation
One of the most popular and practical uses of AI marketing agents is content creation.
These agents can research, draft, and optimize content across formats, including blog posts, newsletters, social captions, product descriptions, and more.
They save marketers a massive amount of time while helping brands maintain a consistent voice and publishing rhythm.
But they’re not just spitting out generic text.
Today’s content agents understand tone, structure, and audience intent.
You can ask one to write a persuasive product intro for Gen Z buyers, and it will tailor the message accordingly.
Some can even perform SEO research, integrating relevant keywords and optimizing content for search rankings without requiring input from multiple tools.
What makes this use case especially game-changing is how it shifts content from a bottleneck to a growth lever.
Small teams no longer need to outsource or delay publishing schedules.
AI agents can produce first drafts in literal minutes, freeing up human writers to refine, strategize, and focus on higher-level storytelling.
4. AI Agents for Campaign Personalization
Honestly speaking, real personalization at scale used to be a dream.
Now, with AI marketing agents, it’s the default.
These agents can segment audiences in real time, customize messaging based on user behavior, and adapt campaigns as customer data evolves.
It’s like having a personal marketer for every contact in your database.
Warmly’s Orchestrator functions on that principle.
It monitors your website for high-intent leads that match your ICP criteria and includes them in tailored email and campaigns.
Let’s say someone visits your pricing page twice but doesn’t sign up.
Warmly’s Orchestrator agent can recognize this intent and trigger a personalized email with a special offer, a case study, or an icebreaker message without you lifting a finger.
Learn how the Orchestrator helped a LinkedIn marketing agency, Straightin, get $10,000 in revenue in just 2 weeks.
It’s not just automated. It’s contextual and relevant, increasing the likelihood of conversion.
The beauty of this use case is in the blend of automation and intelligence.
We’re moving beyond static drip sequences toward dynamic journeys that adapt to each user.
That’s a massive leap forward for customer experience, and it's why companies using AI for personalization are seeing higher engagement and retention.
5. AI Agents for Ad Campaign Management
Running paid campaigns across Google, Meta, LinkedIn, and TikTok?
That’s a full-time job unless you’ve got an AI ad agent doing the heavy lifting.
These agents can create ad copy, select visuals, test variations, optimize bidding strategies, and monitor performance in real time.
Rather than reacting to ad fatigue or overspending days later, AI agents spot trends and pivot fast.
They might pause underperforming ads automatically or shift budget toward top-performing creatives, all while keeping costs in check and ROI on the rise.
Some even suggest new campaign angles based on historical performance and competitor data.
This use case is especially powerful for performance marketers juggling multiple platforms.
AI agents act like your 24/7 campaign managers, always learning and optimizing. That means more wins, fewer wasted dollars, and a lot less stress.
6. AI Agents for Email Marketing Automation
Email has been around forever, but with AI agents running the show, it feels brand new.
These agents don’t just schedule blasts; they tailor content, adjust send times, craft subject lines, and segment lists based on behavior, engagement, and lifecycle stage.
Imagine having an agent that knows exactly when to send a re-engagement email to a dormant lead or one that notices someone clicked your demo link but didn’t book, then follows up with a persuasive nudge.
AI makes this kind of logic-driven engagement effortless and consistent.
What makes this use case stand out is its compounding effect.
As agents collect more data over time, they get better at timing, tone, and targeting.
That leads to higher open rates, more clicks, and better conversions.
For marketers who rely on email to nurture leads or onboard customers, this is a no-brainer upgrade.
7. AI Agents for SEO Optimization
SEO is one of those marketing pillars that demands a million little tasks, such as keyword research, on-page optimization, internal linking, meta descriptions, content updates… the list goes on.
AI SEO agents streamline all of it. They can audit your site, surface ranking opportunities, suggest improvements, and even generate optimized content based on search intent.
Some go even further by automatically analyzing your competitors, identifying keyword gaps, and recommending backlink strategies.
The best ones even integrate with your CMS to push updates directly, turning what used to be a painstaking process into a near hands-free experience.
The real power here? These agents never stop.
SEO isn't something you do once and walk away from—it’s ongoing. AI agents monitor fluctuations in rankings, adapt to algorithm updates, and fine-tune your marketing strategy in real time.
That’s a level of consistency and speed that’s hard to match without a dedicated (and expensive) SEO team.
8. AI Agents for Social Media Management
Managing social media can feel like shouting into the void, and keeping up with trends, platforms, and algorithms is a job in itself.
AI social agents help take back control. They can create post variations, suggest hashtags, schedule content at optimal times, and analyze performance across multiple channels.
But they’re more than just schedulers and auto-content generators.
Many are now trained on specific tones and voices, so they can write on-brand captions and adapt messaging based on the platform (e.g., casual on TikTok, polished on Social).
Some even monitor comments and DMs, surfacing the most important ones for human follow-up or responding instantly with helpful info.
This use case is especially great for lean teams that don’t have a dedicated social strategist.
With the right AI agent, you can stay active, relevant, and responsive without burning hours every week.
Plus, it gives you real-time insights into what your audience actually cares about.
9. AI Agents for Lead Scoring and Qualification
Your pipeline is only as good as your leads. And AI agents can now qualify, score, and prioritize them better than most humans.
These agents analyze behavioral data, firmographics, engagement history, and more to determine which leads are sales-ready and which need nurturing.
No more wasting time chasing cold leads.
AI agents can flag high-intent prospects the moment they hit key triggers like visiting your pricing page, downloading a whitepaper, or opening multiple emails in a short time.
They can also automatically assign leads to the right rep or segment, keeping your CRM squeaky clean and action-ready.
The best part?
These agents aren’t just rule-based - they’re predictive.
Over time, they learn what behaviors typically lead to a sale and get sharper at spotting them early.
That means your sales team spends more time closing and less time guessing.
The 4 Best AI Marketing Agents on the Market in 2026
There’s no shortage of AI marketing tools out there, but not all of them function like true agents.
The ones below don’t just give you data or spit out content - they take action, make decisions, and deliver results.
So, let’s look at the four best AI marketing agents in 2026, each serving a unique use case.
1. Warmly – For AI-Powered B2B Lead Qualification & Customer Journey Orchestration
Warmly acts as your outbound SDR but smarter and faster.
It identifies high-intent website visitors, qualifies them using detailed B2B and intent data, and instantly personalizes outreach - via email, or AI Chat.
It’s ideal for B2B teams looking to scale personalized sales outreach without scaling headcount.
In addition to its website traffic deanonymization, AI-driven chatbot, SDR agents, and the Orchestrator, Warmly also offers:
Warmly offers a free forever plan that allows you to reveal up to 500 monthly visitors, set up ICP filters to quickly identify high-quality leads, and automate basic lead routing.
If you need more, there are three tiers to choose from:
Data Only: Starts at $499/mo when billed monthly or $4,000 when billed annually, lets you identify up to 5,000 monthly visitors, first-party intent signals, alerts, and access to Warmly’s B2B prospecting database.
Business: Starts at $19,000/year for up to 10,000 visitors or $45,000/year for up to 75,000 visitors, everything in Data Only, plus third and second-party signals, sales orchestration, AI Chat, and lead routing.
Enterprise: Custom pricing, custom number of visitors, everything in Business, plus custom signals and warm calling.
2. Jasper – For AI-Driven Content Creation
Jasper is your go-to AI agent for content production at scale.
Whether you’re creating blog posts, product descriptions, or email copy, Jasper can generate on-brand content fast.
Its workflows and brand voice memory make it perfect for marketers who want consistent messaging across multiple formats and channels.
Its key features include:
Brand Voice - Jasper lets you lock in your brand’s tone, style, and messaging guidelines so every piece of content it generates stays on-brand.
SEO & Performance Mode - Integrated with tools like Surfer SEO and Grammarly, Jasper helps you create content that ranks and reads well. Its “Performance Mode” offers real-time suggestions to improve clarity, tone, SEO score, and conversion potential as you write.
AI image generation - Jasper includes options for creating compelling visuals to go with your content.
Pricing
Jasper.ai has 3 pricing plans:
Creator: $49 month/seat (1 user only).
Pro: $69 month/seat (up to 5 users).
Business: Custom pricing.
Its Creator and Pro plans have 7-day trials, so you can try them on for size before subscribing.
3. Mutiny – For Website Personalization & CRO
Mutiny is a conversion-focused AI agent that personalizes your website content based on visitor segments.
It automatically adjusts headlines, CTAs, and messaging depending on industry, size, or funnel stage.
It’s perfect for SaaS and B2B companies trying to boost conversion rates without hand-coding every variation.
Some of its best features include:
No-code website personalization - Mutiny allows marketers to personalize website content (e.g., headlines, CTAs, and copy) based on firmographics, behavior, or campaign source for different visitor segments without touching code.
Dynamic audience segmentation - Mutiny can identify who’s visiting your site and automatically group them into actionable segments like enterprise prospects, returning visitors, or high-intent leads.
A/B testing & predictive analytics - Mutiny’s A/B testing engine lets you experiment with different variations and then uses AI-powered insights to predict which version will drive better results.
Pricing
Mutiny doesn’t disclose prices.
You’ll have to book a demo or contact its team to get more details.
4. AdCreative.ai – For Automated Ad Creative Generation
AdCreative.ai is built for performance marketers who want high-converting ads without creative burnout.
The AI generates ready-to-launch ad creatives and copy for Google, Meta, and other platforms, then refines them based on campaign performance.
It’s a great choice for agencies or teams managing multiple paid campaigns.
Its key features are:
Generates ads at scale - AdCreative.ai automatically produces high-converting ad visuals and copy for platforms like Google, Facebook, Instagram, and LinkedIn. Just input your product or campaign details, and the AI generates dozens of ready-to-launch ad variations in minutes—great for testing at scale without designer delays.
Performance-based scoring system - Each creative comes with a predictive performance score powered by AI, so you can prioritize the versions most likely to convert.
Seamless integrations with ad platforms & CRMs - AdCreative.ai connects with tools like Google Ads, Meta Ads Manager, Zapier, and HubSpot, allowing you to push creatives directly to your campaigns or workflows.
Pricing
AdCreative.ai has four pricing plans:
Starter: Starting at $39 and going up to $189 depending on the number of ad downloads you want, includes 2 users and 1 brand, in addition to access to all the platform’s AI features.
Professional: Starting at $249/mo and going up to $499/mo, includes 20 users, 3 brands, and access to Pro feature toolkit.
Ultimate: Starting at $599/mo and going up to $1099/mo, includes 50 users and 10 brands.
Enterprise: Custom pricing, has custom limits and advanced security features.
Next Steps: Automatically Engage with High-Intent Prospects on Your Website with Warmly’s AI Agents
By now, it’s clear that AI marketing agents aren’t just helpful assistants.
They’re proactive, intelligent operators that are reshaping how modern teams work, tackling everything from content creation and ad management to email automation and customer journey orchestration.
And when it comes to putting this power to work right now, Warmly is the tool that stands out.
Its AI agents and chatbots are built to identify, qualify, and engage high-intent leads the moment they land on your site, keeping your pipeline moving while letting your team focus on closing deals.
So, if you’re ready to let your website work smarter - as your next ideal customer might already be there - you’ve got two easy next steps:
Book a demo to see exactly how Warmly’s AI agents work in your flow, or
What is AI Marketing Agents Use Cases and Top Tools for 2026?
AI Marketing Agents Use Cases and Top Tools for 2026 refers to the concepts and strategies covered in this article. Understanding these fundamentals helps B2B teams improve their sales and marketing effectiveness.
Why is AI Marketing Agents Use Cases and Top Tools for 2026 important?
This matters because it directly impacts pipeline generation and revenue. Teams that master these concepts see better results from their go-to-market efforts.
How can I implement this?
Start with the strategies outlined above. For B2B teams, combining these tactics with tools like Warmly—which identifies website visitors and automates engagement—can accelerate results.
What tools help with AI Marketing Agents Use Cases and Top Tools for 2026?
Several tools can help, depending on your specific needs. Warmly is particularly useful for identifying high-intent website visitors and engaging them before they leave your site.
What are the best practices for AI Marketing Agents Use Cases and Top Tools for 2026?
Key best practices are covered throughout this article. Focus on the fundamentals first, measure your results, and iterate based on data rather than assumptions.
March 29, 2025
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How To Use AI In Account-Based Marketing (ABM)
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Chris Miller
Account-based marketing (aka ABM) has always been about precision: right account, right message, right moment.
But today, “right” isn’t good enough unless it’s also real-time, scalable, and smart.
That’s where AI flips the game.
AI-powered ABM doesn’t just automate tasks.
It helps sales and marketing teams zero in on high-value accounts, surface buying signals, personalize at scale, and adapt outreach based on live behavior, all without overburdening your team.
In this guide, I’ll break down exactly how to use AI in ABM to drive better engagement, more pipeline, and tighter sales-marketing alignment.
From intent-driven targeting to full-funnel orchestration, this isn’t theory - it’s real-world execution.
Let’s get into it!
How is AI being used in account-based marketing in 2025?
In 2025, AI has become the engine behind the most effective plays in ABM.
Namely, AI is helping GTM teams move from static lists and one-size-fits-all messaging to dynamic, data-driven account engagement.
It’s not just about who to target, but when, how, and why.
AI systems are analyzing intent signals, website behavior, CRM data, and social engagement to help teams spot opportunities earlier and tailor their moves in real time.
Instead of relying on gut feel or manual research, companies are now using AI to prioritize accounts, predict deal potential, personalize outreach at scale, and align marketing and sales around shared signals.
And while the tech might sound complex, the impact is simple: faster workflows, sharper focus, and better results.
Next, let’s look at why this shift matters, and the real benefits AI brings to modern ABM.
What are the benefits of using AI for ABM?
AI supercharges what ABM was always meant to do: engage the right accounts with the right message at exactly the right time.
But it does more than just enhance ABM. It also fixes the parts that often fall flat in traditional ABM strategies.
Here’s how:
1. Smarter account targeting
AI analyzes thousands of data points, such as firmographics, technographics, buyer intent, engagement signals, etc., to help you zero in on the accounts that are actually in-market.
As a result, instead of static ICP lists, you get a dynamic view of who’s ready to buy, and when to engage.
2. Personalization at scale
Creating custom messaging for every account used to take hours, or you were doomed to generic “Hey {first name}” attempts at genuine personalization.
With AI, you can auto-generate relevant, persona-specific emails, social content, and talking points based on account data and live signals without sacrificing quality or human tone.
3. Real-time prioritization
AI continuously monitors account behavior (email opens, site visits, social actions, CRM activity) to surface the warmest, most engaged accounts.
This way, your team knows exactly who to focus on, meaning there’s no more chasing cold leads or missing active buyers.
4. Tighter sales and marketing alignment
AI breaks down silos by giving both teams a shared source of truth.
Everyone works off the same signals, knows where an account stands, and can coordinate actions accordingly - from outbound messaging to deal support.
5. Increased efficiency and shorter sales cycles
AI eliminates manual busywork and accelerates everything from list building to campaign sequencing.
As a result, reps spend more time on high-leverage actions, allowing deals to move through the funnel faster with fewer blockers.
Top 8 ways you can use AI for account-based marketing
Exploring the benefits of integrating AI into ABM strategies showed us clearly that the synergy between these two can revolutionize how businesses engage with high-value accounts.
And now, it’s time to delve into eight practical applications of AI in ABM, ranging from more precise account prioritization to hyper-personalized outreach sequences and beyond.
1. Dynamic ICP discovery
At the heart of every successful ABM strategy lies a clear, accurate Ideal Customer Profile.
Your ICP defines who you’re targeting, that is, companies with the right size, industry, tech stack, buying behavior, and pain points.
It’s what keeps your marketing focused and ensures your sales team isn’t wasting cycles chasing poor-fit accounts.
But here’s the problem: most ICPs are built once and then left to collect dust.
They’re based on surface-level firmographics and assumptions, not real-time buying behavior.
And in fast-moving B2B markets, that’s not just inefficient - it’s costly.
This is where AI steps in.
AI doesn’t just help you define a more accurate ICP. It continuously refines it.
By ingesting and analyzing large volumes of data across multiple dimensions (firmographics, technographics, historical conversion data, product usage, site behavior, job changes, etc.), AI can uncover the deeper traits that actually correlate with pipeline velocity and deal closure.
So, instead of targeting “SaaS companies with 50–200 employees,” AI might discover that your best accounts are VC-backed SaaS teams using Snowflake that recently hired a new head of ops and visited your pricing page twice in the last week.
That’s the difference between a generic ICP and a dynamic, revenue-ready one.
This is exactly where platforms like Warmly shine.
Its AI-powered ICP engine goes beyond the basics to uncover what truly makes your best customers convert, then finds more accounts just like them.
It combines live person-level intent signals with enriched firmographic and CRM data to build a living, breathing ICP that updates in real time, helping you pinpoint who’s worth prioritizing right now.
For ABM teams, that means sharper targeting, better personalization, and far less wasted effort on accounts that were never a fit to begin with.
It’s the foundation that makes every downstream ABM play, such as ads, outreach, and nurture, way more effective.
2. Accurate account prioritization
In ABM, timing is everything.
You could have the perfect account that fits your ICP and the perfect message, and still get ignored simply because the buyer isn’t ready.
That’s why intent data has become one of the most valuable tools in modern account-based marketing.
Intent signals help answer a critical question: who’s actively in the market right now?
Instead of relying on manual research or waiting for someone to fill out a form, AI can detect buying intent by tracking a wide range of behavioral signals across digital touchpoints.
These include:
First-party signals like website visits, content downloads, and product usage.
Second-party signals, such as job changes or social engagement.
Third-party signals like keyword searches and competitor research across the web.
AI’s role here is to bring all that data together, processing thousands of micro-interactions in real-time, and turning it into actionable insight.
It identifies patterns, scores engagement, and highlights when a specific account (or even an individual) shows signs of buying readiness.
The result? You easily prioritize the right accounts and reach out when it matters, and not when it’s too early or too late.
Its platform tracks activity from across channels and enrichment sources (e.g., web behavior, job shifts, ad interactions, LinkedIn engagement, and more), then maps that activity back to individuals and accounts in your total addressable market.
And it’s not just about collecting signals. It’s about making them usable.
Warmly’s system syncs high-intent leads directly into your CRM, automatically updates segments, and even notifies your team in real-time (via tools like Slack) when an account is heating up.
That means your reps can take action immediately and with the right context in hand.
For ABM teams, this is a massive unlock.
Instead of generic nurture tracks or cold outreach, you’re delivering targeted, relevant engagement precisely when buyers are most receptive.
That’s how deals get accelerated and competitors get left behind.
3. Hyper-personalized content creation
Personalization is at the core of effective ABM.
When you’re targeting high-value accounts, generic content simply doesn’t cut it.
Decision-makers expect messaging and experiences that speak directly to their challenges, industry context, and role-specific goals.
AI makes this level of personalization possible at scale, and not just in one-off emails or ads, but across your entire content strategy.
With AI, marketers can generate personalized content for blogs, landing pages, product pages, and case studies based on account-level data and behavioral insights.
For example, AI can dynamically tailor web copy based on a visitor’s company size or industry.
A tech prospect from a mid-market SaaS company might see different use cases, testimonials, and CTAs than a healthcare enterprise executive, even though they’re on the same page.
Warmly’s DemandGen agent, for instance, makes sure to show personalized content or special offers to each account, based on their intent level, buyer journey stage, previous social engagement, web research intent, and more.
Blog posts and thought leadership can also be adapted with AI.
Instead of creating one generic article, teams can spin up multiple tailored versions for different verticals or buyer personas in seconds, each optimized for the priorities and language of the target audience.
Most importantly, AI doesn’t just help with creation - it helps with relevance.
By analyzing engagement data, AI can continuously test, optimize, and iterate content variations to understand what resonates best with each account.
The result? Content that feels specific, intentional, and valuable because it actually is.
And in ABM, that’s what builds credibility, nurtures trust, and opens doors with the accounts that matter most.
4. Predictive account targeting
One of the most powerful applications of AI in ABM is its ability to predict which accounts are most likely to convert before they raise their hand.
Unlike intent monitoring, which reacts to observable behaviors (like website visits or ad clicks), predictive prioritization uses machine learning to forecast future actions.
It analyzes historical data, such as closed-won deals, deal speed, and buyer stage, and identifies patterns across firmographics, engagement levels, buying committee structures, and campaign touchpoints.
As a result, it allows ABM teams to get ahead of the curve.
For example, if AI sees that high-converting accounts from your past share common traits, such as using a specific tech stack, having a head of RevOps, and engaging across multiple channels in a short time frame, it can flag similar accounts in your pipeline even before those behaviors fully emerge.
Instead of waiting for obvious buying signals, you can focus early outreach, personalized content, or sales development on accounts with a high likelihood to engage, giving you a competitive edge.
Predictive scoring also helps sales and marketing stay aligned.
Everyone knows which accounts deserve attention now, which need nurturing, and which to deprioritize altogether, based not on gut feel, but on actual data-driven forecasts.
In short: while intent tells you who’s active, predictive AI tells you who’s next.
5. Automated lead-nurturing sequences
In ABM, long sales cycles are the norm, especially when you're targeting complex organizations with multiple stakeholders.
That’s why effective lead nurturing is critical.
It’s not just about staying top-of-mind. It’s about moving accounts forward with the right message at the right time, for the right person.
But here’s the catch: manually managing personalized nurture across dozens - or hundreds - of target accounts isn’t scalable.
AI fixes that.
With AI, ABM teams can create intelligent, multi-channel nurture sequences that adjust automatically based on where an account is in the buyer’s journey.
Instead of using static drip campaigns, AI sequences evolve in real-time, based on actions like email opens, page views, persona engagement, or even inactivity.
They don’t just automate the follow-up - they orchestrate it.
After a lead visits your site or downloads a resource, Warmly can initiate a tailored sequence via email or LinkedIn DMs, adapt messaging based on the individual’s role or behavior, and even pause or reroute sequences when someone becomes sales-ready.
For example:
If a target persona engages multiple times with mid-funnel content, Warmly’s AI SDR agents can escalate that lead to a human rep and alert the team in Slack.
If someone ghosts after early engagement, the AI can re-engage them weeks later with a refreshed message without reps having to lift a finger.
The best part? These nurture flows are always on.
Your outreach doesn’t stop when a rep is busy or asleep, and your best-fit accounts never fall through the cracks.
With AI handling the heavy lifting, your team can focus on strategic conversations and closing, instead of chasing cold leads or repeating the same follow-up steps manually.
6. AI-powered chat engagement
In any kind of marketing - account-based included - first impressions matter, and they often happen on your website.
When a target account visits a high-intent page like pricing, features, or a demo request, that moment is your best shot at engaging them while interest is high.
But the traditional approach of waiting for them to fill out a form or hoping a rep is online often falls short.
That’s where AI-powered chatbots come in.
Today’s AI chat tools can recognize when someone from a high-value account is browsing your site and engage them instantly with personalized, relevant conversation starters.
Unlike generic chatbots that stop at “How can I help you?”, AI-driven systems use real context, like company, role, behavior, and past activity to tailor the experience in real-time.
For example, if a known account visits your pricing page, the AI chat can reference their industry or use case and offer to connect them with a specialist.
If it’s a repeat visitor, it can skip the intro and ask if they’re ready to book time with sales.
Warmly’s AI SDR agents do this exceptionally well.
They combine person-level de-anonymization with generative AI to create real-time conversations that match each prospect’s intent.
These AI chatbots can book meetings directly, answer common questions, or route the visitor to the right rep without human intervention unless it’s needed.
This always-on engagement layer is a game-changer for ABM.
You’re no longer relying on reps to catch leads in the moment.
AI ensures that when your dream account lands on your site, someone’s always there to engage them intelligently and instantly.
7. Signal-based advertising
Traditional advertising casts a wide net, and in ABM, you want a laser.
Every impression, click, and dollar should be aimed squarely at high-value accounts that actually matter.
That’s where AI-driven, signal-based advertising comes in.
Instead of building static ad audiences based on job titles or company size alone, AI allows you to create dynamic segments based on real-time behavior and buying signals.
This means you’re not just targeting accounts that fit your ICP. Instead, you’re targeting those actively showing signs of intent.
For example:
If someone from a target account downloads a case study, AI can immediately move them into a custom ad audience that highlights next-step content (like a demo offer).
If an account starts researching competitors or engages with your product pages, AI can trigger personalized ad sequences that address their specific interests or pain points.
And if an account goes cold, they can be pulled from your ad audiences automatically, saving budget and preventing wasted impressions.
This level of precision is exactly what ABM teams need, and it’s where Warmly’s Demand Gen agent plays a valuable role.
It combines real-time signal tracking with audience automation, syncing your warmest accounts into ad platforms like LinkedIn or Google Ads.
That way, you’re always reaching the right people, at the right time, with the most relevant message.
In a market flooded with generic banners and retargeting overload, signal-based advertising cuts through the noise and connects your brand with real buyers, not just browsers.
8. Intelligent lead routing and notifications
Remember how I mentioned earlier that timing is everything in ABM?
Well, in ABM, timing and context aren’t just important for your prospects - they’re essential for your internal teams, too.
Once a high-value account starts engaging, the question becomes: Who on your team should act on this? And how fast can they do it?
AI-driven lead routing solves both problems.
Instead of using rigid, rules-based systems, AI evaluates each lead in context, considering their persona, level of intent, stage in the journey, and historical behavior, and automatically routes them to the right rep or team, at the right moment.
For example:
If a mid-funnel operations lead from a strategic account visits your ROI calculator, AI can flag this as a hot lead and route it directly to a senior AE.
If a new visitor from a target account downloads a gated asset, AI might assign them to a BDR for personalized outreach.
If multiple stakeholders from the same company engage in a short window, AI can trigger coordinated follow-up across sales and marketing.
This isn't just about faster responses. It’s about smarter handoffs, better alignment, and less lead leakage.
Tools like Warmly enhance this by combining real-time signal monitoring with intelligent Slack, email, and CRM alerts.
Reps get notified the moment someone from a key account engages, along with the context they need to respond in a relevant, personalized way.
In practice, this means no more “random acts of outreach” or siloed follow-up.
AI ensures your team is always in sync with what’s happening on the buyer side, making your ABM motion feel cohesive, responsive, and human.
4 best AI-powered ABM tools on the market
With AI now driving the most effective ABM strategies, the right tech stack can make or break your execution.
Below are four standout AI-powered ABM tools that help B2B teams identify, prioritize, and engage high-value accounts with precision, each bringing unique capabilities to the table.
1. Warmly - Real-time intent tracking and automated multichannel outreach
Warmly is an AI-powered ABM platform that helps B2B teams uncover, prioritize, and engage high-intent accounts through signal-based intelligence and agentic automation.
It monitors 1st, 2nd, and 3rd-party buying signals at the person level, enabling marketing and sales to act in real time with personalized, channel-aware outreach.
With a suite of AI agents, covering sales outreach, demand gen, and marketing, Warmly turns intent into action across your pipeline.
Standout features
AI SDR agents - These automate email and LinkedIn outreach sequences based on signal strength, persona, and behavior.
Demand Gen agent - Automatically builds audience segments and syncs them to ad platforms based on buying signals for hyper-targeted advertising.
Dynamic ICP - Leverages AI to identify accounts matching your ICP based on granular insights that go far beyond firmographics, including behavior, intent, etc.
Company and person-level de-anonymization - Identifies companies and specific individuals visiting your site and maps them to their roles and accounts.
Signal-based scoring - Uses 1st, 2nd, and 3rd-party data (e.g., website visits, job changes, social activity, competitor research) to score and segment accounts in real time.
Slack and CRM integration - Instantly alerts sales teams when key accounts engage, complete with context and suggested next steps.
Intelligent lead routing - Automatically assigns leads to the right rep based on behavior, funnel stage, and fit.
Pricing
Warmly offers a free forever plan that allows you to reveal up to 500 monthly visitors, set up ICP filters to quickly identify high-quality leads, and automate basic lead routing.
If you need more, there are three tiers to choose from:
Data Only: Starts at $599/mo when billed monthly or $5,000 when billed annually, lets you identify up to 5,000 monthly visitors, first-party intent signals, alerts, and access to Warmly’s B2B prospecting database.
Business: Starts at $19,000/year for up to 10,000 visitors or $45,000/year for up to 75,000 visitors, everything in Data Only, plus third and second-party signals, sales orchestration, AI Chat, and lead routing.
Enterprise: Custom pricing, custom number of visitors, everything in Business, plus custom signals and warm calling.
2. 6sense - Predictive analytics and intent-based account prioritization
6sense is an AI-powered revenue platform that helps B2B teams uncover in-market accounts, predict buying behavior, and orchestrate multi-channel engagement.
By combining intent data, predictive analytics, and account intelligence, it enables sales and marketing to focus on the accounts most likely to convert.
Standout features
Granular intent data - Aggregates behavioral signals to identify buying intent across accounts.
Predictive lead scoring - Ranks accounts based on likelihood to engage and convert.
Intelligent workflows - Lets you automate various sales and marketing operations across channels.
Pricing
6sense has a free plan that provides 50 credits/month, Chrome Extension, list builder, sales alerts, and company and people search.
Sales Intelligence + Data Credits + Predictive AI: The most comprehensive package, includes all of 6Sense’s features, such as predictive AI models, scores, and dashboards, sales Copilot, AI recommendations, company and contact insights, alerts, automated workflows, etc.
Sales Intelligence + Data Credits: Includes access to some features related to sales (e.g., sales Copilot, AI writer, etc.) and data (limited company and contact insights).
Sales Intelligence + Predictive AI: Also includes access to some features related to sales and some regarding predictive AI modules (e.g., predictive scoring and dashboards).
However, 6sense doesn’t disclose actual prices for any of the packages.
You’ll have to contact their team for a custom quote.
3. Demandbase - Comprehensive ABM orchestration and personalization
Demandbase is an end-to-end ABM platform that uses AI to unify account intelligence, personalize experiences, and drive coordinated engagement across sales and marketing.
It empowers teams to target high-value accounts with precision by combining firmographic data, real-time behavior tracking, and AI-driven orchestration.
Standout features
Account identification - Uses AI to discover and prioritize target accounts showing buying signals.
Website personalization - Customizes web experiences based on account attributes and behaviors.
ABM analytics - Tracks performance metrics to measure the impact of ABM campaigns.
Pricing
Demandbase doesn’t disclose its prices.
You’ll have to contact its sales to get a custom quote based on your business goals and needs.
4. RollWorks - Scalable ABM for growing B2B companies
RollWorks offers a scalable ABM solution for growing B2B teams, using AI to identify target accounts, activate cross-channel campaigns, and measure engagement.
It’s designed to help marketers focus on accounts with the highest potential, even with limited resources or headcount.
Standout features
Account targeting - Combines firmographic and intent data to pinpoint ideal accounts.
Cross-channel engagement - Runs coordinated campaigns across display ads, social media, and email.
High precision B2B advertising - RollWorks uses machine learning algorithms to optimize budgets, reach, frequency and engagement on the fly.
Pricing
RollWorks has two pricing tiers:
Account-Based Advertising: Includes precision targeting across all digital channels, custom audiences, etc.
Account-Based Marketing: Includes predictive buying signals, buyer insights, detailed analytics and key metrics tracking, etc.
However, RollWorks doesn’t publish prices for any of the packages, so you’ll have to speak to its team directly.
What are some of the challenges of incorporating AI into your ABM strategy?
While AI can dramatically enhance account-based marketing, integrating it into your strategy isn’t always plug-and-play.
Many teams encounter roadblocks that can delay adoption or undermine ROI if not addressed early.
Let’s look at some of the most common challenges:
1. Data readiness and quality
AI is only as good as the data it’s fed.
If your CRM is messy, intent signals are incomplete, or account data is outdated, your AI models may generate inaccurate insights or poor prioritization.
Ensuring clean, enriched, and unified data across systems is foundational for AI-driven ABM to work.
2. Complexity and integration
AI tools often need to integrate with CRMs, marketing automation platforms, ad systems, and sales workflows.
Without tight integration, insights get lost in silos or require manual workarounds, defeating the purpose of automation.
Teams need to assess whether their current sales and marketing tech stack is flexible and open enough to support real-time orchestration.
3. Over-personalization or “uncanny” messaging
AI can personalize content at scale, but if it goes too far or lacks context, it risks coming off as robotic or intrusive.
Striking the balance between relevance and authenticity is key, especially in high-touch ABM programs where trust and tone matter, so it’s best to combine AI with human editors.
4. Skills and confidence gaps
Not every team is immediately comfortable using AI tools or trusting their output.
There’s often a learning curve, and without internal champions or proper onboarding, adoption can stall.
Training, experimentation, and transparency in how AI makes decisions can help build trust.
5. Budget and resource constraints
While many AI-powered ABM tools offer strong ROI, they can be a stretch for smaller teams or early-stage companies.
It’s important to assess not just the tool’s capabilities, but whether your team has the resources to implement and manage it effectively.
Next steps: Putting AI-powered ABM into motion
AI isn’t just reshaping ABM - it’s redefining what’s possible.
From real-time intent monitoring to predictive prioritization and fully automated outreach, AI allows GTM teams to move faster, target smarter, and personalize at a level that was unthinkable just a few years ago.
But tools alone don’t create impact.
The real wins come when strategy, systems, and AI work together, aligning sales and marketing, focusing on the right accounts, and delivering value at every touchpoint.
If you’re ready to scale your ABM strategy with AI that’s built for real-world execution, not just dashboards, Warmly can help.
Our AI marketing agents don’t just surface signals. They act on them, helping you turn intent into pipeline with speed and precision.
Book a demo and find out how Warmly can power your next phase of ABM.
What is the best way to use ai in account-based marketing [2025]?
The best approach depends on your specific situation. Follow the step-by-step guide above for proven methods. Key success factors include proper setup, consistent execution, and measuring results.
How long does it take to use ai in account-based marketing [2025]?
Timeline varies based on complexity and resources. Simple implementations take days, while comprehensive strategies may take weeks to fully execute. Start with quick wins outlined above.
What tools do I need to use ai in account-based marketing [2025]?
Essential tools are covered in the guide above. For B2B sales teams, Warmly can help by identifying website visitors and providing intent signals to prioritize your efforts.
What are common mistakes when trying to use ai in account-based marketing [2025]?
Common pitfalls include moving too fast without proper setup, not measuring results, and using outdated tactics. Follow the best practices above to avoid these issues.
Can I automate this process?
Many aspects can be automated with the right tools. Warmly offers automation for website visitor identification and engagement. See the tools section above for automation options.
What Is Agentic Automation? 10 Use Cases & Software
Time to read
Chris Miller
Agentic automation has already become a full-blown shift in how GTM teams operate.
Instead of building rigid workflows or babysitting sequences, teams are now deploying self-directed AI agents that act, not just react.
These agents don’t wait for triggers. They know the goal (“book more demos,” “revive ghosted leads,” “qualify inbound”) and take the best route to get there, adapting in real time as things change.
For sales, marketing, and revenue teams tired of duct-taping tools together or manually chasing tasks across channels, agentic automation delivers a smarter, more autonomous way to scale.
In this article, I’ll break down exactly what agentic automation is, how it works, real-world use cases across the GTM funnel, and the top tools powering this new era.
Ready to leave behind brittle workflows? Let’s begin by answering the key question:
What is agentic automation?
Agentic automation is the next evolution of AI-powered workflows, but with one big difference: it doesn’t wait around for instructions.
Instead of triggering actions based on fixed rules or linear workflows, agentic automation uses self-directed AI “agents” that can think, plan, and act on their own to achieve a goal.
You tell the agent what you want done (e.g., “revive inactive leads” or “book demos from inbound form fills”), and it figures out how to make that happen across tools, channels, and steps.
This isn’t your typical automation that just runs a playbook.
Agentic AI adapts in real-time. It can change direction mid-task, pull in new information, and even ask clarifying questions when the path forward isn’t clear.
Think of it less like just another tool and more like hiring a proactive teammate who just gets it done. And that’s exactly why it matters.
Because today’s GTM teams aren’t struggling with a lack of tools. They’re struggling with too many disconnected tasks, too much noise, and too little time.
Agentic automation cuts through that by handling high-leverage, multi-step workflows that actually move the needle.
In the next few sections, I’ll cover exactly why this matters (spoiler: it's more than just efficiency), how it’s different from regular automation, and which industries are seeing the biggest wins.
What are the benefits of using agentic automation?
So, why should everyone - and I mean everyone - in the GTM world consider incorporating agentic automation in their operations?
Well, the thing is that agentic automation doesn’t just make things faster.
It makes them smarter, more scalable, and way less manual.
Here are some of the real benefits you can expect:
1. Handles real work, not just busywork
Traditional automation is great at repeating simple tasks.
But agentic automation can take on complex, multi-step workflows, like qualifying leads, booking meetings, or reactivating ghosted deals, without needing you to map out every step.
2. Adapts in real-time
Agentic AI can respond to changing inputs on the fly.
If a lead replies with a new question, if a meeting gets rescheduled, or if more context becomes available, the agent adjusts its plan without breaking the workflow.
3. Connects the dots across your stack
Instead of relying on brittle automations or juggling five different tools, agentic automation can work across systems, pulling info from your CRM, sending outreach via email, updating calendars, and more.
The best agents orchestrate all of this behind the scenes, so the workflow actually flows.
4. Frees up your team to focus on higher-leverage tasks
AI agents can handle everything from outreach and follow-up to CRM enrichment and handoffs.
As a result, your team can spend more time closing deals, crafting strategy, and driving growth.
5. Delivers outcomes, not just actions
You don’t have to pre-program every click.
You just set the goal (“increase demo bookings from inbound leads”) and the agent figures out the best path to get there, end-to-end.
6. Scales with zero burnout
Agents don’t get tired.
You can spin up five, ten, or fifty agents to handle different GTM motions from inbound to outbound to post-sale, all working in parallel without adding headcount.
What’s the difference between regular automation and agentic automation?
Let’s be real: most “automations” today are just glorified to-do lists.
You set a trigger → it runs a predefined action → and that’s about it.
Works great when everything’s predictable.
But the second something goes off-script, like a lead replying with a curveball question or a data field missing in your CRM, things fall apart.
Agentic automation changes the game entirely.
Here’s a quick overview of how regular and agentic automation stack up:
FeatureRegular AutomationAgentic AutomationWorks off fixed rules✅❌Can adapt to changing inputs❌✅Needs every step pre-built✅❌Understands context & goals❌✅Executes across multiple systems⚠️ (workarounds)✅Can reason, decide, and act❌✅Delivers outcomes, not just actions❌✅
So, instead of building 47 logic branches for every possible scenario, you just tell the agent the goal, and it figures out the best way to get there.
Think of it like the difference between:
A basic email sequence tool that sends a pre-set drip campaign vs. an AI SDR that knows your ICP, monitors engagement signals, adapts copy, and books meetings autonomously.
One is rule-based. The other is agentic.
And guess which brings better results.
The industries that can best leverage agentic automation
Agentic automation isn’t just changing how we automate. It’s also changing who can benefit.
Now that AI agents can reason, adapt, and execute on high-level goals without hand holding, we’re seeing a wave of adoption across industries where traditional automation used to hit a wall.
Here’s a look at the industries already seeing results and how agentic automation is helping them unlock new value:
1. B2B sales & marketing
This is where agentic automation is already making serious noise.
GTM teams are using AI agents to:
Score and qualify inbound leads the moment they hit the website.
Warm up cold prospects across email.
Revive ghosted deals with smart, context-aware follow-ups.
Create dynamic, targeted ad audiences fine-tuned to actual intent.
The thing that sets these agents apart from regular automation is that they don’t just run playbooks.
Instead, they adapt on the fly, score leads based on real-time intent, and trigger outreach or routing without waiting for a rep to jump in.
The result? More qualified pipeline, fewer dropped balls, and a sales team that can focus on closing instead of chasing.
2. Customer success & revenue operations
Agentic automation isn’t just a sales tool. It’s also a post-sale powerhouse.
AI agents help CS and RevOps teams manage seamless handoffs, keep tabs on customer health, and surface churn risks before they become problems.
They can do things like:
Trigger personalized renewal nudges.
Coordinate upsell plays.
Loop in product or support when issues arise, and more.
It’s like having a proactive, always-on account coordinator that never drops the ball, keeping customers engaged, informed, and more likely to stick around.
3. SaaS & tech
Speed matters in SaaS, and agentic automation gives fast-moving teams the edge.
AI agents can qualify trial users in real time, trigger onboarding flows based on in-app behavior, and send personalized follow-ups after key product actions, all without human babysitting.
For PLG teams, it’s a game changer, as you can drive usage-based upsells, renewals, and outreach that feels 1:1 without overwhelming your reps.
It’s personalization at scale, with zero burnout.
4. Healthcare
In complex, highly regulated environments like healthcare, agentic automation is helping teams cut through administrative friction.
AI agents can extract insights from unstructured clinical data, coordinate workflows across care teams, and ensure compliance documentation is handled without constant manual input.
They even support personalized care journeys at scale, adapting to patient history, behavior, and treatment plans in real time.
The goal is to streamline operations without losing the human touch, and these agents excel in it.
5. Financial services & insurance
In fast-paced, high-stakes industries like finance and insurance, speed and accuracy are everything.
Agentic automation is being used to triage loan and claim applications, extract and analyze documents, and pre-qualify customers based on real-time data, at a fraction of the time humans would need for the same tasks.
AI agents can even flag fraud patterns on the fly, spotting risks faster than traditional rule-based systems.
When decisions need to be made at scale without sacrificing precision, agents are the perfect fit.
6. IT
IT teams are no strangers to chaos, as tickets, system requests, data syncs, and endless Slack pings pile up fast.
Agentic automation acts like a behind-the-scenes engine that keeps everything running smoothly.
Agents can triage incoming requests, spot workflow inefficiencies, coordinate updates across internal tools, and even monitor systems for outages or anomalies, taking action before alerts hit your inbox.
And in complex tech environments, it’s like giving your IT team a superpower.
Top 10 real-world use cases of agentic automation
Now that we’ve covered what agentic automation is and where it’s making the biggest impact, let’s look at what it can actually do.
From qualifying leads and running campaigns to analyzing contracts and fixing IT issues, agentic AI is already powering real-world workflows by making decisions, adapting to context, and driving outcomes at scale.
Here are 10 powerful, real-world use cases showing how agentic automation is being put to work today:
1. Lead scoring and qualification in real time
In most sales orgs, lead scoring is a fixed formula: assign points for job title, company size, maybe a few clicks on your site, and hope it tells you who’s worth following up with.
The problem? Buyers don’t move linearly anymore, and static models miss the nuance.
Timing, intent, behavior, and context all matter, and traditional automation just isn’t built to handle that.
Agentic automation flips this on its head.
Instead of assigning scores based on rigid rules, AI agents continuously monitor behavior, detect buying signals, and qualify leads dynamically.
These agents don’t just calculate scores - they understand actual intent.
If someone’s bouncing between pricing pages, revisiting key blog posts, or engaging across multiple touchpoints, the agent recognizes it’s time to act, even if that lead wouldn’t have passed a traditional threshold.
Warmly’s Marketing Ops Agent tracks dozens of real-time signals - such as website engagement, recency, firmographic match, traffic source, ad interactions, and even de-anonymized visitor data - to determine which leads are warm right now.
Then, it combines these warm signals with relevant data from over 10+ data providers, creating deep, contextual insight that allows it to spot in-market buyers as soon as they show intent.
Even better, this agent actively improves your ICP over time.
By analyzing which types of leads actually convert, and not just which ones look good on paper, the system continuously sharpens your targeting criteria and surfaces lookalike prospects who match the behavioral patterns of your best customers.
So instead of your reps wondering who to follow up with, agentic automation ensures they’re always working the right leads, at the right time, with context that goes far deeper than traditional MQL checklists.
2. Personalized outreach that runs 24/7
One of the most time-consuming parts of B2B sales is outbound prospecting, that is, finding the right people, personalizing messages, sending follow-ups, and keeping track of engagement.
It's manual, repetitive, and doesn’t scale well, which is why most SDRs end up buried in busywork instead of actually starting conversations that lead to pipeline.
Agentic automation changes that.
With AI sales agents, teams can automate outbound efforts from end to end - from identifying high-intent accounts to sending personalized sequences across channels like email.
And the best part is that these agents don’t just blast generic messages.
They understand which accounts are heating up, who the key stakeholders are, and how to engage them in a meaningful, relevant way - automatically.
Warmly’s AI SDRs are built specifically for this.
They act as full-time, always-on sales assistants that prospect, engage, and nurture leads around the clock, without ever needing a break.
Here’s what they do:
Monitor intent signals to detect when an account is showing buying behavior.
Automatically find key stakeholders across the target account.
Trigger personalized outbound sequences on behalf of reps via email.
Engage leads via contextual website chats and directly book meetings without the manual back and forth.
And because they're always running, you can scale outreach volume dramatically without needing to scale headcount.
3. Hyper-targeted advertising based on live signals
Most B2B ad campaigns still rely on broad targeting, using criteria such as job titles, company size, and industries.
And while that can work, it often wastes spend on the wrong audience or reaches the right people at the wrong time.
What these campaigns lack is context: who’s actually in-market right now, and what are they reacting to?
Agentic automation fills that gap by connecting real-time intent signals with ad delivery.
Instead of relying on static lists or outdated firmographics, AI agents monitor on-site and off-site behavior, and then automatically build and sync high-intent segments to your ad platforms.
That means your campaigns are constantly evolving based on who’s showing interest, what they’re engaging with, and where they are in their buying journey.
Warmly enables exactly this kind of intelligent advertising motion.
Its agentic demand gen engine tracks live buying signals across your site and other digital properties, including:
Onsite behavior (e.g. pricing page visits, return traffic, time spent).
Social engagement (e.g., relevant interactions with your posts or participation in discussions on topics related to your product).
Third-party signals (researching keywords related to your product, visits to competitors’ pages, etc.).
Relevant B2B & CRM data (like industry, title, and stage).
As Warmly detects interest surges from specific accounts or personas, it dynamically updates target segments and syncs them directly to your ad channels.
This allows you to run hyper-specific campaigns, like surfacing a custom offer to only mid-market marketing leaders from in-market accounts, without lifting a finger.
Better yet, agents can automatically stop serving ads to leads who have already converted or passed a certain funnel stage, so your spend stays efficient.
Instead of your team constantly rebuilding audiences and guessing when to retarget, agentic AI advertising lets you stay relevant automatically and in real-time.
4. Automated lead follow-up and nurturing
Following up with leads sounds simple until you’re juggling dozens of conversations, multiple channels, and varying levels of interest.
The truth is, most leads don’t convert on the first touch.
But keeping track of who to follow up with, when, and how? That’s where most teams drop the ball.
Traditional automation helps, but it’s rigid.
Set-it-and-forget-it sequences often lack personalization, can’t adapt to behavior changes, and don’t escalate when engagement spikes.
That’s where agentic automation makes a major difference.
These agents don’t just send templated messages on a fixed timeline.
They monitor each lead’s actions - like revisiting your site, clicking an ad, or engaging with content - and adjust their outreach accordingly.
Messaging gets smarter, timing gets sharper, and high-intent leads get fast-tracked.
Warmly’s SDR agents handle this in the background 24/7.
Once a lead enters the funnel, whether through chat, form fill, or signal detection, Warmly’s agents automatically:
Determine the right follow-up path based on intent and behavior.
Launch personalized email and sequences (avoiding duplication if reps are already engaged).
Monitor responses and engagement patterns.
Escalate qualified leads to your reps at exactly the right moment.
5. Contextual customer support
Support teams are often overwhelmed by volume, from basic questions (“Where’s my order?”) to more complex issues that need routing or escalation.
Traditional chatbots help with the basics, but they’re limited by rigid scripts and can’t adapt when conversations go off course.
Agentic automation unlocks a more flexible, intelligent approach.
Instead of just responding to predefined questions, agentic AI support agents can:
Understand context.
Ask clarifying questions.
Pull relevant data from multiple systems.
Execute multi-step actions to resolve an issue.
Escalate to a human when needed, with full context included.
And because these agents operate around the clock, they reduce wait times, increase resolution speed, and free up human agents to focus on higher-complexity requests.
This kind of hands-off support is especially valuable for fast-growing SaaS companies, e-commerce platforms, and service-based businesses - anywhere customer expectations are high and team capacity is limited.
The result? Better experiences, lower support costs, and faster response times, all powered by agents that never sleep and never burn out.
6. Predictive maintenance in manufacturing
In manufacturing, downtime is expensive, whether it's a single machine going offline or an entire line coming to a halt.
Traditional maintenance strategies rely on fixed schedules or reactive repairs, which either waste resources or result in costly interruptions.
Predictive maintenance, powered by agentic automation, offers a smarter alternative.
These agents monitor real-time data from IoT sensors embedded in equipment, such as vibration, temperature, or energy consumption, and use AI to detect subtle patterns that indicate potential failure.
But unlike traditional monitoring tools that only alert humans, agentic agents can go a step further: they assess the risk, determine the best course of action, and trigger maintenance workflows autonomously.
In highly automated factories, this level of self-directed decision-making leads to:
This type of automation gives manufacturing teams an intelligent layer of defence that adapts to real-world conditions without constant oversight.
7. Personalized customer experiences on-site
When a high-intent visitor lands on your website, timing and relevance make all the difference.
But most sites still treat every visitor the same with static content, generic CTAs, and one-size-fits-all experiences.
That’s a wasted opportunity, especially when you're dealing with accounts that are actively researching your product.
Agentic automation lets you tailor the on-site experience in real-time, based on who the visitor is, where they came from, and how engaged they are.
These agents don’t just personalize headlines. They can adjust offers, trigger live chat, recommend next steps, and adapt content dynamically as the visitor browses.
Warmly uses agentic AI to do exactly that.
Its system identifies anonymous traffic and enriches it with firmographic and behavioral data.
And then, based on persona, traffic source, and intent level, agents can:
Display personalized offers or CTAs (“Ready for a demo?” vs “See how it works”).
Trigger intelligent chatbot conversations.
Suggest relevant case studies, feature pages, or pricing content.
Route high-value visitors directly to live reps or AI chat.
This way, instead of just optimizing for form fills, agentic on-site experiences guide leads through their own journey.
The result? Higher conversion rates, more qualified conversations, and better first impressions without requiring your team to lift a finger.
8. Legal and contract analysis at scale
Legal work is often seen as too complex or nuanced to automate, and in many cases, that’s true.
But much of the day-to-day legal load, especially contract review, is repetitive and rules-based: finding key clauses, identifying risks, checking compliance, and flagging inconsistencies.
And that’s where AI agents step in.
Unlike basic document scanning tools, agentic AI can process legal documents with contextual understanding.
They can analyze NDAs, vendor agreements, terms of service, and more, surfacing missing terms, suggesting revisions, and routing specific risks to the right legal stakeholders.
For in-house legal teams and firms dealing with high volumes of contracts, this leads to:
Faster turnaround times.
Reduced human error.
More consistent compliance.
Better allocation of legal talent to complex, high-value matters.
It’s a prime example of agentic automation replacing process friction with smart, autonomous execution, even in industries known for caution and complexity.
9. IT operations and incident resolution
Modern IT environments are increasingly complex, with hybrid infrastructure, dozens of monitoring tools, and constant pressure to keep systems up and running.
And when something breaks, every minute counts. But traditional incident response often looks like this:
Wait for an alert → check a dashboard → escalate to the right person → hope it gets resolved fast.
Agentic automation turns this reactive model into a proactive, autonomous one.
Instead of relying on static thresholds and manual triage, agentic AI can:
Monitor system health continuously across environments.
Detect anomalies using historical patterns and real-time data.
This isn’t just “automation” in the ticketing sense.
These are AI agents that reason, act, and improve, learning from past incidents and outcomes to respond faster and smarter over time.
10. HR and recruitment workflows
Hiring great people is critical, but it’s also one of the most time-consuming and resource-intensive functions in any company.
Between reviewing resumes, scheduling interviews, sending assessments, updating candidate pipelines, and keeping everyone aligned, it’s no wonder recruiters are overwhelmed.
Agentic automation is changing the game for talent teams by handling many of these workflows autonomously and intelligently.
These agents can do far more than just parse resumes. They:
Analyze job descriptions and candidate profiles to assess fit.
Rank applicants based on customizable hiring criteria.
Send personalized outreach and interview requests.
Coordinate scheduling based on availability across calendars.
Provide real-time status updates to hiring managers.
Flag potential red flags (like job-hopping or skill mismatches) and escalate for review.
What sets agentic systems apart is their ability to adapt.
For example, if a candidate suddenly becomes inactive, the agent can adjust the outreach cadence or route the lead to a talent pool for future roles.
If a hiring manager leaves feedback, the agent updates its ranking logic for the next batch of applicants, learning as it goes.
The result?
Shorter time-to-hire, reduced admin burden on recruiters, and more consistent candidate experience across departments.
4 best agentic automation tools on the market
Not all AI tools are truly agentic.
Many still rely on rigid rules or simple workflow builders.
But a new wave of platforms is giving teams access to actual autonomous agents that can reason, adapt, and execute across complex tasks with minimal input.
Here are four of the best agentic automation tools on the market today.
1. Warmly
Best for: B2B revenue teams that want to scale outreach, qualification, and lead follow-up with AI SDR agents.
Warmly brings agentic automation to sales and marketing with AI SDRs, Demand Gen, and Marketing Ops agents that can orchestrate your entire funnel from top to bottom.
It's purpose-built for GTM teams, replacing manual prospecting, nurturing, and follow-up with autonomous workflows that actually drive pipeline.
Standout features
AI SDRs that prospect, sequence, and multi-thread - Automatically identify high-intent accounts and contacts, then launch multi-step outbound campaigns across email, just like a human rep would.
Real-time lead qualification - Continuously scores and prioritizes leads using dozens of live intent signals, CRM data, and engagement history to surface the hottest opportunities.
Dynamic ICP creation - Combines live intent signals and historical data to continuously update your ICP, ensuring your campaigns are relevant and perfectly targeted every single time.
Adaptive ad targeting agents - Automatically syncs high-intent lead segments to your ad platforms, so only the right audiences see your campaigns, at the right time.
Pricing
Warmly offers a free forever plan that allows you to reveal up to 500 monthly visitors, set up ICP filters to quickly identify high-quality leads, and automate basic lead routing.
If you need more, there are three tiers to choose from:
Data Only: Starts at $599/mo when billed monthly or $5,000 when billed annually, lets you identify up to 5,000 monthly visitors, first-party intent signals, alerts, and access to Warmly’s B2B prospecting database.
Business: Starts at $19,000/year for up to 10,000 visitors or $45,000/year for up to 75,000 visitors, everything in Data Only, plus third and second-party signals, sales orchestration, AI Chat, and lead routing.
Enterprise: Custom pricing, custom number of visitors, everything in Business, plus custom signals and warm calling.
2. UiPath
Best for: Enterprise IT and operations teams looking to integrate agentic AI with RPA and existing automation stacks.
UiPath is extending its legacy in robotic process automation with AI agents that can reason, plan, and take independent action.
It's ideal for enterprises managing complex systems that span across departments.
Standout features
Agent Builder for autonomous workflows - Lets teams design and deploy agents that interact with apps, systems, and users to complete multi-step processes.
LLM-powered engines - Empowers agents to process natural language, extract context, and make on-the-fly decisions based on current state and data inputs.
Process orchestration for hybrid teams - Coordinates humans, agents, and bots within a unified workflow, ensuring smooth handoffs and goal alignment.
Pricing
UiPath has three pricing plans:
Basic: Starting at $25 per month, includes basic automation builder, EU hosting, etc.
Standard: N/A, includes everything in Basic, plus advanced automation builder, enhanced governance controls, agents, robots, and people orchestration, etc.
Enterprise: N/A, includes everything in Standard, plus self-healing UI automation builder, dashboards for monitoring, optimizing, and simulating live business processes, automatic optimizations of your own Robot infrastructures, etc.
Since UiPath doesn’t disclose prices for its Standard and Enterprise plans, you’ll have to contact its sales team for more details.
3. Paradox
Best for: Talent acquisition teams seeking to automate candidate screening, outreach, and scheduling.
Paradox automates repetitive hiring tasks with a conversational AI agent that acts like a recruiting assistant.
It tackles everything from screening candidates and booking interviews to providing real-time updates without HR intervention.
Standout features
Conversational AI assistant ("Olivia") - Engages candidates through chat to qualify them, answer questions, and guide them through the hiring process automatically.
Behavior-adaptive workflows - Adjusts follow-up timing and messaging based on candidate responsiveness and past interactions, improving engagement rates.
Integrated interview coordination - Connects with hiring managers’ calendars and books interviews without needing recruiters to manually intervene.
Pricing
Paradox doesn’t publish prices.
You’ll have to contact its sales team for more information.
4. BigPanda
Best for: IT operations teams aiming to automate incident detection, response, and resolution.
BigPanda applies agentic AI to IT operations by enabling AI agents to detect, diagnose, and resolve incidents autonomously.
It integrates with observability tools to create a self-healing environment that reduces alert fatigue and downtime.
Standout features
Autonomous incident correlation - Groups related alerts together using AI, so teams aren’t overwhelmed with noise and can focus on root causes.
Root cause analysis agents - Analyzes logs and metrics in real time to pinpoint why issues are happening and suggests (or initiate) fixes automatically.
Self-learning optimization - Continuously refines detection and resolution logic based on historical incident data and resolution outcomes.
Pricing
BigPanda doesn’t publish any information regarding its packages or their exact prices.
You can book a demo to learn more.
What are some of the challenges associated with incorporating agentic automation into your sales strategy?
Finally, while agentic automation has the power to transform sales, it’s important to understand that adopting it efficiently isn’t without its challenges.
It’s not just about installing a tool and flipping a switch. It requires a thoughtful shift in how your team works, how your data flows, and how you measure success.
Here are some of the most common challenges to watch for:
1. Data readiness and hygiene
Agentic AI relies on accurate, real-time data to make decisions.
If your CRM is a mess, your enrichment sources are outdated, or your lead activity isn’t being tracked consistently, agents will struggle to operate effectively - or worse, make poor decisions.
Before deploying agents, teams need to invest in cleaning up data pipelines and ensuring core GTM systems are properly integrated.
2. Over-automation without context
Just because agents can automate something doesn’t mean they should.
Without clear guardrails, agentic workflows can lead to robotic outreach, misrouted leads, or impersonal customer experiences.
Sales leaders need to set clear goals, define when humans should stay in the loop, and ensure agents are enhancing, not replacing, meaningful interactions.
3. Integration complexity
Agentic automations often need to work across multiple platforms, including CRM, email, ad platforms, chat, calendar, and more.
If your systems are fragmented or lack API support, you may hit serious friction.
Successful implementation often requires coordination between sales, marketing, ops, and IT to create a connected, automation-friendly environment.
4. Rep adaptation and process change
Reps may be sceptical, or even resistant, when they hear “AI SDR.”
And that’s fair.
If not rolled out properly, agentic tools can feel like a black box or a threat to their role.
Teams need to invest in onboarding, education, and transparency, positioning agents as assistants that free reps up to focus on higher-leverage work, not tools that replace them.
5. Monitoring, optimization, and oversight
Unlike static workflows, agentic automation evolves over time.
That’s powerful, but it also means you need systems in place to monitor performance, review outputs, and tune behavior.
Clear ownership is critical to keep things on track and aligned with strategy.
6. Measuring success beyond vanity metrics
It’s tempting to measure success based on volume, such as more sequences launched, more contacts reached.
But agentic automation should be evaluated based on real outcomes: higher conversion rates, more qualified meetings, smoother handoffs, and less manual work.
Teams need to align on the right KPIs before scaling automation.
Next steps: Agentic automation is already here - the only question is how you’ll use it
The way sales teams operate is changing, and fast.
Agentic automation isn’t some future-state vision.
It’s already booking meetings, qualifying leads, running follow-ups, and moving pipeline forward in real time.
The teams that win in this new era won’t be the ones with the most tools. They’ll be the ones who know how to orchestrate outcomes, and not just send more generic messages.
That’s where agentic AI shines. It doesn’t just follow rules. It understands context. It adapts. It acts.
And when used right, it frees your team to focus on the parts of selling that actually require a human, such as building trust, closing deals, and thinking strategically.
Curious how it all works in action?
Warmly’s AI agents are helping revenue teams like yours scale without the spam, book more qualified meetings, and stay top-of-mind with the right leads, all day, every day.
Book a demo and see what agentic automation looks like in real life.
What is What Is Agentic Automation? 10 Use Cases & Software [2025]?
What Is Agentic Automation? 10 Use Cases & Software [2025] refers to the concepts and strategies covered in this article. Understanding these fundamentals helps B2B teams improve their sales and marketing effectiveness.
Why is What Is Agentic Automation? 10 Use Cases & Software [2025] important?
This matters because it directly impacts pipeline generation and revenue. Teams that master these concepts see better results from their go-to-market efforts.
How can I implement this?
Start with the strategies outlined above. For B2B teams, combining these tactics with tools like Warmly—which identifies website visitors and automates engagement—can accelerate results.
What tools help with What Is Agentic Automation? 10 Use Cases & Software [2025]?
Several tools can help, depending on your specific needs. Warmly is particularly useful for identifying high-intent website visitors and engaging them before they leave your site.
What are the best practices for What Is Agentic Automation? 10 Use Cases & Software [2025]?
Key best practices are covered throughout this article. Focus on the fundamentals first, measure your results, and iterate based on data rather than assumptions.
AI Data Enrichment: What Is It & How To Do It [2026]
Time to read
Chris Miller
In 2026, sales teams don’t win with more data. They win with smarter data.
That’s where AI data enrichment comes in.
Instead of relying on static lists or partial profiles, enrichment tools powered by AI can automatically fill in the blanks, validate details, and surface critical insights, all in real-time.
Whether it’s identifying decision-makers, uncovering buying signals, or syncing firmographic data across your stack, enriched data means your team spends less time guessing and more time closing.
In this article, I’ll break down what AI data enrichment really is, how it works, why it matters for modern GTM teams, and how to implement it right, plus the top tools to consider as a bonus.
Let’s dive into the tech that turns raw contacts into ready-to-convert leads!
What is AI data enrichment, and how does it work?
AI data enrichment is the process of enhancing raw or incomplete data by automatically adding relevant, real-time information using artificial intelligence.
Instead of relying on manual research or third-party lists that go stale quickly, AI enrichment tools connect to multiple data sources (like public databases, proprietary datasets, CRM records, and third-party APIs) to fill in the blanks accurately and at scale.
Here’s how it typically works:
AI models ingest your existing records, such as lead lists, contact info, or firmographic data.
They cross-reference these entries against real-time sources to verify, supplement, or correct missing information (like job titles, buying signals, industry, tech stack, recent funding, etc.).
Machine learning ensures the enrichment becomes smarter over time, prioritizing high-confidence matches and eliminating duplicates or bad data automatically.
The result?
A living, breathing database that’s always up-to-date and immediately actionable without hours of manual effort.
And because the enrichment happens automatically, your sales and marketing teams stay focused on engaging the right people, not cleaning up endless rows of spreadsheets.
Next, let’s look at why this matters and how AI takes enrichment to the next level.
What are the benefits of using AI for data enrichment?
The main benefits of using AI for data enrichment include real-time precision and freshness, hyper-targeted segmentation and personalization, and the fact that you'll save time from manually researching prospects.
AI data enrichment isn’t just about speed.
It’s about turning scattered, outdated, or incomplete data into revenue-ready sales intelligence that helps GTM teams move faster, personalize smarter, and close more deals.
Here's what that unlocks:
1. Real-time precision and data freshness
AI enrichment tools continuously sync your data with live external sources, pulling in updates on titles, funding rounds, tech stack changes, and more.
This means your team is always working with the most accurate and up-to-date information available, not lists that went stale three months ago.
2. Hyper-targeted segmentation and personalization
With richer data, such as technographics, job roles, or recent buying intent, you can segment leads far more effectively and personalize your messaging at scale.
AI makes it possible to go beyond surface-level traits and speak to what actually matters to each account or buyer.
3. Automation that reduces manual effort
Traditionally, enrichment meant hours of spreadsheet work or chasing leads on LinkedIn.
With AI, that legwork is automated.
New leads are enriched as they enter your system, and existing records are continuously updated, freeing up time for reps to focus on outreach and deal progression.
4. Better conversions across the funnel
When you’re reaching out to the right people, with relevant context, at the right moment, responses naturally go up.
AI-enriched data directly impacts conversion rates by powering smarter targeting and reducing the guesswork in your GTM motion.
5. Improved CRM hygiene and data confidence
Bad data costs you, resulting in lost deals, wasted outreach, and reporting errors.
AI helps keep your systems clean by deduplicating records, flagging inconsistencies, and enriching missing fields in real-time.
This means there’s no more bloated CRMs full of half-complete or outdated profiles.
6. Competitive edge at every touchpoint
Teams that adopt AI enrichment don’t just move faster - they move smarter.
With richer signals and deeper context behind every lead, your GTM strategy becomes more predictive, more personalized, and ultimately more effective than those still relying on static, manual data.
What are the different types of data enrichment?
There are several different kinds of data enrichment, each serving a distinct purpose and playing its role in your marketing strategy.
Here are the key types you’ll see, which are often combined in modern enrichment platforms:
Firmographic enrichment - Adds business-level data like industry, company size, revenue, location, and growth stage. This is essential for B2B segmentation and ideal customer profiling.
Technographic enrichment - Surfaces what tools or platforms a company is using, like CRM, cloud infrastructure, ad tech, or sales software. This is useful for competitive targeting, integrations, and outbound personalization.
Demographic enrichment - Fills in individual-level details like job title, seniority, department, and social profiles, so you’re not just targeting “a company,” but the right person inside it.
Behavioral and intent enrichment - Brings in real-time buying signals, such as recent site visits, content downloads, hiring trends, or spikes in keyword research. This is where AI really shines, surfacing leads that are actually in-market.
Contact enrichment - Adds missing details like email addresses, phone numbers, or LinkedIn profiles for outreach-ready records, often with confidence scoring and automatic syncing to your CRM or sales tools.
Together, these enrichment types give you a 360-degree view of who your leads are, what they’re doing, and how best to engage them without chasing static lists or scraping the internet manually.
What are the different AI data enrichment techniques?
AI data enrichment is not just about appending missing fields.
It's about transforming raw data into actionable intelligence through a variety of sophisticated techniques.
Here's a quick overview of the most impactful methods:
1. External data integration
This technique means adding useful information to your existing customer or lead data by pulling data from outside sources.
For example, AI can look at public records, social media, company websites, or quickly sift through databases like Coldly to find extra details, like someone’s job title, the size of their company, or what tools they use.
By combining this external data with what you already have, you get a more complete picture of who your buyers are and how to reach them more effectively.
2. Data cleansing and deduplication
This technique helps clean up messy or incorrect data in your CRM or other lead databases.
AI looks for things like typos, missing fields, outdated info, or records that show up more than once.
For example, if the same lead is entered twice in your CRM with slightly different names or email addresses, AI can spot it and fix it automatically.
As a result, you get clean, accurate, and reliable data that your team can actually trust.
3. Semantic enrichment
This technique is all about giving your data more meaning.
AI looks at what’s already in your database and connects it to related topics or categories using public knowledge sources.
For example, if a contact’s job title is “VP of Growth,” semantic enrichment might also tag them as part of the marketing department or leadership team.
This helps your team understand context and improves targeting, allowing you to spot key stakeholders and related accounts more easily.
4. Geospatial enrichment
This technique adds location data, like the exact coordinates or region a person or company is based in.
So instead of just knowing someone is in “New York,” AI might add their zip code or nearby landmarks.
This is especially useful for planning local mail campaigns, assigning territories, or optimizing deliveries.
5. Sentiment analysis
AI reads through text like social media posts, reviews, or survey answers, and figures out the tone.
Is someone happy, frustrated, or excited?
Sentiment analysis helps companies understand how people feel about their brand, product, or service without having to read through every single comment.
6. Multimedia content analysis
Multimedia content analysis helps you understand and organize large volumes of visual content, such as images and videos, without manual review.
AI can scan a photo or video and figure out what’s in it, such as objects, people, logos, or even emotions.
It also pulls out useful details (like location tags or timestamps), helping companies organize and understand their visual content more easily.
This way, if your brand is mentioned in a product unboxing video or someone posts a photo with your logo, AI can detect that automatically.
It’s essential for brand monitoring, content moderation, and making smarter use of user-generated content.
7. Predictive analytics
AI looks at past behavior and trends to guess what might happen next.
For example, if a lead has visited your pricing page three times and opened your last two emails, predictive analytics might flag them as likely to buy soon.
This helps you prioritize outreach and take action at the right time.
8. Behavioral data enrichment
This technique tracks how people interact with your website, emails, or product, and then adds those insights to their profile.
This includes things like: which pages they visited, how long they stayed, and what they clicked on.
It gives you a clearer view of what someone is interested in right now, so you can follow up in a smarter, more relevant way.
Platforms like Warmly excel in this, leveraging sophisticated AI models to screen for and identify intent signals in real-time, combining internal data (interactions with your website) with external data from 10+ data enrichment providers.
As a result, you can:
Identify high-intent leads while their interest is at its peak.
Create detailed lead segments and target audiences.
Craft hyper-personalized outreach strategies leveraging the data you have on each lead.
How can you incorporate AI data enrichment into your sales process?
When it comes to incorporating AI data enrichment, the good news is that you don’t need to reinvent your sales process to benefit from it.
You just need to plug it into the right places.
Here’s how to make it work, step by step:
Step #1: Start with your CRM or lead source
Before you do anything else, connect your AI data enrichment tool to wherever your leads first enter the system, which is usually your CRM (like HubSpot, Salesforce, or Pipedrive) or your lead capture forms (like website forms, demo requests, or webinar signups).
Once connected, the AI can automatically enrich each new contact as they come in, adding details like job title, company size, LinkedIn profile, industry, and more.
Say someone fills out your demo request form and only gives you their name and work email.
Without enrichment, your team has very little to go on.
But with AI data enrichment connected to your CRM:
The system identifies that the person is Julia Wong, Head of Product at a Series B fintech company using Salesforce and Stripe.
It automatically adds her LinkedIn profile, company headcount, funding stage, and a few recent company updates.
As a result, your rep now knows who she is, what her company does, and can tailor the pitch accordingly.
Step #2: Clean and enrich existing records
Your CRM might already be packed with leads, but if that data is old, incomplete, or duplicated, it’s holding your team back.
This step is about using AI to automatically:
Fill in missing fields (like job titles or industries).
Update stale info (like a contact who changed roles or companies).
Merge duplicates so you’re not contacting the same person twice.
It’s the fastest way to turn your database into a useful, up-to-date sales asset.
For example, imagine your rep is about to follow up with Sophie D., but her CRM record hasn’t been touched in over a year.
With AI enrichment:
You discover she’s no longer at her old company - she’s now VP of Marketing at a startup that just raised a Series A.
The AI updates her profile, syncs her new LinkedIn, and tags her new company with relevant firmographics.
Now your rep knows Sophie’s a high-value target and not a dead-end lead.
Step #3: Use enriched data to segment smarter
Once your data is enriched, you can go far beyond basic filtering.
Instead of blasting generic messages to everyone in your CRM, AI-enriched fields let you create precise, dynamic segments based on:
Company size.
Industry.
Funding stage.
Tech stack.
Buying signals.
Job seniority, and more.
For instance, Warmly’s Marketing Ops agents work 24/7 on building targeted audience segments, unearthing granular insights and traits that go beyond just firmographics.
This way, you get more accurate, always updated ICP and dynamic, well-targeted audiences.
The outcome?
Smarter segments = more relevant messaging = better results.
Step #4: Prioritize leads based on real signals
Not all leads are created equal, and AI enrichment helps you spot the ones most likely to convert.
By layering in signals like job changes, recent funding, hiring spikes, or product research behavior, AI can help you automatically score and prioritize leads based on who’s showing real buying intent.
This means your reps stop wasting time on cold or low-fit prospects and focus on the ones that are actually worth it.
For example, if you’ve got 300 leads sitting in your CRM, AI enrichment can highlight that:
5 of them work at companies that just raised a Series B,
2 have just installed a competitive tool your product integrates with,
And 1 rep just switched into a new buying role last week.
And voila - that’s your shortlist! That’s who your team should talk to today.
Step #5: Personalize your outreach at scale
Enriched data turns generic outreach into relevant, high-converting messages without making your team write everything from scratch.
With details like job title, industry, tools used, recent activity, or even company news, your reps (or your AI assistant) can personalize emails, InMails, and call openers automatically.
You still get the scale, but now it feels like 1:1 communication.
This means that instead of sending:
“Hi Jamie, I’d love to connect and share what we’re building.”
You send:
“Hi Jamie, I saw you recently joined Acme as Head of GTM. Since you’re using HubSpot + Gong, I thought I’d share how we help RevOps teams using that same stack increase pipeline coverage.”
With tools like Warmly, you can put the entire outreach process on autopilot:
The platform identifies high-intent leads and enriches them with in-depth B2B and intent data.
Its AI SDR agents automatically pick up those leads and include them in email or LinkedIn sequences, using the info they have on each lead to tailor messaging accordingly.
The result?
Higher open, reply, and conversion rates across channels.
Step #6: Sync across your stack
It’s not enough to enrich your data. You need to make sure everyone in your GTM motion is seeing and using it.
That means syncing enriched profiles across your CRM, email tool, sales engagement platform, and analytics dashboards.
When done right, every system pulls from the same up-to-date, enriched source of truth and your reps, marketers, and ops folks stay aligned.
Step #7: Review and refine regularly
AI data enrichment isn’t a “set it and forget it” solution.
To get the best results, you’ll want to monitor performance, audit your data health, and adjust your enrichment strategy as your sales process evolves.
Look at which enriched fields actually help your team convert.
Is the funding stage more useful than the tech stack? Are job titles getting messy again?
Refining over time keeps your data - and your strategy - sharp.
For example, if you notice your reps rarely use the “industry” field but rely heavily on job seniority and recent hiring signals, that’s your cue to update your scoring model and prioritize the fields that actually drive results.
What are some of the challenges in using AI for data enrichment?
While AI data enrichment can certainly transform your sales process, like any powerful tool, it comes with its own set of challenges.
Let’s look at some of the most common issues teams face (and how to overcome them):
1. Data quality in, data quality out
AI can enrich your data, but it can’t work miracles if your starting point is garbage.
If your CRM is full of outdated, mislabelled, or inconsistent records, enrichment tools may struggle to match or append the right information.
💡Tip: Start with a basic data clean-up before layering in AI. The better your foundation, the better the enrichment results.
2. Integration complexity
Many enrichment tools promise plug-and-play, but syncing them across multiple platforms (CRM, sales engagement, analytics) can get messy, especially if your systems are siloed or custom-configured.
💡Tip: Choose tools with native integrations for your core stack, and work closely with RevOps or your CRM admin to set up clean, bi-directional syncs.
3. Over-enrichment or irrelevant fields
Just because you can add 30 new fields to every contact doesn’t mean you should.
Too much data clutters your workflows, confuses reps, and can even hurt personalization if the info isn’t actually helpful.
💡Tip: Be selective. Focus on enriching fields your team will use, such as job role, seniority, and buying signals, not vanity metrics.
4. Privacy and compliance risks
Pulling in external data, especially personal or behavioral data, can introduce privacy issues.
You’ll need to make sure you’re compliant with GDPR, CCPA, and other local regulations around data usage and transparency.
💡Tip: Use enrichment providers who are transparent about their data sources and have strong privacy practices in place. Always give users a way to opt out if required.
5. AI blind spots or mismatches
No system, including AI, is perfect.
Sometimes enrichment tools misidentify people, attach the wrong company data, or pull in outdated information.
If you rely too heavily on enrichment without validation, your reps may act on bad signals.
💡Tip: Set confidence thresholds for enriched fields. Some tools let you prioritize “high certainty” matches or even flag data for manual review before syncing to your CRM.
Next steps: Get smarter data to do sharper outreach with Warmly
In today’s sales landscape, it’s not about how much data you have.
It’s about how accurate, timely, and actionable that data is, and that’s where AI-powered enrichment makes all the difference.
By automating enrichment, syncing it across your tools, and embedding it into your workflows, you can unlock faster research, better segmentation, and truly personalized outreach at scale.
But the key isn’t just adding data - it’s knowing what to do with it.
Warmly helps GTM teams not only enrich leads with real-time firmographic and behavioral signals, but also act on them, automatically surfacing the hottest accounts, adapting outreach, and helping reps prioritize with confidence.
Want to see it in action? Book a demo with Warmly and discover how smarter data can fuel more efficient selling.
9 Best Data Enrichment Tools in 2024 - Explore the top platforms helping B2B teams clean, complete, and supercharge their databases with smarter, more actionable data.
What is the best way to ai data enrichment: what is it & do it [2026]?
The best approach depends on your specific situation. Follow the step-by-step guide above for proven methods. Key success factors include proper setup, consistent execution, and measuring results.
How long does it take to ai data enrichment: what is it & do it [2026]?
Timeline varies based on complexity and resources. Simple implementations take days, while comprehensive strategies may take weeks to fully execute. Start with quick wins outlined above.
What tools do I need to ai data enrichment: what is it & do it [2026]?
Essential tools are covered in the guide above. For B2B sales teams, Warmly can help by identifying website visitors and providing intent signals to prioritize your efforts.
What are common mistakes when trying to ai data enrichment: what is it & do it [2026]?
Common pitfalls include moving too fast without proper setup, not measuring results, and using outdated tactics. Follow the best practices above to avoid these issues.
Can I automate this process?
Many aspects can be automated with the right tools. Warmly offers automation for website visitor identification and engagement. See the tools section above for automation options.
8 Account-Based Marketing Examples To Take Inspiration From In 2026
Time to read
Chris Miller
Account-based marketing (ABM) is a highly specific strategy that completely transforms how B2B teams approach growth.
Instead of casting a wide net, ABM aligns marketing and sales to go deep on high-value accounts, using personalization, timing, and intent signals to drive real results.
And in 2026, ABM is smarter, more automated, and more connected than ever.
But let’s be honest: generic templates and surface-level tactics won’t cut it.
If you want your ABM strategy to actually move pipeline, you need ideas rooted in reality.
In this article, we’re breaking down 8 of the most inspiring, creative, and effective ABM examples, from hyper-personalized outreach to multi-channel plays that actually convert.
These will help you think bigger, test smarter, and sell more effectively, regardless of whether you’re refining your current ABM motion or building one from scratch.
What are some of the best practices of ABM in 2026?
ABM in 2026 is more intelligent, integrated, and intent-driven than ever.
As teams lean into AI, better data, and tighter sales-marketing collaboration, ABM has shifted from being a niche strategy to a foundational B2B motion.
But getting it right still requires more than just tech - it takes focus, coordination, and experimentation.
Here are the best practices leading teams are using today:
1. Hyper-personalization at scale
Today, ABM isn’t just about personalization - it’s about relevance at scale.
Leading teams are using AI and real-time intent data to create content and outreach that feels handcrafted, even when it’s automated.
That might mean referencing a prospect’s latest funding round, tailoring ads to industry-specific challenges, or adapting messaging based on where an account sits in the buying cycle.
The key isn’t just adding a name, it’s showing you understand the actual context and each individual lead’s current buying stage and other relevant circumstances.
According to Salesforce, 73% of B2B buyers expect vendors to personalize their engagement to their needs, and that number is probably going to increase, so keep that in mind.
Tools like Warmly make this kind of personalization scalable.
With AI SDRs that trigger automated, context-aware sequences across email and LinkedIn based on live engagement signals and website behavior, teams can deliver the right message to the right stakeholder at the right time.
Whether it’s finding key contacts or orchestrating follow-ups, Warmly helps revenue teams build 1:1 relationships across hundreds of accounts without adding headcount.
2. Using AI and predictive insights to guide targeting
With so much data available, the best ABM programs use AI to filter noise from signals.
Predictive models surface which accounts are warming up, which are showing in-market signals, and which should be deprioritized.
Instead of relying on guesswork, teams are scoring accounts in real time using a mix of firmographics, CRM activity, website behavior, and buying signals, so reps can strike when interest is peaking.
This is where platforms like Warmly give teams a serious edge.
Warmly monitors first, second, and third-party signals at the person level, not just the account level, pulling in everything from product usage and web behavior to social activity and job changes.
As a result, teams can build intelligent segments based on real-time signals and launch precise, personalized sequences or ad campaigns the moment actual intent is detected.
It’s ABM targeting driven by live buyer context, not assumptions.
3. Aligning marketing and sales around shared account goals
In 2026, ABM is no longer a marketing-led initiative - it’s a go-to-market strategy.
That means sales and marketing don’t just "collaborate".
Instead, they plan together, build together, and measure success using shared KPIs.
Teams that win with ABM often have unified account plans, regular alignment meetings, and tools that sync campaigns across functions.
When both teams are working off the same playbook, outreach feels cohesive, and deal cycles speed up.
4. Going multi-channel, but staying coordinated
No single channel wins deals.
The most effective ABM programs engage accounts across email, LinkedIn, targeted ads, virtual events, landing pages, and more, but with one consistent narrative.
The key thing that sets high-performing teams apart is their orchestration - using tools that sequence touches across platforms while ensuring every stakeholder sees a story that makes sense.
Done right, this builds familiarity, increases trust, and helps accounts self-navigate deeper into the funnel.
That’s the exact role of Warmly’s AI SDRs, which can engage accounts on all channels that matter, including:
Your website via a smart AI Chatbot that engages and qualifies leads and books meetings.
LinkedIn through personalized DMs and connection requests to engage surging accounts while they’re still interested.
Email drip campaigns that nurture accounts and lead them down the ABM funnel.
5. Measuring what matters (and refining fast)
Modern ABM isn’t set-it-and-forget-it.
The best programs track not just clicks and opens, but pipeline influence, sales velocity, and account engagement over time.
Leading teams today are using account-based dashboards to identify what’s working and where things stall, then refining campaigns accordingly.
It’s not about proving ROI with vanity metrics; it’s about building a funnel that continuously learns and improves.
Think of it as agile marketing, but account-first.
What does a best-in-class ABM strategy look like in 2026?
In 2026, a best-in-class ABM strategy is more than just a campaign - it’s become an entire system that covers all the essential parts of the funnel.
This means that the strongest B2B teams aren’t just “running ABM”.
They’ve built always-on, cross-functional growth engines that embed account-based thinking across their entire revenue strategy.
Here’s what I mean by this:
1. ABM is embedded into the full go-to-market motion
At leading companies, ABM isn’t a side project - it’s a core growth strategy.
From demand gen to outbound, sales enablement to customer marketing, every team is aligned around a unified list of high-value accounts.
Rather than splitting resources between lead gen and ABM, the entire GTM org is structured to win and expand within a focused TAM.
Metrics, headcount, and budget follow suit for optimal results.
2. Segmentation is intelligent, dynamic, and real-time
Top teams aren’t relying on quarterly account refreshes or static firmographic filters.
Their segmentation engines are fluid, driven by real-time data, behavioral signals, and evolving buying committee dynamics.
For example, if a VP of Ops at a target account visits your pricing page and a second stakeholder starts a product chat the next day, that account automatically gets promoted into a new play.
This is where Warmly shines: it lets teams track intent signals and web activity at the individual level, across 1st, 2nd, and 3rd party sources.
With that visibility, you can create dynamic audiences that update automatically and trigger personalized sequences or outbound plays without delay using the Orchestrator or agentic AI SDRs.
3. Personalization is programmatic, but human
Best-in-class ABM doesn’t mean handcrafted messages for every touch. It means building systems that feel personalized without being slow.
Teams use AI to generate context-aware outreach at scale, but pair that with human insight where it matters (e.g., for high-tier accounts or C-level personas).
The result is outreach that respects the buyer’s context while protecting your team’s bandwidth.
Tools like Warmly’s AI SDRs help strike this balance by automating thoughtful messages based on live activity and contact-level behavior, so reps can focus on strategic conversations instead of manual follow-ups.
4. Revenue teams operate as one account team
The old marketing-to-sales handoff doesn’t exist in a best-in-class ABM motion.
Instead, everyone touching the account, such as BDRs, AEs, CSMs, marketers, etc. works from a shared source of truth.
They know who’s engaged, who’s cold, and what’s been tried.
Tools are integrated. Roles are clearly defined. And there’s total clarity on ownership across every stage of the account lifecycle.
5. Orchestration is proactive, not reactive
Instead of reacting to inbound interest or waiting for MQLs to convert, top ABM teams proactively orchestrate sequences, ads, meetings, and outreach based on buying stage and stakeholder activity.
This orchestration happens across multiple touchpoints, including email, LinkedIn, chat, and events and unfolds in a way that feels cohesive and timely.
Warmly’s Orchestrator makes this possible at scale, automatically triggering next-best actions - such as adding accounts to targeted LinkedIn ads campaigns, or firing personalized LinkedIn and email sequences - based on behavior, contact role, or engagement score.
So instead of having reps babysit sequences, the system works behind the scenes to keep momentum going.
8 account-based marketing examples that you can learn from
So what does all of this look like when it’s put into action?
Now that we’ve unpacked what makes a best-in-class ABM strategy tick in 2026, let’s shift from theory to practice.
The following examples showcase real-world ABM campaigns, each with its own unique playbook, from hyper-personalized outreach to orchestrated multi-channel engagement.
These aren’t just flashy stunts. They’re strategic, measurable efforts that drove pipeline, created real momentum, and sparked deals with high-value accounts.
Whether you're looking for creative inspiration, tactical ideas, or proof that ABM really works, these campaigns offer a front-row seat to what’s possible.
Let’s break them down.
1. Turning anonymous website traffic into pipeline: How Arc used Warmly to boost funnel conversion and 3x ROI
Challenge: Arc, a modern banking platform for venture-backed startups, operates in a fiercely competitive space where attention is scarce and timing is everything.
For Basile Senesi, Arc’s CRO, the go-to-market strategy hinges on identifying high-intent buyers fast and engaging them before competitors do.
But like many early-stage teams, Arc faced a visibility gap.
Thousands of weekly site visitors were flowing through their funnel, but sales had no idea who they were, what they were looking at, or when to reach out.
Attribution was fuzzy. Outreach was reactive. And reps were losing valuable time chasing the wrong leads.
Solution: That changed when Arc implemented Warmly.
In under 30 days, Arc used Warmly to:
Identify site visitors in real-time.
Drop insights directly into Salesforce, Outreach, and Slack (where SDRs already lived).
Automatically add high-fit visitors to personalized sequences.
Alert reps the moment a key account hits a pricing page or demo request form.
Results:
Arc unlocked a 10–15% lift in funnel conversion.
Accelerated sales cycles.
Added 1-3 extra customers per month without expanding headcount.
Got a 200% return on investment within 6 months, with a projected 3–5X ROI by year’s end.
This is a prime example of ABM done right: high-intent targeting, signal-based outreach, and operational efficiency that scales with the team, not against it.
2. Skipping the funnel: How StraightIn used ABM strategies to fast-track high-value accounts and close $10K in 2 weeks
Challenge: For LinkedIn marketing agency StraightIn, ABM was a necessity.
With thousands of monthly site visitors coming in from paid and organic campaigns, the challenge wasn’t generating traffic.
It was figuring out which accounts were worth pursuing and how to engage them in a relevant, timely way.
Their traditional demand gen approach relied on top-of-funnel marketing and broad outreach.
But like many B2B teams, they realized that ABM was a smarter path: identifying high-fit accounts early, tailoring outreach, and orchestrating touchpoints across channels.
Solution: That’s where Warmly came in.
Using Warmly’s AI Orchestrator, StraightIn turned anonymous website traffic into real-time account insights.
They could now:
Identify key stakeholders from in-market accounts the moment they hit their site.
Segment visitors by job title, company size, and behavior, such as time on pricing pages or industry-specific blog posts.
Trigger personalized outreach across LinkedIn and email tailored to each account’s interest and buying stage.
With these ABM workflows in place, they skipped the spray-and-pray and went straight to engagement.
Leads from Warmly were automatically dropped into Salesloft and Salesflow campaigns, giving reps a warm context for follow-up and significantly increasing email open, click, and reply rates.
Even better, the team used Warmly’s first-party intent data to cut LinkedIn Ad spend while improving performance by building retargeting audiences based solely on high-fit, already-engaged accounts.
Results:
Within two weeks, StraightIn closed two deals totalling $10K, proving that an ABM motion, when powered by real-time engagement signals and automated outreach, doesn’t need months to start delivering ROI.
3. From anonymous clicks to high-intent conversations: How Caddis Systems drove a 500% conversion lift in 7 days
Challenge: For Caddis Systems, a niche manufacturing software provider with a lean, 3-person sales team, converting website visitors into pipeline wasn’t just a priority - it was a pain point.
Despite running AdWords campaigns and cold outreach through an agency, their inbound motion lacked one crucial piece: visibility into which accounts were actually showing intent.
In short, their ABM motion was stalled before it even started.
Solution: Caddis Systems decided to implement Warmly.
In just one week, Caddis used Warmly to turn previously anonymous site traffic into targeted, real-time engagement with their ideal accounts, driving a 5x increase in website conversions and proving that even small teams can win big with smart ABM execution.
Here’s how it worked:
Account identification - Warmly deanonymized site traffic and surfaced contact- and company-level insights in real-time, instantly showing Caddis which visitors matched their ICP.
Live, high-intent engagement - Reps received Slack alerts when key accounts landed on high-value pages (like pricing or product docs), and jumped into conversations right then and there using Warmly’s Live Video Chat.
Segmentation and prioritization - With AI Orchestrator, Caddis built dynamic segments to filter and route visitors based on source (e.g. ad campaigns) and behavioral signals, automating outreach only to high-fit accounts.
Multi-channel orchestration - Leads from ads, email, and chat were nurtured across multiple touchpoints, creating a fluid, ABM-style journey personalized to each account’s engagement level and activity.
This way, instead of waiting for prospects to fill out a form or show blatant intent, Caddis built a proactive ABM system that met accounts where they were, based on real-time behavior, not guesswork.
Results:
500% increase in website conversions.
Multiple qualified meetings from live chat.
4-5x ROI in under 7 days.
Warmly didn’t just improve lead gen, it gave Caddis an actual account-based infrastructure, enabling their small team to behave like a much larger org.
Their ABM playbook became automated, precise, and fast, just how modern GTM teams need it in 2026.
4. Connecting campaigns to pipeline: How Premikati uses Warmly’s Orchestrator and chatbot to scale ABM with precision
Challenge: When Premikati - a procurement and finance operations consulting company - launched multi-channel campaigns across LinkedIn and paid search, they saw promising traffic numbers but no clear insight into who was engaging or whether it was leading to pipeline.
That made it nearly impossible to prove ROI, prioritize spend, or follow up with high-intent accounts.
In short: Premikati was doing everything right, but couldn’t connect the dots.
Solution: By layering Warmly’s Orchestrator and AI chatbot into their go-to-market motion, Premikati transformed those disconnected clicks into a fully orchestrated ABM system - one where visitor identification, lead scoring, segmentation, and follow-up happen automatically.
Here’s what made it work:
Real-time UTM tracking and deanonymization helped its team identify which campaigns were actually driving the right traffic, and from which channels.
Warmly’s Orchestrator segmented site visitors by campaign, behavior, and buying stage, automatically enrolling them into relevant LinkedIn and email sequences.
Instead of stopping at one contact, Warmly surfaced other key stakeholders from the same account, allowing Premikati to multi-thread and personalize follow-up based on roles and activity.
Warmly’s AI chatbot handled initial engagement, qualifying inbound leads, and even booking meetings autonomously - something their previous chat tools never managed.
The result is an ABM strategy that proactively moves accounts through the funnel, using real-time signals and intent data to determine who to engage, how to follow up, and when to loop in reps.
And it’s working.
Warmly gave Premikati a clear view of ROI per channel, automated the middle of the funnel, and empowered reps to have more relevant, better-timed conversations.
Results:
More booked meetings.
More efficient workflows.
A marketing team that can now scale with confidence.
That’s a prime example of modern ABM: tightly orchestrated, dynamically segmented, and built to scale.
5. Voice-led ABM at scale: How Connectteam used AI SDRs to increase engagement 5x and cut no-shows by 73%
Connectteam, a fast-scaling employee management platform serving frontline-heavy industries like construction, retail, and healthcare, had one core ABM challenge: how to personalize outreach across multiple verticals without overextending their SDR team.
With 120,000 phone calls handled monthly, a lean SDR org, and rising lead volumes, Connecteam’s ops leaders knew they needed to evolve their approach.
Email and SMS weren’t cutting it, and cold outreach lacked the contextual relevance required for meaningful engagement.
Plus, a 75% meeting no-show rate was throttling pipeline momentum.
Solution: Deploying Julian, an AI-powered SDR from 11x built to handle voice-based outreach at ABM scale.
Here’s how Connecteam brought ABM into the voice channel and made it work:
Segmented, vertical-specific outreach - 11x embedded with Connecteam to tailor Julian’s outreach by industry, aligning messaging for retail, healthcare, and construction personas without requiring manual SDR input.
Intent-based follow-ups - Julian reached out to leads based on behavior and funnel stage, reactivating old or cold opportunities that the sales team didn’t have bandwidth to pursue.
Meeting scheduling and confirmation - Julian didn’t just call. He booked meetings, handled confirmations, and ran proactive follow-up sequences, slashing no-show rates by over 70%.
Results:
$450K+ saved annually on SDR salaries.
73% drop in no-shows.
$30K monthly revenue lift per SDR.
For ABM teams looking to scale intelligently, Connecteam’s approach shows what’s possible when you blend vertical-specific messaging, AI-led orchestration, and real-time follow-up without growing headcount.
It’s a bold reminder that ABM isn’t just about who you target - but how fast and personal you engage once intent is shown.
6. Scaling smart: How Everstage 2.5x’d sales engagement and doubled ABM match rates
Challenge: Despite investing in multi-channel paid campaigns across LinkedIn and Google, Everstage - a sales commission platform- couldn’t track which accounts were engaging, what content was driving interest, or whether they were targeting the right prospects at the right time.
Its tech stack, spread across Google Analytics, HubSpot, and spreadsheets, offered fragmented insights and poor account match rates.
And when outreach did happen, it often lacked the precision needed to drive meaningful engagement.
Solution: The company rebuilt its ABM engine with Factors.ai at the center, and everything changed.
Here’s how Everstage transformed its account-based strategy:
Centralized attribution and funnel analytics - With a unified view of their marketing funnel, Everstage could now see exactly which campaigns, pages, and segments were underperforming and fix them fast.
Intent-powered segmentation - By layering in behavioral signals from G2, CRM, LinkedIn, and web data, Everstage was able to score and prioritize ABM accounts based on fit and buying intent.
Outbound-ready insights - Sales and BDR teams gained account-level timelines that showed who engaged, when, and where, making personalized, timely outreach both easier and more effective.
Results:
2.5x increase in sales email engagement.
Higher ROI per campaign.
More efficient outbound workflows.
Smarter targeting with reduced waste.
This is modern ABM in action: clean attribution, targeted engagement, and sales-marketing collaboration built around shared data.
7. From inefficient targeting to 40% pipeline growth: How Coalfire rebuilt its ABM engine from the ground up
Challenge: Coalfire, a cybersecurity advisory firm, had the fundamentals of account-based marketing in place but struggled with execution.
Their campaigns were reaching the wrong accounts, lead quality was inconsistent, and fragmented attribution made it nearly impossible to connect effort with outcome.
In short, their ABM investments weren’t translating into revenue.
Solution: To fix that, Coalfire turned to Demandbase, using its AI-driven platform to unify targeting, improve account scoring, and drive smarter, data-backed decisions across the funnel.
Here’s how they did it:
Precision targeting powered by intent data - With real-time insights and predictive analytics, Coalfire redefined its ICP and focused outreach only on accounts showing relevant behavior, eliminating wasted ad spend and improving account engagement by 30%.
Smarter segmentation and prioritization - Coalfire could now rank and route accounts based on interest, fit, and stage, so sales teams only pursued prospects that were both qualified and ready.
Clear attribution - With campaign-level visibility, Coalfire could see exactly which programs were driving results, and which weren’t, enabling continuous optimization.
What followed was a complete transformation of Coalfire’s ABM motion, delivering measurable gains in pipeline, engagement, and sales alignment.
Results:
40% increase in marketing-generated pipeline.
25% higher lead-to-opportunity conversion rates.
12% shorter sales cycle.
45% lift in conversions for targeted campaigns.
8. Precision over volume: How PitchBook cut CPC by 38% and boosted deal size with targeted ABM ads
Challenge: PitchBook, a leading financial data and research firm, knew that traditional digital campaigns weren’t enough to move the needle with high-value accounts.
It needed a way to drive demand, accelerate deal velocity, and better support their sales and customer success teams without wasting spend on low-fit prospects.
That’s where account-based advertising (ABA) came into play.
Solution: By partnering with RollWorks, PitchBook refined their ABM strategy from broad outreach to precision targeting, focusing their efforts on the highest-fit A and B-tier accounts across their funnel.
Here’s what worked:
Hyper-personalized advertising - Instead of generic display ads, PitchBook launched ads tailored by account name, product relevance, and lifecycle stage, creating relevance from the very first impression.
Smart account prioritization - They streamlined from 10,000+ accounts to a tightly focused list of high-fit targets, cutting cost per click by 38% in just two weeks (from $29 to $18).
Ad-informed sales timing - Marketing delayed sales handoff for cold accounts, ensuring reps reached out only after a full 30-day engagement window. This built brand familiarity and improved response rates.
Results:
14.9% faster sales cycle.
24.3% higher win rate on influenced deals.
36.9% higher average deal size.
31.8% of closed-won pipeline influenced by ads.
This campaign proves what effective ABM looks like in 2026: fewer accounts, deeper personalization, smarter handoffs, and tighter marketing-sales alignment.
It’s not about more leads - it’s about the right accounts, engaged the right way.
What does the future look like for ABM?
ABM of the future will be more than just hyper-personalized.
It will also be predictive, autonomous, and orchestrated across every step of the buyer’s journey.
As GTM teams face pressure to do more with less, AI is becoming the engine behind next-gen ABM strategies.
Instead of just identifying who to target, AI can now help decide when, where, and how to engage, and in many cases, it can execute that engagement autonomously.
I’m not just talking about scoring leads or generating insights.
AI in ABM is now being used to:
Auto-prioritize accounts based on real-time intent signals from websites, email, chat, and third-party research intent.
Segment audiences dynamically based on fit and behavior, not static ICP rules.
Generate personalized messaging across email, LinkedIn, SMS, and ads at scale.
Automate outreach through AI SDRs, especially for warm but mid-priority leads that humans often can’t reach quickly enough.
Trigger full-funnel workflows, like warming up accounts with ad impressions, chatbot touches, and follow-up sequences, all without manual handoffs.
This is the foundation of what we call ABM 2.0 at Warmly.
Let’s say a VP of Operations at a target account visits your pricing page, interacts with your chatbot, and opens an email.
Well, Warmly picks that up in real-time.
Instead of waiting for a rep to manually follow up, that contact is routed into a tailored AI SDR campaign powered by our integration with 11x.
From there, autonomous AI SDR agents launch a personalized outreach sequence across email, LinkedIn, or even SMS, while your human reps focus on the highest-intent conversations.
In practice, that means:
Warm leads get AI-powered outreach immediately, without adding SDR headcount.
High-intent leads get flagged for human follow-up, with full context at their fingertips.
Your pipeline grows passively, even when your team is offline.
This kind of AI-led orchestration flips the traditional ABM model on its head.
Instead of running rigid campaigns in quarterly bursts, you're now operating a 24/7, signal-based system that adapts in real time and meets buyers wherever they are in their journey.
Looking ahead, we’ll see even more intelligent use of AI in ABM:
Fully autonomous research agents that enrich leads and accounts before any contact is made.
AI-generated playbooks that build dynamic and personalized cadences based on buyer behavior patterns.
Predictive engagement timing, where AI not only suggests who to reach out to, but when they’re most likely to respond, etc.
The bottom line?
The future of ABM is no longer limited to identifying high-value accounts. It’s about orchestrating the entire buyer experience, powered by data and executed (in part) by intelligent automation.
And if that sounds complex, it doesn’t have to be.
With platforms like Warmly, AI isn't replacing your team.
It’s amplifying their reach, optimizing their time, and making sure no opportunity slips through the cracks.
Next steps: From inspiration to execution
Account-based marketing in 2026 isn’t about doing more - it’s about doing smarter.
As these examples show, the most successful ABM strategies aren’t necessarily the flashiest. They’re simply the most intentional.
They combine real-time data, smart segmentation, and personalized engagement to connect with the right people, in the right way, at the right time.
And increasingly, they’re powered by AI, from autonomous SDRs running outreach in the background to signal-based lead scoring that ensures reps never miss a high-intent opportunity.
Whether you’re refining an existing ABM motion or building one from the ground up, the takeaway is the same: ABM today is about orchestration, not just outreach.
And that orchestration starts with the right tools.
If you’re ready to turn anonymous web traffic into warm pipeline, run personalized outreach at scale, and build a truly adaptive ABM system, Warmly can help.
Book a demo today to see how Warmly’s signal-based platform can help you identify, prioritize, and engage the accounts that actually convert.
What is 8 Account Based Marketing Examples To Take Inspiration From In 2026?
8 Account Based Marketing Examples To Take Inspiration From In 2026 refers to the concepts and strategies covered in this article. Understanding these fundamentals helps B2B teams improve their sales and marketing effectiveness.
Why is 8 Account Based Marketing Examples To Take Inspiration From In 2026 important?
This matters because it directly impacts pipeline generation and revenue. Teams that master these concepts see better results from their go-to-market efforts.
How can I implement this?
Start with the strategies outlined above. For B2B teams, combining these tactics with tools like Warmly—which identifies website visitors and automates engagement—can accelerate results.
What tools help with 8 Account Based Marketing Examples To Take Inspiration From In 2026?
Several tools can help, depending on your specific needs. Warmly is particularly useful for identifying high-intent website visitors and engaging them before they leave your site.
What are the best practices for 8 Account Based Marketing Examples To Take Inspiration From In 2026?
Key best practices are covered throughout this article. Focus on the fundamentals first, measure your results, and iterate based on data rather than assumptions.
How to Evaluate B2B Data Providers: Warmly's Decision Framework
Time to read
Chris Miller
Finding good data is critical for any GTM organization. As the data landscape becomes increasingly complex, selecting the right B2B data providers is crucial for achieving business success.
According to one survey, 66% of B2B and B2B2C marketers say improving data quality is a key part of their go-to-market (GTM) strategy. Additionally, data quality is crucial when developing AI tools, such as custom chatbots or AI-powered sales agents.
As a signal-based orchestration platform, Warmly integrates with over 25 leading data providers, and our team rigorously assesses approximately 20 new providers monthly.
To hone and refine our process, we interviewed 20 Demand Generation leaders who have purchased data from hundreds of B2B data providers in their careers, developing our Data Vendor Decision Framework within our Data Team.
TLDR: You should consider more than just the quality of data when choosing a data vendor. Our Decision Framework highlights six essential factors that contribute to the overall quality of a B2B data provider: data quality, data quantity, ease of use and implementation, partnerships, legality and compliance, and data cleanliness.
For each factor, we’ve provided clear, actionable steps, including pre-work, evaluation methods, and key red flags to be aware of.
By evaluating all six factors for each vendor you’re considering, you’ll gain a complete picture of quality and reliability.
Six Factors to Consider When Choosing a B2B Data Provider
Quality of data
Quantity of data
Ease of Use & Implementation
Partnership Meter
Legality & Compliance
Cleanliness & Data Integrity
Resources
Email us at privacy@warmly.ai for a sample CSV of Test Data for testing company-level matching for Website Identification.
You can also email us at privacy@warmly.ai for a sample CSV of Test Data for testing person-level enrichment for Outreach.
1. Quality of Data
The quality of data has a direct impact on business decisions, making it a key consideration for organizations.
Pre-work
Prepare test data, including examples from current vendors and internal sources. Include ideal customer matches and some incorrect data as a control test.
Ensure that the test data represents your industry, target audience, and business use case.
Consider including data from multiple sources to compare accuracy across different providers.
Evaluate
What percentage of your Ideal Customer Profile (ICP) data does the provider match? Ensure they do not validate incorrect data.
Request sample reports and conduct manual spot checks on key data points.
Assess whether the provider offers validation mechanisms to minimise inaccuracies.
Why It Matters
No data source is perfect. A provider that correctly identifies 60% of your ICP is performing well. Unrealistically high match rates can indicate low-quality data (they are truly guessing to achieve high results).
High match rates may indicate that the vendor employs unreliable data aggregation techniques.
False positives can be more damaging than missing out on some matches. Imagine sending a sales rep to work hundreds of false positive leads that never convert; that’s a massive waste of time.
Red Flags
If a provider claims matches on obviously incorrect data, it's a major warning sign.
Look for transparency in their data sourcing and cleansing methodologies.
Avoid vendors who do not disclose their data verification processes.
2. Quantity of Data
When assessing data volume, consider:
Pre-work
Generate a large sample set (10k+ leads or IPs) for enrichment.
Ensure the sample includes a diverse range of potential customers.
Include identifiers like company names, domains, and other referenced data.
Evaluate
How much of your sample set does the provider match?
Assess both absolute match rates and coverage within your target market.
Compare results across multiple vendors to benchmark performance.
Why It Matters
Higher match rates indicate a broader reach, even if some leads fall outside your ICP.
Some vendors specialize in niche datasets, while others offer broader but less precise data.
A good vendor provides balanced depth and breadth of data.
Red Flags
A provider offering only one match per lead lacks depth. The best vendors provide multiple options ranked by confidence level.
Ensure the vendor ranks and segments the data appropriately.
Look for metadata that indicates data freshness and reliability (for example, a timestamp of the last time that data point was validated).
3. Ease of Use & Implementation
Even high-quality data is ineffective if it is challenging to implement.
Pre-work
Review technical documentation and plan a testing timeline.
Refer to the API documentation for clear integration instructions.
Look at case studies or testimonials about implementation experiences.
Evaluate
Do they offer flexible APIs, real-time updates, and timely flat-file deliveries?
Assess data refresh frequency and latency.
Verify if they support standard data formats (e.g., CSV, JSON, XML).
Observe how quickly they communicate with you during the testing phase.
Why It Matters
Quick, seamless integration indicates vendor maturity and minimizes operational delays.
A well-structured API or easy import process reduces engineering effort.
Smooth onboarding is a sign of a strong customer support team.
Red Flags
A lack of clear service-level agreements (SLAs), low uptime commitments, or slow response times can signal potential future problems. Any vendor that has been around for less than three years.
Ensure the vendor commits to 99.9% uptime for API reliability.
Confirm their response times for technical support inquiries.
4. Partnership Meter
Even the best B2B data providers can struggle to build strong relationships with their customers. However, partnership quality can be a significant factor in your overall experience with a data vendor.
Pre-work
Conduct reference checks.
Speak with current or former customers about their experience.
Research vendor reputation on platforms like G2 or Capterra.
Evaluate
Are they invested in long-term success, or do they prioritize short-term profits?
Look for signs of continued innovation in their roadmap.
Assess whether they provide strategic support beyond just selling data.
Quiz them on their strengths and weaknesses. Are they honest about their shortcomings?
Will they work with you to get started (pay-as-you-go) and understand volume-based usage, and help you stabilize known costs in the long run (e.g., switch to an unlimited data model once you know usage)?
Why It Matters
Trust and alignment are crucial. The best vendors are partners, not opportunists.
A good vendor should offer flexibility, training, and collaborative problem-solving. Avoid companies that rely on opaque pricing or aggressive upselling.
Red Flags
Extremely low pricing often indicates high customer churn and a lack of commitment.
Include a mandated 90-day migration window in contracts to protect against sudden service drops.
Ensure contract terms include exit flexibility without penalties.
Look for vendors who offer long-term engagement strategies (e.g., multi-year contracts).
If they cannot confirm contractually, they will never sell your data to others.
5. Legality and Compliance
Data sources must be legally obtained. To verify compliance:
Pre-work
Request and review legal documentation, including Data Processing Agreements (DPAs) and privacy policies.
Confirm compliance with GDPR, CCPA, and other relevant regulations.
Ensure vendors provide explicit consent and opt-out mechanisms.
Evaluate
Confirm compliance with GDPR, CCPA, and other regulations. Ensure transparency in data collection.
Look for certifications such as ISO 27001 or SOC 2.
Verify whether they regularly update their compliance processes (e.g., which privacy lawyers do they work with to stay informed about new and upcoming privacy laws).
Ensure they’re a registered data broker and have them send confirmations of registration for each state for the current calendar year (as of 2025: California, Vermont, Oregon, and Texas).
Understand their opt-out processes for individuals who wish to have their data removed or hidden.
Why It Matters
Protects your company from legal risks and maintains customer trust.
Data privacy violations can lead to hefty fines and reputation damage.
Transparent vendors provide audit trails and clear accountability.
Red Flags
Vendors unwilling to provide documentation or those with vague sourcing details should be avoided.
Avoid companies that lack a dedicated Data Protection Officer (DPO).
Ensure they disclose data retention and deletion policies.
6. Cleanliness and Integrity
Clean data is essential for accurate insights. To evaluate cleanliness:
Pre-work
Request documentation on data validation, update frequency, and duplicate detection.
Verify if they conduct manual review processes in conjunction with automation.
Verify how frequently their datasets are refreshed.
Evaluate
What processes are in place to maintain data accuracy and integrity?
Look for machine-learning-driven duplication and validation techniques.
Assess whether they offer enrichment tools to improve data quality.
Why It Matters
Outdated or incorrect data can lead to misinformed business decisions and wasted resources.
Clean data ensures higher accuracy in targeting and analysis.
Efficient data hygiene practices reduce manual cleanup efforts.
Red Flags
No clear strategy for removing outdated information or for a lack of regular data quality audits.
Avoid vendors who cannot articulate their data hygiene process.
Ensure they provide transparency into error rates and data refresh cycles.
💡 This framework works particularly well for evaluating modern AI-powered platforms that aggregate data from multiple sources.
For example, Growth Today's analysis of Clay's enrichment platform showcases how newer tools are addressing data quality and coverage challenges through waterfall enrichment and AI.
By applying our six factors to platforms like Clay, you can ensure your chosen solution delivers both innovation and reliability.
Evaluating B2B Data Providers
The best data vendor isn't necessarily the cheapest or the one with the most data; it's the one that aligns best with your needs and prioritizes a long-term partnership.
Leaders should focus on quality, quantity, ease of use, partnership, legality, and cleanliness to make informed vendor selections.
If you’re looking for a data vendor that can pass muster, look no further than Warmly. Our dedicated data team has spent years developing our data sets and ensuring we are a great, compliant data provider for you.
How Does Warmly Stack Up Against B2B Data Providers?
1. Quality of data ✅
Warmly’s transparent data commitment means you can test our data quality with our free tier before committing to us as a data provider.
Because we aggregate data from 25+ providers and cross-check it using our proprietary waterfall technique, we can validate and verify data on your behalf, ensuring it’s of the highest quality.
A great example of this is our Data Quality Slider, a unique feature that lets you maximize data quality (at the expense of data quantity) if you prefer.
We can also use AI to help you in scoring leads and filtering data to identify the best warm leads for you.
2. Quantity of data ✅
Warmly’s transparent data commitment means you can test our data quantity with our free tier before committing to us as a data provider. Because we aggregate data from 25+ providers and cross-check it via our waterfall technique, we can validate and verify on your behalf to give the highest quantity of data. See more in our overview of data quality.
A great example of this is our Data Quality Slider, a unique feature that lets you maximize data quantity (at the expense of data quality) if you prefer. Most of our datasets are updated daily, and some are updated weekly to ensure freshness.
Push the data directly to your preferred outbound/outreach providers via our integrations (e.g., email providers like Outreach, Salesloft, and HubSpot).
Warmly is a genuine partner. We’re here to offer transparent and communicative offerings that respect your time.
Our best-in-class customer service organization stems from years of experience in B2B data companies, and our RevOps and GTM experts are here to provide you with GTM consulting and GTM motion architecture, in addition to warm lead data.
We are committed to driving ROI for you every step of the way, reviewing your data and that of our dozens of data providers to help you make informed decisions about your lead generation strategies.
We ensure that all our data providers are partners and provide us with essential levers, such as migration windows, flexible pay-as-you-scale models, and assurances that they will not sell any data with signed data processing agreements.
We want you to relax while we handle the negotiations and dealings with 1st, 2nd, and 3rd-party data providers.
We conduct randomized privacy audits of our vendors to ensure that all vendors are registered data brokers and comply with all relevant standards.
While not required, Warmly is a registered data broker in all US states where compliance is mandated (our privacy team updates and checks this monthly).
All our data retention and deletion policies are downloadable and transparent from our security portal but from a high-level we can delete your data on demand, respond quickly to individual data deletion requests (see our compliant opt-outs here) as well as if you cease to become a customer we will delete your data within 30 days.
Contact our Data Protection Officer, Uri Steinfeld (uri@warmly.ai), with any questions.
6. Cleanliness & Data Integrity ✅
Warmly ensure clean data and data integrity as the cornerstone of how we create, collect, and distribute data.
Request an NDA from your Warmly representative to gain access to our Technical Deep Dive and learn how we process data to maximize quantity, quality, and compliance.
Because we deal with many B2B data providers, we need to merge these datasets appropriately into a clean, simplified, and de-duplicated database of records.
Although no data is perfect, our cleansing process is performed daily and ensures that no duplicate data is ever added to your CRM.
Warmly: Comprehensive Data for Signal-Based Selling
The checklist above should serve as your first port of call when evaluating a new data provider. As you can see, Warmly ticks all the boxes in one solution.
Our blend of 1st, 2nd, and 3rd-party data ensures you get accurate, real-time, person-level de-anonymization data to help you identify your warmest leads and reach out to them immediately.
ZoomInfo vs. 6sense: Find out what the strengths and weaknesses of ZoomInfo and 6sense are in this comparison guide.
Frequently Asked Questions
What is the best way to evaluate b2b data providers: warmly's decision framework (2026)?
The best approach depends on your specific situation. Follow the step-by-step guide above for proven methods. Key success factors include proper setup, consistent execution, and measuring results.
How long does it take to evaluate b2b data providers: warmly's decision framework (2026)?
Timeline varies based on complexity and resources. Simple implementations take days, while comprehensive strategies may take weeks to fully execute. Start with quick wins outlined above.
What tools do I need to evaluate b2b data providers: warmly's decision framework (2026)?
Essential tools are covered in the guide above. For B2B sales teams, Warmly can help by identifying website visitors and providing intent signals to prioritize your efforts.
What are common mistakes when trying to evaluate b2b data providers: warmly's decision framework (2026)?
Common pitfalls include moving too fast without proper setup, not measuring results, and using outdated tactics. Follow the best practices above to avoid these issues.
Can I automate this process?
Many aspects can be automated with the right tools. Warmly offers automation for website visitor identification and engagement. See the tools section above for automation options.
10 Real-World AI In Sales Examples In 2026
Time to read
Chris Miller
AI in sales is not a thing of the future - it’s already here.
From lead qualification and pipeline prioritization to full-blown outbound orchestration, AI is transforming how sales actually gets done, helping teams work smarter, close deals faster, and scale with precision.
But forget vague promises.
In this article, we’ll be diving into real-world AI in sales examples, including the workflows, tools, and strategies modern revenue teams are already using to win more with less effort.
Whether you're exploring AI for the first time or looking to uplevel your current stack, these examples will give you the clarity (and inspiration) you need to make it work.
Let’s get into it.
How is AI used in sales in 2026?
Within just a few years, AI has gone from a futuristic add-on to a core pillar of modern sales operations.
Today, it’s not just about automating busywork - although that’s still one of its most widespread use cases. Instead, it’s about making sales teams faster, sharper, and more scalable.
Modern AI doesn’t just sit in the background running scripts.
It actively helps sales reps prioritize the right accounts, personalize outreach at scale, spot buying signals, and even predict which deals are most likely to close.
It learns from historical patterns, analyzes real-time behavior, and adapts based on context, giving reps the edge they need in competitive markets.
But here’s the real shift: AI is no longer limited to isolated tasks like email writing or call transcription.
We're seeing the rise of agentic AI, that is, smart systems that can autonomously execute multi-step sales workflows with minimal human input.
Think of outbound campaigns that launch, learn, and iterate on their own.
In the next section, we’ll look at exactly how sales teams are applying this tech in the wild with real-world examples of AI:
Driving pipeline.
Boosting conversions.
Reshaping how revenue gets built.
Top 7 ways you can use AI in sales
Now that I’ve covered the basics of how AI is reshaping the sales landscape at a high level, let’s get more specific.
What does this actually look like in action?
The first thing you need to understand is that the most effective sales teams aren’t using AI as just another tool.
Instead, they’re building their workflows around it.
From intelligent outreach to pipeline forecasting, AI is being embedded directly into daily sales operations in ways that save time, increase precision, and drive results.
Below, I’ll walk you through some of the most impactful ways AI is being used in sales right now - many of which we’ve tested ourselves and have seen make a real difference in how teams operate and drive revenue.
1. Outbound that scales without burning out your team
Outbound prospecting is one of the most resource-intensive parts of sales.
It takes time, research, coordination, and consistency to do it well.
But as teams push to hit bigger pipeline goals with the same - or smaller - headcounts, the old way of doing outbound just doesn’t scale.
That’s where AI-powered outbound steps in.
Instead of relying on reps to manually build lists, write outreach, and follow up across dozens (or hundreds) of accounts, AI can take on much of that legwork.
And not just basic automation. We’re talking about outbound that adapts in real-time to signals, personas, and past interactions.
We’ve put this into play with Warmly’s AI SDR feature, and the difference is immediate.
The system can:
Handle prospecting for thousands of accounts simultaneously.
Tailor messaging based on industry, buyer intent, or job title.
Adjust sequences on the fly based on engagement (clicks, replies, ghosting).
Work across multiple channels, not just email.
It’s like giving your team an always-on sales assistant who never forgets to follow up, never sends a generic message, and never gets tired.
This way, instead of deciding who gets your attention, you can start covering more ground, focusing on high-value leads without sacrificing quality or personalization.
2. AI chatbots that actually feel human
Let’s be honest: most chatbots still feel like you’re yelling into the void.
They follow rigid scripts, fail to understand intent, and often leave prospects more frustrated than informed.
But recently, AI chat has significantly levelled up, especially when it’s tuned to the right signals.
Today, chatbots are not just about answering simple questions - they’re about starting meaningful conversations that adapt in real-time based on who the visitor is and what they’re trying to do.
That’s exactly what we had in mind when designing Warmly’s AI Chat.
This AI-powered chatbot doesn’t just sit passively on your website. Instead, it engages visitors proactively based on behavior, past interactions, and buying intent.
So, if someone’s poking around your pricing page or returning after a missed meeting, it knows how to tailor the message - a “Welcome back” here, a demo offer there, or quick access to key resources if someone’s in research mode.
It also stays completely on-brand. You can train it with the same messaging your reps use, so it reflects your tone, value prop, and positioning perfectly.
This means that when done right, AI chatbots can:
Qualify leads instantly, without asking them to fill out a form.
Respond dynamically based on real-time visitor signals.
Surface high-value offers at just the right moment.
Book meetings while your team sleeps.
This way, you get conversations that convert, plus a whole new way to engage high-intent leads around the clock without burning out your human reps.
3. Automated lead nurturing
Following up is one of those things everyone knows they should do, but it’s also one of the easiest to let slide.
A lead books a demo, misses it, never replies... and before you know it, they’ve disappeared from the funnel entirely.
Multiply that by dozens of leads a week, and the cracks start to show.
That’s where automated lead nurturing powered by AI steps in to save the day.
Instead of relying on manual reminders or hoping your reps remember who to follow up with, AI can automatically track lead behavior and re-engage at the right moment through personalized emails, LinkedIn messages, or even in-site chat prompts.
We’ve seen this in action with Warmly’s nurturing workflows run by the AI SDRs and the Orchestrator, which are designed to keep every lead moving.
What makes it work is the contextual awareness:
Each message adapts to where the lead is in their journey.
Timing is optimized based on engagement signals.
Sequences can span multiple channels (email + LinkedIn) for higher visibility.
No lead gets left behind just because your team got busy.
This kind of automation is all about making sure no opportunity slips through the cracks.
And when your follow-up game is this consistent, it’s only a matter of time before it starts showing up in your conversion rates.
4. Smarter lead scoring that helps reps focus where it counts
Not all leads are created equal, but traditional lead scoring often treats them like they are.
Someone opens a newsletter? +5 points. Downloads a case study? +10.
But that kind of static, rules-based approach doesn’t capture real buying intent, and it definitely doesn’t keep up with how buyers behave in 2026.
It uses machine learning to analyze everything from firmographics to on-site behavior, CRM history, and real-time engagement signals to predict which leads are most likely to convert.
Instead of just tracking activity volume, AI focuses on conversion patterns, surfacing the leads that actually move the needle.
Instead of just scoring leads based on broad criteria, Warmly helps you:
Define your ICP using deep, AI-driven research, not just basic firmographics.
Monitor real-time buying signals across site visits, CRM enrichment, and de-anonymized data from 10+ providers.
Automatically enrich and prioritize lead segments with contextual data.
Route hot leads to the right rep instantly, with Slack alerts and CRM-ready notifications.
This means you’re not just guessing who to reach out to - you know beforehand who’s most likely to be interested in what you have to offer.
Your reps will be spending more time in front of qualified leads and less time chasing dead ends.
It’s like having an AI-powered filter on your entire funnel, so what comes through is warmer, faster-moving, and way more likely to close.
5. Real-time pipeline visibility without the guesswork
Forecasting has always been part math, part magic.
Sales leaders rely on CRM reports, rep gut feel, and last-minute spreadsheet gymnastics to predict what’s coming in.
But with complex deal cycles, shifting buyer behavior, and distributed teams, it’s no surprise that forecasts are often off the mark.
AI is changing that by turning guesswork into clarity.
In 2026, the best-performing teams are using AI to track pipeline health and predict outcomes with far more accuracy.
These tools analyze historical deal data, rep activity, buyer engagement, intent signals, and even deal velocity to flag risks early and highlight what’s most likely to close.
As a result, you’ll be able to:
Understand which deals are moving and which are stalling.
See intent patterns in real time across entire accounts.
Make faster decisions on where to focus, without waiting for end-of-quarter reviews.
What’s especially powerful is how early these systems can surface issues:
Before a deal goes cold, before a quarter slips away, and definitely before your team wastes time on pipeline that was never real to begin with.
When AI becomes your co-pilot for forecasting, your sales process becomes a lot less reactive and a lot more predictable.
6. Real-time call intelligence that helps reps close faster
They’re where you build trust, handle objections, and move deals forward.
But let’s be real: in the moment, it’s easy to miss buying signals, skip a discovery question, or mishandle a pricing objection.
And traditional coaching? It usually happens after the fact, when the momentum’s already gone.
That’s why real-time call intelligence has become a must-have.
Tools like Gong, Fireflies, and Chorus now do more than transcribe calls.
They coach reps in real-time, suggesting relevant talk tracks, surfacing competitor mentions, and even flagging when a deal is at risk mid-conversation.
For newer reps, it shortens the ramp-up time. For experienced ones, it keeps them sharp.
What’s just as important, though, is what happens before the call starts. That’s where Warmly’s AI Co-Pilots make a big difference.
Before a rep even joins the call, Warmly helps them:
Identify who’s most worth talking to, based on real-time website behavior and AI-modelled intent signals.
Personalize their outreach as Copilot breaks down why a lead is interested and even suggests what to say.
Figure out when to transition to a face-to-face video call, turning website chats into meaningful, human interactions that build trust faster.
This combination of pre-call AI context and in-call intelligence creates a compounding advantage: your reps aren’t just reacting anymore.
Instead, they’re leading conversations with the right people, at the right time, with the right message.
And when every second on a call counts, that edge can make all the difference.
7. Personalization at scale, without the manual grind
We all know personalized outreach works. It gets more replies, builds better rapport, and ultimately converts more leads.
The problem? Doing it well doesn’t really scale.
Most reps either spend too much time researching every prospect or settle for generic templates that feel like spam.
AI can successfully bridge that gap.
Namely, AI can instantly generate relevant, contextual, and human-sounding messages based on real data, such as what a prospect cares about, what stage they’re in, and what’s actually going to resonate.
For example, Warmly’s Orchestrator automatically personalizes and sends messages to key stakeholders of surging accounts, using essential B2B data, intent signals, and previous interactions to tailor each message to perfection.
It’s not just “insert {{first name}} and hope for the best.” It’s real personalization, driven by real context across hundreds or even thousands of leads.
This kind of scalable personalization helps you cut through the noise, increase reply rates, and focus rep time where it matters most - talking to the right people, with the right message, at the right time.
10 real-world AI in sales examples that you can learn from
It’s one thing to talk about what AI can do in sales and another to see it working in the wild because while theory is helpful, execution is where the real learning happens.
In this section, we’re looking at actual companies using AI to improve prospecting, close deals faster, and scale revenue right now.
From fast-growing startups to established enterprises, these examples show how AI is being applied across different parts of the sales process and what results teams are seeing as a result.
Whether you're building your own AI playbook or just exploring what’s possible, these stories offer practical takeaways, proven strategies, and a few creative ideas worth stealing.
1. How Premikati used AI to turn anonymous traffic into qualified pipeline
Challenge: Like many B2B teams, Premikati was running multi-channel marketing campaigns but struggling to connect the dots between site traffic and real results.
Despite getting decent volume from PPC and LinkedIn, they lacked the visibility to attribute leads, track ROI, or prioritize high-value prospects.
Solution: Enter Warmly’s AI-powered Orchestrator and AI Chat.
By combining real-time de-anonymization with signal-based segmentation, Warmly gave Premikati’s team deep insight into who was landing on their site, where they came from, and what they were doing once they arrived.
No more guessing which campaign worked, as AI connected the dots across UTM links, page visits, and research intent.
Once identified, leads were scored and routed into personalized automated outbound sequences through Warmly’s AI Orchestrator, which was fine-tuned to:
Reach out to those leads in highly personalized LinkedIn and email sequences, leveraging the deep insights Warmly has on each lead.
And with Warmly’s AI Chat, Premikati could finally engage and convert inbound visitors instantly with no human intervention needed.
“We booked a qualified meeting from Warmly, which has never happened with our previous chatbots.” - Michael Buczynski, VP of Marketing.
Moreover, reps could now monitor live chat sessions, be looped into conversations with warm leads, or let the bot handle bookings thanks to AI that adapts to visitor signals in real-time.
Results:
Better visibility into campaign performance.
More meetings booked from inbound traffic.
Scaled outreach with fewer manual steps.
Expanded reach within buying committees.
💡Takeaway:
If you’re running paid campaigns without real-time lead tracking and AI-driven follow-up, you’re flying blind, as all the qualified website traffic will fall through the cracks.
Use AI to segment traffic, personalize engagement, and orchestrate outreach automatically, so every visitor has a path to pipeline.
2. How Kandji used a sophisticated AI-powered chatbot to book 2 qualified meetings in just 8 minutes
Challenge: Kandji, an Apple device management platform, needed a faster and more scalable way to turn website visits into real sales conversations without burdening its sales team with more manual work.
Like many high-growth companies, they were looking for ways to generate pipeline more efficiently without sacrificing personalization or timing.
Solution: Kandji adopted Warmly’s AI Chat to engage visitors at the exact moment of interest.
The AI chatbot delivered personalized, company-specific messages based on real-time visitor identification, and it did so without reps needing to monitor the site around the clock.
Just weeks after implementing Warmly, Kandji’s team saw the impact: two qualified meetings were booked within 8 minutes.
Slack notifications alerted their reps the moment a high-intent lead responded to the AI, allowing the team to jump into the chat, continue the conversation as real humans, and guide prospects straight into booked calls.
It was a seamless handoff from Warmly’s AI to Kandji’s sales team.
Results:
2 qualified meetings booked in less than 10 minutes.
Increased inbound engagement with minimal human effort.
Personalized experiences delivered at scale.
💡 Takeaway:
Timing matters more than ever.
With AI-powered chatbots, you don’t need a rep online 24/7. You just need to be ready when it counts.
Set up real-time alerts, craft smart AI responses, and create seamless handoffs.
That’s how you turn intent into action at scale.
3. How StraightIn closed $10K in 2 weeks with AI-powered prospecting
Challenge: StraightIn, a LinkedIn marketing agency, had strong website traffic and active email and social campaigns, but struggled to identify who was actually visiting their site and where they were in the buying journey.
Without clear visibility into intent signals, much of their demand gen effort was flying blind.
Solution:
Using Warmly’s AI Orchestrator and real-time visitor de-anonymization, StraightIn began tracking high-intent leads the moment they hit the site.
💡Note: You will need to be on one of Warmly’s paid plans to gain access to AI Prospector. For ARC, the ROI was 200% over 6 months.
Instead of wasting time nurturing cold prospects, the team shifted to targeting only warm visitors - people actively showing buying intent through behaviors like pricing page visits and deep scrolls.
Here’s the workflow StraightIn’s team built with Warmly’s AI Orchestrator:
The AI feature screened for website visitors ready to buy based on granular firmographic and behavioral data and ICP fit.
Once identified, high-intent leads were automatically segmented and added to personalized email and LinkedIn sequences.
Some leads were also added to LinkedIn Ads campaigns, eliminating unnecessary ToFu spend.
Results:
Two deals closed from LinkedIn in just two weeks, totalling $10K in new revenue.
Email engagement shot up: +9% open rate, +6% CTR, and +1% positive replies.
LinkedIn ad spend dropped significantly while conversion quality improved.
💡 Takeaway:
The fastest way to boost ROI is to stop chasing cold leads.
Use AI to identify high-intent visitors early, segment them smartly, and automate outreach where it matters.
You’ll move faster, spend less, and close more, just like StraightIn did.
4. How Connectteam scaled outreach and cut no-shows by 73% using an AI SDR
Challenge: Connectteam, an all-in-one employee management app, was scaling fast, but its SDR team was maxed out.
Traditional email and SMS campaigns weren’t cutting it, especially for reactivating older leads.
With no bandwidth to hire more reps, and a painful 75% meeting no-show rate, the team needed a way to personalize outreach and increase capacity without adding headcount.
Their goal was to re-engage cold and closed-lost leads, reduce no-shows and keep demos full, while avoiding scaling costs and operational complexity.
Solution: Connectteam turned to 11x, deploying “Julian,” an AI-powered SDR designed to make personalized phone calls, schedule meetings, and follow up automatically.
Julian did more than just handle cold calls, though. He monitored intent, followed up with closed-lost leads, and confirmed meetings in real time.
Results:
Meeting no-shows dropped by 73%.
Each SDR generated ~$30K more revenue without hiring.
Outreach tailored by vertical, powered by real-time behavior and signals.
Missed and stale leads re-engaged with intent-based phone outreach.
And now?
Warmly has partnered with 11x, meaning teams can bring this same AI SDR power directly into our sales stack alongside Warmly’s existing AI capabilities, lead nurturing, and signal-based outreach capabilities, unlocking a whole new world of opportunity for our customers.
They unlock new opportunities by engaging leads you couldn’t reach before.
Whether it’s booking meetings or reviving cold accounts, adding an AI phone rep can immediately increase revenue without increasing headcount.
5. How InvestNext boosted reply rates by 30% with AI-personalized outreach
Challenge: InvestNext, a real estate investment management platform, had a strong value proposition, but their outbound sales motion was struggling to scale.
Personalizing each cold email took 15–20 minutes per lead, which wasn’t sustainable for their lean sales team.
With competition growing and inboxes getting noisier, they needed a way to scale outreach without losing personalization and break through in a saturated market,
Solution: InvestNext adopted OneShot.ai, an AI-powered personalization engine designed to create smart, tailored email outreach at scale.
Rather than sending one-size-fits-all messaging or burning rep hours on manual copy-pasting, OneShot.ai automates high-quality personalization, customizing content by persona, company, and context.
This way, InvestNext got more engagement, more replies, and more deals without hiring more reps.
Results:
30% increase in reply rates (highest in company history).
25% lift in open rates.
75% reduction in time spent personalizing emails.
Multiple deals closed from email-only sequences.
💡 Takeaway:
Scaling personalization doesn’t have to mean sacrificing quality.
With AI, you can send better emails faster and free up your reps to focus on what actually moves the deal forward.
If you’re still writing every cold email by hand, it might be time to let AI take the first shot.
6. How a BPO scaled outbound calls to 500K+ patients using an AI-powered virtual assistant
Challenge: This Dallas-based BPO was growing fast, but growth brought pressure.
With call volumes spiking and a lean workforce, the leadership team needed a way to scale without compromising customer experience or blowing up cost per call.
One urgent use case was outreach on behalf of Medicare: informing qualifying members about free COVID-19 test kits and collecting confirmation for fulfilment.
But there was a catch: the campaign had to go live within one week, and hiring 130 new agents to handle the expected call volume wasn’t realistic.
Solution:
The BPO partnered with Uniphore, deploying the U-Self Serve intelligent virtual assistant (IVA) to automate outbound calls at scale.
Within days, Uniphore’s team integrated the IVA with the BPO’s existing dialer and scripted voice flows using studio-quality voiceovers to replicate a human agent experience.
The assistant was trained to confirm test kit requests, answer FAQs, and escalate complex cases to live agents with full context, enabling a smooth handoff.
What would have required 130 agents was now handled by a scalable AI layer at a fraction of the cost.
Results:
500K+ Medicare members engaged.
Real-time FAQ handling and confirmation without human reps.
Rapid go-live in under 1 week.
~$2 saved per call in operational costs.
💡 Takeaway:
AI doesn’t just reduce support costs. It unlocks entirely new outreach campaigns that would otherwise be too expensive or resource-heavy to run.
For lean teams under pressure to scale, virtual assistants offer a fast, flexible path to reach more people, faster.
7. How Whatfix built a more confident sales team with AI-powered knowledge access
Challenge: Sales success isn’t just about product features - it’s about confidence.
At Whatfix, a digital adoption platform, new reps often struggled to find the right information at the right time.
The key issue was that salespeople aren't proactive learners by nature. They need knowledge delivered when it's immediately relevant, not weeks in advance.
But with content spread across documents, slides, and sales enablement platforms, knowledge accessibility was a bottleneck.
The team needed a solution that could surface accurate, context-specific answers instantly and on demand.
Solution: Whatfix turned to Docket, an AI-powered knowledge assistant that gives sales reps real-time answers to product, positioning, and competitive questions with no digging or document-hunting required.
Docket was set up specifically for new hires, embedding itself into their daily workflow.
Now, instead of pinging managers or wading through folders, reps can simply ask a question and get a short, accurate, and confident response right when they need it.
Results:
New reps are empowered from day one.
Product knowledge is more accurate and consistent across the team.
Sales conversations are more confident, credible, and effective.
Establishing a self-serve learning culture without forcing reps into structured training.
💡 Takeaway:
Sales teams need faster access to the right content, not more content.
AI knowledge assistants like Docket meet reps where they are, reduce hesitation, and turn every sales conversation into a more confident one.
If your team is constantly asking, “Where do I find that?”, this is the fix.
8. How an insurance provider cut costs by 45% and sped up renewals with AI voice agents
Challenge: A major insurance provider was facing a wave of operational pain: overwhelmed call centers, long wait times, missed policy renewals, and rising agent workloads.
Customers were frustrated by delays, confused by complex renewal processes, and increasingly at risk of churning.
The company needed a way to reduce costs, boost retention, and deliver a better experience without overloading its team.
Solution: They turned to Convin’s AI-powered call automation, deploying voice agents to handle routine policy renewal calls at scale.
Instead of relying solely on human agents, AI took on the heavy lifting by proactively reminding customers about upcoming renewals, answering FAQs, and completing the process over the phone in minutes.
The system even handled multilingual support and personalized conversations based on customer profiles, helping policyholders feel heard without ever waiting on hold.
Results:
36% faster renewal processing, thanks to automated reminders and real-time voice interactions.
50% reduction in agent workload, freeing up human reps for complex or high-risk cases.
28% drop in policy lapse rates through proactive outreach and risk mitigation.
💡 Takeaway:
AI voice agents aren’t just a support tool - they’re a revenue driver.
If your team is buried under repeatable tasks like renewals, billing calls, or reminders, AI automation can reduce costs and boost CX at the same time.
9. How Spirit Airlines cut employee support inquiries by 76% using AI video
Challenge: With over 13,000 employees across airports, cockpits, and offices, Spirit Airlines needed a better way to communicate updated health, wellness, and benefits policies.
Traditional methods like long emails and intranet posts weren’t cutting through, especially for mobile, deskless workers like flight attendants.
Manual video creation helped a little, but constant re-recordings made it time-consuming and hard to scale.
This resulted in low engagement and a constant flood of employee support calls asking the same questions over and over.
Solution: Spirit turned to Synthesia, an AI-powered video platform that transforms written documents into polished, engaging videos at scale.
Instead of writing lengthy emails or filming updates manually, the HR team now feeds content into Synthesia’s AI assistant, which creates a video draft in minutes.
These videos are embedded across internal channels (email, intranet, employee app), giving everyone easy access anytime, anywhere.
The team especially values the ability to quickly update scripts and regenerate videos without reshoots, which is crucial for communicating frequent policy changes to a constantly moving workforce.
Results:
76% reduction in phone-based employee support.
600% increase in engagement with benefits content.
Nearly 300 videos created and updated in real time.
💡 Takeaway:
If you’re sharing important internal updates through walls of text, it might be time to switch channels.
AI video tools like Synthesia make it easy to scale communication, increase retention, and reduce internal support burden without needing an entire production crew.
10. How Okta runs revenue in real time using AI-powered forecasting
Challenge: As a fast-scaling enterprise cybersecurity leader, Okta needed a more reliable, aligned, and proactive approach to managing revenue.
Their revenue teams were spread across functions and tools, making it hard to answer critical questions like:
Where are we headed this quarter? What’s at risk? And who needs to act right now?
Manual forecasting, siloed data, and reactive decision-making weren’t cutting it at scale.
Okta needed a system to predict outcomes, surface risks, and unify the entire organization around pipeline health and execution.
Solution: Enter Clari’s AI-powered Revenue Orchestration Platform.
Okta uses Clari daily across sales, finance, and leadership to align on pipeline status, forecast with confidence, and drive action where it matters most.
Results:
Accurate, real-time forecasting trusted by execs across the org.
Faster response to pipeline risks, with opportunity scoring and trend analysis.
Seamless team alignment through a single source of revenue truth.
Hours saved every week by removing guesswork from sales workflows.
Reliable data and stage hygiene driving cleaner forecasts and tighter execution.
💡 Takeaway:
Forecasting is more about good orchestration than reporting.
With AI surfacing the right signals and risks in real time, revenue teams can act earlier, move faster, and close stronger.
If your team is still stitching together forecasts in spreadsheets, it’s time to run revenue like Okta: live, aligned, and AI-driven.
Next steps: Making AI real inside your sales team
If there’s one theme running through every example in this article, it’s this: AI in sales is primarily about good execution.
But adopting AI isn’t about chasing the latest tool or adding another widget to your stack.
It’s about rethinking how your team operates:
Where are reps spending time they shouldn’t be? Where are leads falling through the cracks? Where is your pipeline stalling?
Those are the friction points AI is built to solve.
This is why the smartest teams in 2026 are operationalizing AI use.
And not with theory, but with real, practical workflows that automate the boring stuff, surface the important stuff, and give reps the context they need to sell with confidence.
If you’re ready to build your own AI-powered sales funnel, Warmly can help.
From signal-based outreach to full-cycle agentic SDRs, our platform is built to help modern sales teams work smarter, move faster, and scale without burning out.
What is 10 Real World AI In Sales Examples In 2026 [Reviewed]?
10 Real World AI In Sales Examples In 2026 [Reviewed] refers to the concepts and strategies covered in this article. Understanding these fundamentals helps B2B teams improve their sales and marketing effectiveness.
Why is 10 Real World AI In Sales Examples In 2026 [Reviewed] important?
This matters because it directly impacts pipeline generation and revenue. Teams that master these concepts see better results from their go-to-market efforts.
How can I implement this?
Start with the strategies outlined above. For B2B teams, combining these tactics with tools like Warmly—which identifies website visitors and automates engagement—can accelerate results.
What tools help with 10 Real World AI In Sales Examples In 2026 [Reviewed]?
Several tools can help, depending on your specific needs. Warmly is particularly useful for identifying high-intent website visitors and engaging them before they leave your site.
What are the best practices for 10 Real World AI In Sales Examples In 2026 [Reviewed]?
Key best practices are covered throughout this article. Focus on the fundamentals first, measure your results, and iterate based on data rather than assumptions.
10 Best AI Advertising Tools in 2026 (Tested & Ranked)
Time to read
Alan Zhao
AI advertising isn’t coming in the near future. It’s already rewriting the rules.
In 2026, the most competitive ad campaigns are both creative and intelligent.
Powered by AI, advertisers are now automating testing, optimizing performance in real-time, and targeting with precision that was unthinkable just a few years ago.
No matter whether you’re running paid social, programmatic display, or search ads, the right AI tools can help you scale campaigns faster, spend smarter, and drive more conversions without increasing or burning out your team.
But with a growing list of platforms claiming to “optimize with AI,” knowing what actually works (and what’s just buzz) isn’t easy.
That’s where this guide comes in.
I’ve researched and reviewed dozens of platforms and came up with this list of the 10 best AI tools for advertising in 2026, based on real features, real outcomes, and real use cases, so you can cut through the noise and focus on what will actually move the needle.
TL;DR
The best AI-powered advertising tool on the market is Warmly, with its signal-based ad targeting that aligns campaigns with real-time buyer intent so you can reach the right audience at the right time.
Advertising tools like Albert and Birch are ideal for autonomous digital marketing management and multichannel ad management
On the other hand, there are tools like AdCreative and Jacquard that can help you create the ad campaigns, such as the copy and imagery.
The most important factors to consider when choosing AI tools for advertising include alignment with your ad channels and real-time optimization capabilities.
Key factors to consider when buying AI tools for advertising
Before we dive into the list of top AI advertising tools, it’s worth zooming out for a second.
Choosing the right platform isn’t just about who has the flashiest features or most integrations. It’s about finding the best fit for your strategy, channels, and team.
Therefore, below are a few key things to keep in mind as you evaluate your options, as they’re what separates tools that actually drive performance from those that just add complexity.
1. Alignment with your ad channels
Not every AI tool is built for every channel.
Make sure the platform you choose actually supports the channels you use most, whether that’s Google Ads, Meta, LinkedIn, programmatic display, or all of the above.
2. Real-time optimization capabilities
One of AI’s biggest value props in advertising is its ability to adjust campaigns in real-time.
Look for tools that actively monitor your audience and performance data and make smart, on-the-fly adjustments, targeting the right people with the right offer at the right time.
3. Creative and copy generation features
If you're looking to save time (and mental energy), tools that generate headlines, ad variations, and visuals using AI can be a huge win.
But it’s not just about quantity. Check if the creative actually converts and if it can be customized to match your brand voice.
4. Integration with your existing stack
Your AI tool shouldn't live in a silo.
Make sure it plays nicely with your CRM, analytics tools, ad platforms, and any creative software you’re already using because disconnected systems = missed insights.
5. Level of control and transparency
AI shouldn’t be a black box.
Look for platforms that give you visibility into why certain decisions are made, and let you fine-tune campaign parameters instead of fully automating everything with no human input.
6. Pricing and scalability
Some tools price based on ad spend, others on features or team size.
Pick something that fits your budget today but won’t punish you for growing, especially if you’re scaling campaigns across multiple channels or regions.
What are the 10 best AI tools for advertising in 2026?
The best AI tools for advertising on the market are Warmly, Albert, and AdCreative.
Here are the best AI-powered advertising tools on the market in 2026 after evaluating 30+ tools:
Warmly: Signal-based ad targeting.
Albert: Autonomous digital marketing management.
AdCreative: AI-powered ad creatives.
Proxima: Predictive analytics for improved audience targeting.
Jacquard: AI-generated marketing copy.
Birch: Automated multichannel ad management.
Neural.love: AI-generated visual content.
Madgicx: Meta ads optimization and automation.
Pencil: AI-powered ad generation.
Adzooma: PPC performance insights for seamless ad optimization.
1. Warmly
Best for: Signal-based ad targeting that aligns campaigns with real-time buyer intent, allowing you to reach the right audience at the right time.
Who is it for: B2B marketing and demand gen teams looking to run hyper-targeted, intent-driven ad campaigns across LinkedIn and other key channels.
Warmly (that’s us) offers the best AI tool for advertising with our AI-powered revenue platform built to help teams identify, engage, and convert high-intent buyers using signal-based targeting and AI-driven workflows.
Unlike traditional ad tools that rely on static lists or guesswork, Warmly combines real-time behavioral signals with powerful orchestration to automatically serve the right message to the right prospect at the right moment.
The best part? It lets you reach warm leads not only through ads, but also through personalized onsite experiences and email and LinkedIn.
Let’s take a look at the key features that make Warmly the perfect choice for advertising.
Feature #1: Signal-based ad targeting
Warmly’s standout feature is its ability to track onsite and offsite signals - such as website visits and interactions, research intent, and social signals (e.g., LinkedIn engagement) - and leverage them to create highly targeted custom ad audiences.
For example, using Bombora intent data and Warmly’s Orchestrator, you can detect when companies are researching your competitors, integration partners, or relevant keywords.
Warmly then syncs those companies to LinkedIn Ad audiences, triggering campaigns that speak directly to what those prospects are actively exploring.
The AI agent uses all three types of intent signals to detect high-value opportunities, including them instantly in paid social campaigns across LinkedIn, Google Ads, Facebook, etc.
The result?
A constantly refreshed audience of in-market accounts sees highly relevant ads on all the channels they live in, while your competitors are still guessing.
It’s a scalable, automated way to increase awareness, warm up your ICP, and capture mindshare early in the buying journey.
Feature #2: Warm Offers
With Warm Offers, you can dynamically personalize offers or CTAs on your website based on a visitor’s traffic source, persona, and intent level.
Let’s say a buyer arrives via a high-intent keyword ad.
Instead of showing them a generic landing page, Warmly’s DemandGen agent can surface a tailored message or time-sensitive offer that aligns with their use case, dramatically increasing the odds they’ll convert.
It’s not just personalization for personalization’s sake.
It’s smart, signal-based customization designed to convert pipeline-ready prospects faster.
Feature #3: AI-powered outreach workflows
Warmly doesn’t stop at ads. It also orchestrates what happens before and after someone clicks or converts.
With AI-powered lead workflows, you can automatically include each lead into specific engagement workflows based on their intent score.
For example, you can:
Engage high-intent leads via AI Chat - Warmly’s highly customizable AI-driven chatbot can qualify leads, answer their questions, book meetings, and loop in human reps when necessary, all the while maintaining your brand’s tone of voice and remaining context-aware.
Nurture leads via hyper-personalized campaigns - Warmly’s AI agents pick up warm leads in real-time, including them in tailored LinkedIn and email drip campaigns that take personalization way beyond first names and firmographics, including things like research intent, previous interactions, etc.
Reengage leads through retargeted ad sets - Leads who didn’t convert the first time around can be warmed up again through carefully crafted ads and warm offers tailored to their intent and actual needs.
This makes Warmly more than just an ad tool.
In fact, it’s much fairer to say that it’s a full-funnel engagement engine that ensures no high-intent lead slips through the cracks, ever.
It’s an easy-to-use platform that automatically builds and maintains real-time, up-to-date lists of your best-fit accounts and contacts, so your GTM team can focus on strategy and creativity instead of spreadsheets.
Here's how it works:
The Marketing Ops Agent connects directly to your CRM, ad platforms, outbound tools, and intent sources to automate key targeting workflows:
AI-powered lead scoring and enrichment to create dynamic lists ranked by fit and buying intent.
Buying committee identification using insights from past closed-won deals.
Instant audience syncing with HubSpot, Marketo, LinkedIn, Meta, Outreach, and more. No more manual CSV uploads.
Automatic list updates as new intent signals or engagement changes occur.
Unified buying intent scores that stay synced across every sales and marketing channel.
Pricing
Warmly’s pricing is modular and component-based and comes with a free plan that lets you identify up to 500 website visitors per month.
You can choose the components that best match the needs of your business, as components are priced by output and not just by a monthly fee.
There are 4 paid product plans that you can choose from:
AI Data Agent (Starts at $10,000/yr), which includes 10,000 credits, person-level web visitor de-anonymization (RB2B + Vector data), CRM integration, and access to our Coldly Contact Database.
AI Inbound Agent (Starts at $16,000/yr), which adds a native marketing outbound automation, domain warmup, and lead routing with custom CRM fields.
AI Outbound Agent (Starts at $22,000/yr), which adds automated signal-based outbound orchestration, AI chatbot & live video chat, email automation, LinkedIn automation, and email warmup.
Marketing Ops Agent (Starts at $25,000/yr), which adds AI-powered account scoring, AI enrichments and custom signals, buying committee identification, real-time buying intent signal tracking, and automatic updates across all enrichments, signals, account and lead lists.
Pros & Cons
✅ Advanced signal tracking lets you create hyper-targeted ad campaigns.
✅ Seamless integrations with your entire sales and marketing stack, including LinkedIn Ads, Google Ads, Facebook and Instagram ads, Zapier, Bombora, CRMs, Slack, and more.
✅ Dynamic website personalization based on lead behavior and source.
✅ AI workflows that go beyond advertising and automate multi-channel outreach and follow-up.
✅ Real-time updates to ad audiences based on intent and live behavior, not static lists.
✅ Highly customizable and configurable, so teams of all sizes and experience levels can adjust it to their needs.
❌ Its most advanced features are available only on the paid plans.
2. Albert
Best for: Autonomous cross-channel campaign management and optimization.
Who is it for: Mid-size to large marketing teams aiming to scale digital advertising with minimal manual intervention.
Albert is an AI-driven marketing platform that autonomously manages and optimizes digital advertising campaigns across various channels, including search, social, and programmatic.
Moreover, Albert enhances campaign performance and efficiency, by continuously analyzing data and making real-time adjustments, allowing teams to focus on strategy and creative development instead of manual tasks.
Key features
Autonomous ad management - Albert independently manages media across platforms like Google Ads, Facebook, Instagram, and YouTube, optimizing bids and campaign designs to maximize ROI.
Real-time analytics and insights - It provides real-time data and insights, enabling marketers to make informed decisions and quickly adapt to changing market conditions.
Personalization at scale - The platform personalizes ads for every audience segment, regardless of how small, using real-time insights and historical data for optimal results.
Pricing
Albert doesn’t disclose its pricing.
Contact its sales team to get a custom quote based on your advertising budget and goals.
Pros & Cons
✅ Integrates with all the major advertising platforms.
✅ Uses machine learning to learn and adjust to relevant changes on the fly.
❌ More suitable for larger organizations with substantial advertising budgets.
3. AdCreative
Best for: Quickly generating high-converting ad creatives using AI.
Who is it for: Marketers, agencies, and e-commerce businesses seeking to streamline ad creation and boost campaign performance.
AdCreative is an AI-powered platform designed to automate the creation of ad creatives, including banners, videos, and text, tailored for various advertising channels.
By leveraging machine learning trained on a vast database of high-performing ads, it enables users to produce data-backed creatives quickly, aiming to boost conversion rates and reduce the time spent on manual design tasks.
Key features
Uses AI to generate ad creatives - Lets you easily produce conversion-focused ad visuals in multiple formats and sizes suitable for platforms like Facebook, Instagram, Google Ads, and more.
Creative scoring - Allows you to assess the potential performance of each creative with AI-driven scoring, helping you prioritize assets likely to yield better results.
Stock image access - Provides access to a vast library of over 100 million royalty-free images to further improve ad visuals.
Pricing
AdCreative has four pricing plans:
Starter: Starting at $39 and going up to $189 depending on the number of ad downloads you want, includes 2 users and 1 brand, in addition to access to all the platform’s AI features.
Professional: Starting at $249/mo and going up to $499/mo, includes 20 users, 3 brands, and access to Pro feature toolkit.
Ultimate: Starting at $599/mo and going up to $1099/mo, includes 50 users and 10 brands.
Enterprise: Custom pricing, has custom limits and advanced security features.
Pros & Cons
✅ Data-driven approach that helps in crafting high-performing ads.
✅ User-friendly interface suitable for users with varying levels of design expertise.
❌ Customization options are limited compared to traditional design tools.
4. Proxima
Best for: Predictive audience targeting to improve ad efficiency and ROI.
Who is it for: Consumer brands and performance marketers aiming to scale customer acquisition while reducing costs.
Proxima is a predictive data intelligence platform designed to help consumer brands identify and engage high-value customers through AI-driven insights.
By analyzing vast datasets, Proxima enables marketers to optimize their advertising strategies, ensuring that campaigns reach the most receptive audiences.
This approach not only improves conversion rates but also enhances overall marketing efficiency.
Key features
Predictive audience scoring - Uses machine learning to score and segment audiences based on their likelihood to convert, allowing for more targeted and effective ad campaigns.
Real-time optimization - Continuously analyzes campaign performance data to provide actionable insights, facilitating timely adjustments for improved results.
Data security - Ensures robust protection with end-to-end encryption.
Pricing
Proxima has two pricing plans:
Starter: $119/mo, includes basic audience categories, unlimited users, weekly seed refreshes, etc.
Pro: N/A, everything in Starter, plus advanced AI audiences, customer insights, Meta industry trends, performance benchmarks, etc.
There’s also a 30-day free trial that lets you try Proxima on for size.
Pros & Cons
✅ Reduces customer acquisition costs through efficient targeting.
✅ Powered by constantly learning AI algorithms that enable real-time campaign optimization.
❌ Primarily tailored for e-commerce brands on Shopify and Meta, potentially limiting usability for B2B marketing.
5. Jacquard
Best for: Crafting hyper-personalized, brand-compliant messaging at scale across digital channels.
Who is it for: Enterprise marketing teams aiming to improve customer engagement through AI-driven, contextually relevant communications.
Jacquard is an AI-powered platform designed to automate the creation of contextually relevant, on-brand marketing messages tailored to individual consumers.
Building upon the legacy of Phrasee, Jacquard leverages advanced natural language generation and predictive analytics to produce high-performing content across email, SMS, push notifications, and other digital channels.
Its architecture combines generative AI with deterministic controls, ensuring brand consistency while delivering personalized experiences at scale.
Key features
Language² engine - Generates thousands of brand-compliant message variants in seconds, calibrated for tone, structure, and compliance, without the need for prompt engineering.
Neural² predictive analytics - Predicts the performance of messaging variants before deployment, selecting top-performing content based on extensive training data from over a decade of campaigns.
Contextual¹ personalization engine - Leverages multi-agent systems to create hyper-personalized messages by analyzing user data, contextual factors, and product information, moving beyond basic personalization tactics.
Pricing
Jacquard has two tiers:
Base Platform: Starting at $24k/year, includes one brand, language, integration, business unit, and 120 UMVs.
Growth Tiers: Custom pricing, based on your requirements.
Note: UMV (Unique Message Variant) is the ‘Unit of Work’ in Jacquard. “Work” goes into creating UMVs (i.e. generation, calibration, performance prediction) and they perform work when they are sent to people (i.e. performance, diversity, learning).
Pros & Cons
✅ Delivers personalized, high-performing content at scale, boosting customer engagement.
✅ Ensures brand consistency across all messaging through deterministic controls.
❌ Primarily tailored for enterprise-level organizations, cost-prohibitive for smaller businesses.
6. Birch
Best for: Advanced ad automation and cross-platform campaign management.
Who is it for: Marketing teams and agencies aiming to streamline ad operations across platforms like Meta, Google, TikTok, and Snapchat.
Birch is an AI-driven ad automation platform designed to simplify and enhance digital advertising efforts.
By offering customizable automation rules, real-time performance analytics, and seamless integrations, Birch enables marketers to efficiently manage and optimize campaigns across multiple advertising platforms.
Key features
Customizable automation rules - Lets you set up complex, condition-based rules to automate tasks such as bid adjustments, budget allocations, and pausing underperforming ads, reducing manual workload.
Creative and audience insights - Provides tools like Ads Explorer that identify top-performing ads, current trends, and fatigued ads, helping with data-driven decision-making.
Real-time alerts and reporting - Triggers instant notifications via Slack or email about significant campaign changes, and generates detailed reports to monitor performance.
Pricing
Birch has three pricing plans:
Essential: Starting at $49/mo for up to $10k monthly ad spend, includes smart rules, post boosting, reports, Slack integration, etc.
Pro: Starting at $99/mo for up to $10k monthly ad spend, includes everything in Essential, plus automated rules and strategies, Ads Explorer, top creatives and audiences, etc.
Enterprise: Custom pricing, includes everything in Pro, plus premium support and tech setup help.
Keep in mind that the price of the Essential and Pro plan can go up to $499/mo and $1,799/mo, depending on your monthly ad spend.
Pros & Cons
✅ Provides comprehensive insights into ad performance and audience engagement.
✅ Lots of customization options that enable you to create custom automated workflows.
❌ Has a learning curve, especially when it comes to more advanced features.
7. Neural.love
Best for: AI-powered creative asset generation and multimedia enhancement.
Who is it for: Content creators, marketers, and small businesses seeking to produce high-quality visuals and audio without extensive technical expertise.
Neural.love is an AI-driven platform offering a suite of tools for generating and enhancing digital media.
Although this isn’t an advertising platform in itself, it can be used to create various media types, such as unique images and videos from text prompts, as well as upscale and restore photos and videos, and improve audio quality.
The platform emphasizes user-friendly design and privacy, ensuring that all files are stored securely and not used for AI training purposes.
Key features
AI Art Generator - Transforms text prompts into unique artworks across various styles, including photorealistic images, anime, fantasy, and more.
Image enhancement tools - Lets you improve image quality by upscaling resolution up to 4x, colorizing black-and-white photos, restoring old images, and refining facial details.
Convert image to video - Lets you transform static images into captivating MP4 videos without any video editing experience.
Pricing
Neural.love has a free forever plan that provides limited AI tool access, watermarked content, basic image edit and text generation tools.
This plan, however, omits video editing features.
If you want to generate and edit videos and access more advanced AI functionality, there are two plans to choose from:
Pro: Starting from €10 for 100 credits + 0,10/credit and goes up to €482 for 6,000 credits + €0,08/credit, includes everything in Free, plus access to all AI tools.
Pay As You Go: No monthly fee, the pricing starts at €0,19/credit and can go down to €0,13/credit if you get 6000+ credits.
Pros & Cons
✅ User-friendly interface suitable for both beginners and professionals.
✅ Supports a wide range of creative and enhancement tools.
❌ No video editing features on the free plan.
8. Madgicx
Best for: AI-powered Meta ad optimization with advanced automation and creative tools.
Who is it for: E-commerce brands, agencies, and media buyers aiming to maximize ROI through intelligent ad management.
Madgicx is an AI-driven advertising platform designed to streamline and enhance Meta (Facebook and Instagram) ad campaigns.
It combines automation, creative insights, and performance analytics to help marketers optimize their advertising efforts efficiently.
Key features
AI Marketer - Acts as a personal AI media assistant, providing daily optimization recommendations based on account data to improve ad performance.
Automation templates - Offers pre-built automation strategies like 'Stop Loss' and 'Surf' to manage budgets and scale campaigns effectively.
Ads Manager 2.0 - Provides a comprehensive view of campaign performance, allowing for real-time adjustments and asset management.
Pricing
Madgicx one main product package that includes all the platform’s essential features:
All-in-One: Starting at $39/mo for less than $1k monthly as spend and can go up to $435/mo for a spend of $50k-$100k, includes automation, ad management, analytics, and targeting.
There are also two optional add-ons:
2. Ad Library + AI Ad Generator: $29/mo, provides access to an ad library with 2M+ ads and an integrated AI ad generator.
3. One-Click Report: $29/mo, lets you build your bespoke dashboard for multi-channel performance reporting across Facebook, Google, and Shopify.
If you want to try it before committing, there’s a 7-day free trial.
Pros & Cons
✅ Real-time ad bidding optimization.
✅ Advanced ad analytics and insights.
❌ Many of its AI features are still in the development process, which makes them unstable and glitchy.
9. Pencil
Best for: AI-powered ad generation with predictive performance insights.
Who is it for: Brands and agencies aiming to scale creative production and optimize ad performance across platforms.
Pencil is an AI-driven platform designed to streamline the creation of high-performing video and image ads.
By leveraging generative AI, Pencil enables users to produce ad creatives rapidly, reducing production time and costs.
Additionally, the platform offers predictive analytics to forecast ad performance, allowing marketers to make data-informed decisions before launching campaigns.
Key features
Generative AI for ad creation - Lets you use AI to generate video and image ads quickly and seamlessly, enhancing creative efficiency.
Predictive performance insights - Provides analytics that predict ad success, aiding in selecting the most effective creatives.
Collaborative tools - Has features that enable the whole team to work together on creating, approving, and launching ads.
Pricing
Pencil has two essential pricing plans:
Basic: Starting at $14/mo for 50 ad generations and goes up to $186/mo for 1000 generations, includes AI Magic Tools to generate text, images, video and ad creatives, real-time AI insights and performance predictions, 1 brand, etc.
Pro: Custom pricing, includes everything in Basic, plus bulk AI generation using feeds across audiences, markets & SKUs, unlimited users, brands & markets with entity & role access controls, etc.
There’s also a wide range of add-ons you can purchase on the Pro plan for an additional cost, such as managed GenAI Studio from 3 GenAI-first strategists, creators & creative directors, managed fine-tuning of AI models on your data, etc.
Note: The Pro plan is annual only.
Pros & Cons
✅ Lets you generate compelling ads using text prompts only.
✅ Supports a wide range of advertising platforms for broad reach.
❌ Editing features have a learning curve.
10. Adzooma
Best for: Effortless, cross-platform PPC campaign optimization based on granular performance insights.
Who is it for: PPC marketers, SMBs, and agencies that want to streamline ad performance across Google, Microsoft, and Meta.
Adzooma is a user-friendly PPC optimization platform that acts like a virtual assistant for digital advertisers.
Designed to simplify and enhance campaign performance across Google, Microsoft, and Facebook Ads, Adzooma delivers actionable insights, performance reports, and smart recommendations all from one centralized dashboard.
Whether you’re managing one account or many, it helps you save time, boost efficiency, and make smarter decisions with less effort.
Key features
Performance reports - Instantly analyzes your ad accounts with reports that surface critical issues, flag opportunities, and suggest optimizations.
Opportunities engine - Provides daily, personalized recommendations based on real campaign performance.
SEO and web metrics reporting - Goes beyond PPC with additional reports that surface SEO insights and website performance data, helping you align paid and organic strategies for better ad relevance and higher conversions.
Pricing
Adzooma has a free forever plan that includes monthly PPC performance reports, monthly opportunity analysis, monthly SEO/web metrics reports, etc.
For more, you can subscribe to one of two paid plans:
Silver: $69/mo, includes weekly PPC performance reports, weekly opportunity analysis, weekly SEO/web metrics reports, unlimited users, etc.
Gold: $179/mo, includes daily PPC performance reports, daily opportunity analysis, daily SEO/web metrics reports, and everything in Silver.
Pros & Cons
✅ Great for beginners and pros alike with a clean, intuitive interface.
✅ Combines ad, SEO, and web performance metrics in one place
❌ Best suited for small to mid-sized teams, as larger orgs may need more granular controls.
Next steps: Make your ad strategy smarter
You’ve seen what the top AI advertising tools can do, and now it’s time to put them to work.
Whether you're streamlining creative production, optimizing ad spend, or finally nailing intent-based targeting, the right AI platform can be the edge your campaigns have been missing.
And if precision targeting is your priority, Warmly is the place to start.
With real-time buyer signals, automatic audience orchestration, and hyper-relevant messaging, Warmly helps you show the right ads to the right people exactly when it matters most.
The best tool depends on your specific needs, budget, and use case. Review the detailed comparisons above to find the right fit. For website visitor identification and engagement, Warmly is a top choice.
How do I choose a ai for advertising tool?
Consider factors like features, pricing, integrations, data quality, and ease of use. Test free trials when available. Prioritize tools that integrate with your existing tech stack.
What should I look for in ai for advertising software?
Key criteria include core features for your use case, pricing transparency, customer support quality, and proven results. See the evaluation criteria above for a complete checklist.
Is there a free ai for advertising tool?
Many tools offer free tiers with limited features. Check the pricing sections above for free options. Note that free plans often have significant limitations for business use.
Which ai for advertising tool has the best ROI?
ROI depends on your specific situation and how you use the tool. Tools like Warmly that identify website visitors and automate engagement often show strong ROI by converting existing traffic.
10 Best AI Sales Calls Tools & Software [2026]
Time to read
Alan Zhao
AI is changing the way sales calls happen in 2026 at lightning speed.
Instead of scattered notes, awkward silences, or missed opportunities, reps now show up with smart AI sales calls tools that listen, summarize, and even coach in real time.
From live call analysis to automatic follow-ups, AI sales call tools are giving revenue teams a serious edge by:
Boosting close rates.
Shortening sales cycles.
Freeing reps to focus on relationships instead of administrative tasks.
But with dozens of new tools on the market, how do you know which ones are actually worth your team’s time (and budget)?
By doing thorough research, going through each platform’s features and pros and cons, and singling out the best-performing tools, which is exactly what I did so you wouldn’t have to.
In this guide, I’ll break down the 10 best AI sales call tools and software in 2026, focusing on what they do, who they’re for, and why they matter, so you can find the right fit to power your next conversation.
TL;DR
Warmly offers the best AI sales call tool in 2026 with its real-time lead engagement, AI Copilot for personalized outreach, and AI SDR agents that handle prospecting and follow-ups autonomously.
Intelligence-driven tools like Gong and Clari are ideal for sales teams seeking deeper call insights, coaching support, and improved forecasting accuracy.
On the other hand, there are tools like Synthflow, Avoma, and Fireflies that can help you automate transcription, note-taking, and call summaries, freeing up your reps to focus on closing deals instead of admin work.
Key benefits of using AI sales call tools and software
AI sales call tools aren’t just about saving time.
They’re about closing smarter, coaching faster, and scaling conversations that convert.
Here are the biggest advantages teams are seeing in 2026 that make for enough of a reason for anyone to start implementing them in their sales operations.
1. Better call preparation
AI tools can surface key intel, like past interactions, company insights, or decision-maker details, before the call even starts.
That means reps go in confident and context-aware, knowing exactly what to say and how to present it.
2. Real-time support during calls
Whether it’s objection handling prompts or live talk ratio tracking, real-time insights help reps adjust on the fly and keep calls on track.
This means no more awkward silences, stutters, or messed-up objection handling.
3. Less manual follow-up work
No more scrambling to write down next steps.
Some tools offer auto-summaries and CRM sync, cutting down admin time and ensuring nothing falls through the cracks.
4. Faster onboarding
New reps learn by doing, with AI significantly speeding up the process by giving them coaching and feedback after every call.
That shortens ramp time and helps teams hit quota sooner.
5. Improved consistency across teams
AI ensures every call is tracked, measured, and improved.
You get a repeatable process instead of guesswork and more reliable pipeline coverage.
6. Data-driven coaching
Rather than anecdotal feedback, managers can coach based on real patterns and performance trends.
That means more targeted support and better rep outcomes.
What are the best AI sales call tools and software in 2026?
The best AI sales call tools on the market include Warmly, Gong, and Clari.
Here are the 10 best AI sales call tools & software on the market after evaluating 30+ tools:
Warmly: AI-driven agents that detect buying signals and initiate outreach on your behalf, looping in human reps when the time is right.
Gong: Provides conversational intelligence by analyzing sales calls to deliver insights that improve team performance and close rates.
Clari: Uses AI to provide real-time coaching and insights, offering instant visibility into sales calls and potential risks.
Synthflow AI: Offers customizable AI voice agents that handle both inbound and outbound calls, automating tasks like lead qualification, appointment scheduling, and customer support.
Avoma: An all-in-one AI meeting assistant that automates note-taking, call transcription, and provides conversation intelligence to enhance sales productivity.
Docket: Acts as an AI sales engineer, providing real-time technical support during sales calls.
Otter.ai: Introduces AI meeting agents that transcribe, summarize, and provide actionable insights from sales meetings.
Uniphore: Delivers real-time AI assistance during calls, analyzing customer emotions and intent to guide sales conversations.
Fireflies.ai: Automates transcription and sales calls analysis for better informed follow-ups and insights.
Alta: Provides AI agents that automate prospecting, research, outreach, and meeting scheduling, integrating seamlessly with various business tools.
1. Warmly
Best for: Real-time AI-driven lead engagement and conversion through personalized interactions.
Who is it for: B2B sales and marketing teams aiming to identify, engage, and convert high-intent leads efficiently.
Warmly is an AI-powered sales platform built to help B2B teams identify high-intent leads, engage them in real time, and convert them into pipeline without the usual guesswork.
From instant video chats to personalized outreach and AI-powered SDR agents, Warmly acts as a full-stack co-pilot that makes your team faster, smarter, and more human.
Let’s get a closer look at some of its key features.
Feature #1: Warm Video Call
Warmly’s Warm Video Callfeature is built for one thing: helping sales teams connect with high-intent leads at the exact right moment - when interest is fresh and timing is critical.
Here’s how it works.
Once a warm lead lands on your website, instead of letting them go cold, Warmly’s AI Chat engages them in a personalized, relevant way bound to resonate with them.
At the same time, Warmly’s AI Copilot agents monitor and analyze leads’ behavior in real-time, looking for signs of high intent and readiness to convert.
Once the Copilot detects an ICP-matching lead engaging with Warmly’s AI-driven chatbot on your website in a meaningful way, it instantly loops in human reps, enabling them to take over the conversation.
And the best part is that it doesn’t stop at chat.
With one seamless click, Warmly transitions warm conversations into face-to-face video calls, making it easier for reps to build trust, answer questions, and move the deal forward while the lead is still hot.
And all that without leaving Warmly’s dashboard or juggling multiple tools.
The result?
More genuine human connections, better-qualified pipeline, and a dramatically higher chance of turning site visitors into booked meetings.
You can try Warmly’s Warm Video Call feature here:
Feature #2: AI Copilot
Personalized outreach works - but not if it takes hours of manual research for every lead.
In addition to looping reps in warm chats, the Copilot also:
Surfaces the right accounts.
Explains why they’re a good fit.
Suggests tailored messaging that’s relevant to each buyer’s context (e.g., depending on whether they’re a returning website visitor or part of a new outbound sequence, etc.).
It pulls insights from real-time behavior, CRM data, firmographics, and intent signals to ensure every touchpoint feels relevant, timely, and authentic.
This provides reps with tons of extra help before the sales call actually happens, equipping them with all the information they need to be convincing and confident.
As a result, with Warmly’s AI Copilot, reps don’t just reach out faster - they show up smarter.
And that means less spam, more real conversations, and better outcomes across the board.
Feature #3: AI SDR
While the AI Copilot helps your team reach out with confidence, Warmly’s AI SDR takes it even further by doing the groundwork for you and warming leads up before your reps even step in.
Operating 24/7, Warmly’s agentic SDRs initiate personalized conversations with prospects across email, LinkedIn, and live chat.
These AI-powered assistants engage new leads the moment they show interest, qualify them based on real-time data, and nurture them with follow-ups designed to keep the momentum going.
Whether it’s a first touch or reactivation of a stale lead, Warmly’s SDRs handle the repetitive (but critical) parts of outbound so your human team can focus on closing, not chasing.
The result? Massive productivity gains, more qualified meetings booked, and a higher-quality pipeline, making it that much easier for human reps to actually score.
It’s scalable sales development, powered by AI and grounded in real human intent.
Feature #4: Marketing Ops Agent
The best GTM strategy is meaningless if you don’t have the right target list of companies and people.
It’s an easy-to-use platform that automatically builds and maintains real-time, up-to-date lists of your best-fit accounts and contacts, so your GTM team can focus on strategy and creativity instead of spreadsheets.
Here's how it works:
The Marketing Ops Agent connects directly to your CRM, ad platforms, outbound tools, and intent sources to automate key targeting workflows:
AI-powered lead scoring and enrichment to create dynamic lists ranked by fit and buying intent.
Buying committee identification using insights from past closed-won deals.
Instant audience syncing with HubSpot, Marketo, LinkedIn, Meta, Outreach, and more. No more manual CSV uploads.
Automatic list updates as new intent signals or engagement changes occur.
Unified buying intent scores that stay synced across every sales and marketing channel.
Pricing
Warmly’s pricing is modular and component-based and comes with a free plan that lets you identify up to 500 website visitors per month.
You can choose the components that best match the needs of your business, as components are priced by output and not just by a monthly fee.
There are 4 paid product plans that you can choose from:
AI Data Agent (Starts at $10,000/yr), which includes 10,000 credits, person-level web visitor de-anonymization (RB2B + Vector data), CRM integration, and access to our Coldly Contact Database.
AI Inbound Agent (Starts at $16,000/yr), which adds a native marketing outbound automation, domain warmup, and lead routing with custom CRM fields.
AI Outbound Agent (Starts at $22,000/yr), which adds automated signal-based outbound orchestration, AI chatbot & live video chat, email automation, LinkedIn automation, and email warmup.
Marketing Ops Agent (Starts at $25,000/yr), which adds AI-powered account scoring, AI enrichments and custom signals, buying committee identification, real-time buying intent signal tracking, and automatic updates across all enrichments, signals, account and lead lists.
Pros & Cons
✅ Identifies and engages high-intent leads in real-time.
✅ Instantly transitions conversations from chat to video for stronger, face-to-face rapport.
✅ Real-time signal tracking helps reps prioritize the right leads at the right time, allowing you to beat your competition to the punch.
✅ Eliminates guesswork with AI Copilots that tell reps who to reach, why, and what to say.
✅ 24/7 AI SDRs handle outreach, lead nurturing, and follow-ups, with no human rep needed.
✅ AI identifies your true ICP based on granular intent data and actual behavior, not just firmographics.
✅ Seamless Slack integration keeps reps in the loop the moment a hot lead appears.
✅ Personalized conversations across email, LinkedIn, and live chat automated at scale.
❌ Its most advanced features are available only on the paid plans.
2. Gong
Best for: AI-powered conversation intelligence that transforms sales calls into actionable insights.
Who is it for: Sales teams and managers aiming to enhance performance through data-driven coaching and deal analysis.
Gong is one of the leading revenue intelligence platforms on the market, designed to capture, analyze, and turn every sales interaction into a goldmine of insights.
By leveraging AI to surface deal risks, highlight coaching opportunities, and track pipeline health, Gong helps sales teams close more deals with less guesswork.
Key features
Advanced call recording and transcription - Automatically captures and transcribes sales calls, providing a searchable database for review.
AI-driven coaching - Provides personalized feedback and performance metrics to sales reps, driving targeted improvements.
Call Spotlight - Generates concise summaries highlighting key discussion points and actionable next steps from recorded calls.
Pricing
Gong doesn’t have fixed subscription fees.
Instead, it urges potential users to complete a simple questionnaire and specify their team size and requirements to get a custom quote.
Pros & Cons
✅ Enhances sales coaching by pinpointing specific areas for improvement based on call analysis.
✅ Improves forecast accuracy through AI-driven deal insights and predictive analytics.
❌ Some users report delays in call processing and transcription availability.
3. Clari
Best for: AI-powered revenue intelligence that enhances sales call effectiveness and forecasting accuracy.
Who is it for: Sales and revenue teams seeking to improve deal execution, coaching, and pipeline management through actionable insights.
Clari is a revenue intelligence platform that helps sales teams turn every conversation into a clear next step.
With powerful AI-driven insights from sales calls, emails, and CRM data, Clari gives reps and managers real-time visibility into deal health, coaching opportunities, and pipeline risk, so nothing slips through the cracks.
Key features
Clari Copilot - Automatically records, transcribes, and summarizes sales calls, providing actionable insights and next steps to improve sales performance.
Groove Dialer - Lets reps make calls directly from their workflow and allows managers to provide live coaching during calls,
Smart Deal Summaries - Aggregates emails, call transcripts, and notes into a single AI-curated interface, saving sales reps time on deal reviews.
Pricing
Clari doesn’t disclose its price or information about any distinct product packages.
You have to contact its team for a quote.
Pros & Cons
✅ Real-time AI-driven call summaries provide immediate, actionable insights from sales calls.
✅ Seamless Salesforce integration ensures that call data and insights are automatically synced with CRM records.
❌ Adjusting certain features or views often requires assistance from Clari's support team, making on-the-fly customization nearly impossible.
4. Synthflow
Best for: Deploying customizable AI voice agents to automate sales calls and enhance customer interactions.
Who is it for: Sales and support teams seeking to scale outreach and customer service without increasing headcount.
Synthflow AI is a no-code platform that lets you build and deploy AI voice agents to handle sales calls, lead qualification, appointment scheduling, and customer support 24/7.
Designed to sound human and act fast, Synthflow helps teams scale conversations without growing in size.
Key features
No-code AI voice agent builder - Lets you design and deploy AI-powered phone agents without any coding knowledge, streamlining the setup process.
24/7 multilingual support - Allows you to handle customer interactions around the clock in over 20 languages, ensuring global reach.
Real-time call analytics - Provides access to detailed insights into call performance, helping teams refine strategies and improve outcomes.
Pricing
Synthflow has five pricing plans:
Starter: $29/mo, includes 50 minutes and 5 concurrent calls, real-time booking, human transfer, etc.
Pro: Starts at $450/mo for 2,000 minutes, then $0.13/min, includes everything in Starter, plus workflow builder, team access, more concurrent calls, etc.
Growth: Starts at $900/mo for 4,000 minutes, then $0.12/min, includes everything in Pro, plus more calls and workflows, access to new features, etc.
Agency: $1,400/mo for 6,000 minutes, then $0.12/min, includes everything in Growth, plus more calls and workflows, white labeling, premium support, etc.
Enterprise: Custom, volume-based price, as low as $0.08/min, everything in Agency, plus more advanced security and customization features.
There’s also a 14-day free trial for Pro, Growth, and Agency plans.
Pros & Cons
✅ Lets you quickly and easily deploy AI voice agents without technical expertise.
✅ High voice quality.
❌ Has a learning curve for more advanced features.
5. Avoma
Best for: AI-powered meeting automation and sales call intelligence that improves rep productivity and call outcomes.
Who is it for: Sales teams, managers, and revenue leaders who want to streamline post-call workflows and drive performance with real-time insights.
Avoma is an AI meeting assistant and conversation intelligence platform that helps sales teams automate note-taking, extract insights from calls, and deliver personalized coaching.
As a result, reps can focus more on selling and less on manual tasks, with all relevant sales conversations being recorded and analyzed on autopilot.
Key features
AI meeting assistant - Automatically records, transcribes, and summarizes sales calls, providing structured notes and action items.
Real-time call analysis - Offers live insights during calls, helping reps adapt conversations on the fly.
AI call scoring - Evaluates rep performance with AI-generated scores based on customizable criteria.
Pricing
Avoma has three pricing plans:
Startup: $29/user/month, includes automatic video recording, unlimited real-time transcription, unlimited AI summary notes, etc.
Organization: $39/user/month, includes everything in Startup, plus custom AI topics & templates, smart playlist & AI automations, limited conversation intelligence, etc.
Enterprise: $39/user/month with 20 seats minimum, includes everything in Organization, plus designated Success Manager, advanced security, unlimited usage intelligence, etc.
In addition to these plans, Avoma also has three optional add-ons:
Conversation Intelligence: $35/user/mo, includes AI coaching recommendations, AI call scoring, smart trackers, and performance dashboards.
Revenue Intelligence: $35/user/mo, includes AI deal risks, sales methodology tracker, AI win loss analysis, and forecasting.
Lead Router: $25/user/mo, includes advanced routing rules, inbound form qualification, and outbound lead hand-off.
Pros & Cons
✅ Its AI-generated summaries capture all significant points, allowing users to focus on actual meetings instead of note-taking.
✅ Easy to integrate with existing sales tools and workflows.
❌ Transcription accuracy may occasionally be messed up due to poor audio or accents.
6. Docket
Best for: Automating technical sales support and streamlining RFP responses with AI-driven precision.
Who is it for: Sales teams seeking to enhance efficiency and accuracy in technical sales calls and other customer interactions.
Docket is an AI-powered sales enablement platform that acts like a virtual sales engineer, answering technical questions, generating documents, and speeding up RFPs.
By tapping into its proprietary Sales Knowledge Lake that contains all the info on your product, Docket helps sales teams deliver fast, accurate, and consistent responses without relying on technical staff, making each sales call flow more smoothly.
Key features
AI Sales Engineer - Delivers instant, verified answers to complex technical questions, reducing dependency on human sales engineers.
Sales Knowledge Lake - Centralizes and continuously updates sales knowledge from various sources, ensuring consistent and accurate information.
Enterprise-grade security - Complies with SOC 2 Type II, GDPR, and ISO 27001 standards, ensuring data protection and privacy critical for handling highly sensitive data.
Pricing
Docket doesn’t publish any information regarding its pricing or packages.
You can book a demo to get more details.
Pros & Cons
✅ Reduces technical query response time, providing answers instantly.
✅ Improves onboarding and training by centralizing sales knowledge.
❌ May not fully replace the nuanced expertise of human sales engineers in highly complex scenarios.
7. Otter
Best for: Automating sales call transcriptions and generating actionable insights to streamline sales processes.
Who is it for: Sales teams and professionals seeking to improve efficiency and accuracy in capturing and using information from sales calls.
Otter is an AI-powered meeting assistant designed to transcribe, summarize, and extract actionable insights from sales calls in real time.
With its specialized AI Sales Agent, Otter enhances sales productivity by automating note-taking, CRM updates, and follow-up communications.
Key features
Real-time transcription - Provides live transcriptions during meetings, ensuring accurate capture of conversations.
AI Chat - Allows users to query past meetings and generate content like follow-up emails based on them.
Extracts sales insights - Highlights critical sales metrics and key points, such as BANT qualifications, during calls.
Pricing
Otter.ai has four tiers:
Basic: Free forever, provides 300 monthly transcription minutes and max 30 minutes per conversation, lets you import and transcribe 3 audio or video files lifetime per user, and includes essential features.
Pro: $16.99/user/mo, more advanced features and more minutes.
Business: $30/user/mo, everything in Pro, plus more minutes and admin features.
Enterprise: Custom price, everything in Business, plus more advanced security features.
Pros & Cons
✅ Lets you search through transcripts, enabling you to locate specific information quickly.
✅ User-friendly interface.
❌ Sometimes struggles to accurately identify and label speakers, leading to potential confusion in transcripts.
8. Uniphore
Best for: Real-time sales coaching and sentiment analysis to improve customer engagement and sales outcomes.
Who is it for: Sales teams and managers seeking to leverage AI for deeper insights into customer interactions and to enhance sales performance.
Uniphore is a conversational AI platform that enhances sales interactions by analyzing customer sentiment, providing real-time guidance, and automating post-call tasks.
It has several distinct products that allow you to capture conversations, analyze them, and provide reps with real-time AI agent assistance.
Key features
Enterprise recording - Captures conversations in high-quality AI-ready format with built-in audit and filtering capabilities.
GenAI-powered conversational intelligence - Analyzes the tone and sentiment of the customer and the agent during conversations, monitors and scores quality on autopilot, and lets you extract insights by simply asking questions in natural language.
Real-time AI sales assistant- Guides agents in real-time with next-best actions and automated sales workflows triggered by live intent detection, delivers instant answers during calls, provides coaching tips, etc.
Pricing
Uniphore doesn’t disclose any information regarding its pricing plans.
It’s best to contact its sales team for more details.
Pros & Cons
✅ The platform efficiently generates concise summaries and action items after each call.
✅ Detailed analytics and performance metrics allow managers to provide targeted coaching to sales reps.
❌ Non-transparent pricing.
9. Fireflies
Best for: Automating sales calls transcription, and analysis to extract key insights and action items.
Who is it for: Sales teams that want to gain data-driven insights into sales calls to enhance sales strategies.
Fireflies is an AI meeting assistant that records, transcribes, and analyzes sales calls to surface key insights and action items.
It helps sales teams streamline follow-ups, improve performance, and focus on closing deals rather than note-taking.
Key features
Automatic call recording and transcription - The platform automatically records and transcribes meetings across various conferencing platforms, ensuring accurate documentation of sales calls.
AI-powered search - You can quickly search through transcribed conversations to find specific topics, questions, or action items, saving time on manual note review.
Analytics dashboard - Provides granular insights into conversation metrics, such as talk-to-listen ratios and meeting participation, helping teams refine their communication approaches
Pricing
Fireflies has a free forever plan with unlimited transcriptions, limited AI summaries, 800 minutes of storage per seat, and access to some of its basic features.
More advanced users can choose from three paid plans:
Pro: $18/user/mo, includes everything in Free, plus talk-time analytics, AI apps, etc.
Business: $29/user/mo, includes everything in Pro, plus video recording, conversation intelligence, etc.
Enterprise: $39/user/mo, includes everything in Business, plus more advanced security and compliance features.
Best for: Automating sales calls and prospecting to enhance pipeline generation and sales efficiency.
Who is it for: B2B sales and revenue teams aiming to scale outreach and optimize operations by leveraging autonomous AI agents.
Alta is an AI-powered revenue workforce platform that automates sales calls, prospecting, and revenue operations through specialized agents.
Its AI agents - Katie (SDR), Alex (Calling), and Luna (RevOps) - work 24/7 to streamline sales processes, allowing teams to focus on closing deals.
Key features
AI calling agent (Alex) - Fully customizable agent that automates outbound and inbound sales calls, scoring leads, booking meetings, and updating CRM systems in real-time.
AI SDR agent (Katie) - Identifies buying signals, researches prospects, and executes personalized multichannel outreach across email, LinkedIn, and calls, preparing prospects for further nurturing.
Real-time analytics - Offers comprehensive dashboards to monitor performance metrics, analyze patterns, and optimize strategies.
Pricing
Alta doesn’t have fixed fees.
Instead, its pricing is tailored to each individual user based on their bespoke needs.
To get a custom quote, fill out their website form.
Pros & Cons
✅ High-quality AI-generated voices mimic human voices almost perfectly.
✅ Its AI agents work around the clock, increasing productivity and ensuring no opportunity is missed.
❌ Some users mention it can be difficult to fully customize Alta’s agents to fit specific business needs.
Next steps: Make sure every sales call counts
One thing is clear - the way sales teams approach calls is changing fast.
With the right AI tools in place, reps aren’t just showing up. They’re showing up informed, supported, and ready to close.
From real-time insights and automated summaries to full-blown AI agents that handle research and outreach, today’s best platforms both optimize and transform sales calls.
If you're ready to turn conversations into conversions, book a demo with Warmly and see how AI can help your team have smarter, more impactful calls every single time.
The best tool depends on your specific needs, budget, and use case. Review the detailed comparisons above to find the right fit. For website visitor identification and engagement, Warmly is a top choice.
How do I choose a ai sales calls tool?
Consider factors like features, pricing, integrations, data quality, and ease of use. Test free trials when available. Prioritize tools that integrate with your existing tech stack.
What should I look for in ai sales calls software?
Key criteria include core features for your use case, pricing transparency, customer support quality, and proven results. See the evaluation criteria above for a complete checklist.
Is there a free ai sales calls tool?
Many tools offer free tiers with limited features. Check the pricing sections above for free options. Note that free plans often have significant limitations for business use.
Which ai sales calls tool has the best ROI?
ROI depends on your specific situation and how you use the tool. Tools like Warmly that identify website visitors and automate engagement often show strong ROI by converting existing traffic.
10 Ways To Use AI For Sales Enablement In 2026
Time to read
Chris Miller
AI isn’t just transforming how we sell - it’s transforming how we enable sales.
In 2026, sales enablement is no longer just about decks, playbooks, and training.
It’s about giving reps real-time intelligence, content tailored to the moment, and tools that help them close faster with less guesswork.
That’s where AI sales enablement comes in.
From surfacing the right collateral mid-call to predicting which deals need attention (and why), AI is reshaping how revenue teams operate, making them smarter, faster, and more focused.
In this article, I’ll break down the most powerful, practical ways AI is leveling up sales enablement that work equally well for enabling a team of 5 or 500.
The results of applying these use cases yourself? More pipeline, higher win rates, and getting more out of every rep without burning them out.
Let’s dive in.
How is AI being used for sales enablement in 2026?
In 2026, AI has evolved from a supplementary tool to a central component in sales enablement strategies, driving faster onboarding, smarter selling, and more productive teams.
Namely, sales enablement isn’t just about content libraries and rep training anymore.
It’s about intelligence. Context. Timing. And delivering the right support at every stage of the deal cycle, which is exactly what AI enables.
AI is being woven into the sales stack in ways that help reps spend more time selling and less time searching, guessing, or second-guessing.
It powers everything from intelligent coaching and knowledge surfacing to deal prioritization and pipeline analysis, quietly working behind the scenes to boost rep confidence and performance.
For enablement leaders, this shift means moving beyond static playbooks.
With AI, enablement becomes dynamic: adapting to buyer behavior, surfacing what works, and optimizing as you go.
In the next section, we’ll break down exactly how teams are using AI across the enablement journey from onboarding and training to active deal support and beyond.
What are the benefits of using AI for sales enablement?
When deciding whether to apply new technology to tested and tried systems, there’s one question everyone wants answered first:
What’s in it for me?
Well, implementing AI in sales enablement offers numerous advantages, including:
Increased efficiency - Automating routine tasks and providing real-time insights streamlines the sales process, allowing teams to accomplish more in less time.
Improved personalization - AI's ability to analyze vast amounts of data enables highly personalized interactions, enhancing customer satisfaction and loyalty.
More precise lead scoring and qualification - AI can detect hot leads that fit in your ICP faster and with higher precision than human reps, especially on scale.
Enhanced decision-making - Predictive analytics and data-driven insights support strategic planning and resource allocation.
Accelerated onboarding - Personalized training programs reduce ramp-up time for new hires, ensuring they contribute effectively sooner.
Consistent messaging - AI ensures that all sales materials and communications align with the company's messaging and branding, maintaining consistency across the board.
Makes every rep your best performer - With AI-driven real-time recommendations and suggestions, every rep, from newbies to veterans, can excel in wowing leads and closing more deals.
So, it’s fair to say that by integrating AI into sales enablement, organizations position themselves to respond swiftly to market changes, meet customer expectations, and drive sustained growth.
Top 10 use cases of AI in sales enablement
Now that we’ve covered how AI is reshaping sales enablement and why it matters, let’s look at what that actually looks like in practice.
In 2026, AI is being deployed across the entire enablement journey: surfacing deal insights, scoring leads, guiding reps mid-call, and even reviving pipeline that would’ve been left for dead.
These aren’t just optimizations - they’re strategic advantages.
So, let’s check out the ten real-world use cases where AI is making a measurable impact from enhanced lead generation to better rep support.
1. Smarter lead scoring that actually shows you who’s worth your time
Traditional lead scoring has always had a problem: it’s either too simple (based on basic firmographics), too static (set-it-and-forget-it rules), or too slow to reflect real buying intent.
And in today’s market, that’s not good enough.
Modern sales teams need lead scoring that’s dynamic, data-rich, and context-aware because buyer behavior moves fast, and so should your prioritization.
That’s where AI changes the game.
By continuously analyzing behavioral signals, engagement patterns, company fit, and even deal progression trends, AI can help reps instantly identify which leads are worth pursuing and which ones aren’t ready yet.
It doesn’t just rank leads by static rules - it learns who your best customers actually are, in depth.
Using AI-powered ICP identification, it analyzes more than just titles or industries.
It maps the deep, shared traits of your highest-value customers - things like buying behavior, timing patterns, and engagement signals - and then scores new leads against that model in real time.
And when those high-fit leads start showing intent? You’ll know right away.
Warmly actively monitors dozens of warm lead signals from site visits to content engagement and enriches them with data from 10+ providers.
That means you’re not just scoring based on assumptions. You’re scoring based on actual, verified behavior.
And to make sure no opportunity slips through, Warmly routes hot leads directly to the right reps with instant alerts in Slack or your CRM.
The result: no more delay, no more guesswork, just focused action on the leads most likely to convert.
2. Intent signal tracking that tells you who’s ready - and why
Instead of waiting for leads to fill out forms or book a demo, the best sales teams today use AI to spot intent before the prospect ever reaches out.
AI-powered intent tracking helps reps understand who’s in market, what they care about, and when to engage.
That means fewer cold starts and more conversations that actually go somewhere.
Warmly gives teams a real-time window into buyer intent - not just based on website visits or email clicks, but from a wide network of 1st, 2nd, and 3rd party signals.
This means you can:
Identify inbound leads on your website - In addition to revealing the companies and individuals visiting your website, Warmly also tracks how they interact with it in real-time, enabling you to pinpoint high-interest opportunities from the get-go.
Monitor social signals - Warmly’s Social Signals track posts, comments, likes, and shares across your ICP on LinkedIn, so you can spot prospects talking about pain points you solve, jumping into high-intent conversations, or even mentioning your brand directly.
Track research intent - The platform identifies companies researching competitors and topics and keywords relevant to your product, allowing you to reach those leads before the competition does and convert them.
This way, instead of blindly cold-emailing, reps can engage with full context and relevance.
You can also create intelligent segments based on these intent signals, combining them with enriched person-level data to triggertargeted nurture campaigns or outbound sequences in real-time.
The best part is that Warmly monitors these signals live.
When something changes, like a prospect revisiting your site or a key account spiking on a competitor keyword, Warmly flags it instantly, routes it to the right rep, and recommends the best next step.
This kind of always-on, AI-powered intent tracking means your team never misses a moment of buying interest.
Instead of chasing leads, you’re acting on them at the exact right time with the right context to convert.
3. Personalized outreach at scale without burning out your team
Buyers today can spot a generic sequence from a mile away.
“Spray and pray” outbound doesn’t just fall flat - it damages your brand.
To break through the noise, reps need to show up with timing, context, and relevance already baked in and AI makes that possible.
Modern AI-powered outreach platforms don’t just send more messages. They send better ones.
They track who’s engaging, what signals they’re showing, and what messaging will actually resonate.
The result? Personalized emails and LinkedIn touches that feel handcrafted but scale like automation.
This is exactly what Warmly’s AI Orchestratoris built to do.
It constantly monitors for on-site and off-site buying signals and intent and triggers hyper-personalized multichannel sequences without any manual lift from your team.
💡Note: You will need to be on one of Warmly’s paid plans to gain access to AI Prospector. For ARC, the ROI was 200% over 6 months.
For example, when a lead matching your ICP visits your pricing page, complains about a competitor on LinkedIn, or spikes in research activity, the Orchestrator reaches out instantly on your behalf.
And it doesn’t stop there.
Warmly can also find all key stakeholders in an account using your connected tools (like Apollo, Demandbase, or ZoomInfo), and then multithread outreach, so you’re not just talking to one champion, but the whole buying committee.
The best part? These sequences are smart, going far beyond generic AI-written cadences.
Warmly factors in who’s already being messaged, adjusts tone and messaging per persona, and ensures follow-ups happen automatically.
This way, you’re not scaling spam. You’re scaling relevance.
That means more replies, more meetings, and a bigger pipeline without growing headcount.
4. Real-time recommendations that guide reps while the deal is still in motion
In sales, timing is everything.
You can have the perfect pitch, the right case study, and a qualified buyer… but if it’s delivered too late or buried in the wrong thread, it lands flat.
That’s why top sales teams today are leaning on AI not just for after-the-fact insights, but for real-time recommendations that help reps act in the moment when interest is highest and momentum matters.
Whether it’s suggesting the right piece of content to send after a product question, flagging a key stakeholder who just joined the call, or prompting the rep to push for a meeting while interest is peaking, AI helps reps make smarter moves, faster.
Tools like Warmly’s AI Copilotplay a central role here.
It monitors buyer engagement and context signals to recommend next steps that are timely and tailored.
For example, if a lead is actively chatting with your AI assistant on-site, Warmly can alert your rep in real-time and even let them jump into the conversation, turning passive engagement into an instant, live interaction.
Need to make a bigger impression?
The experience can escalate from live chat to a face-to-face video call on the spot, helping reps capitalize on interest while the lead is still hot. No delay. No scheduling friction.
Beyond interactions, Warmly also helps reps personalize their follow-up, providing intel on who the buyer is, why they’re interested, and what kind of messaging is most likely to resonate.
It even suggests what to say, so reps don’t waste time researching or guessing.
This blend of real-time data, contextual awareness, and suggested actions means your reps are never flying blind.
They're backed by an AI assistant that’s quietly working behind the scenes to guide them deal by deal, moment by moment.
5. Enhanced conversational intelligence and coaching
The best coaching doesn’t come from generic tips or quarterly reviews.
It comes from the actual conversations your reps are having every single day.
AI is giving sales leaders and enablement teams something they’ve never really had before: visibility into what’s being said, how it’s landing, and what’s working in real-time, at scale.
AI-powered conversational intelligence tools like Gong, Chorus, and others, analyze sales calls in real-time or after the fact, surfacing key insights such as objection patterns, talk-to-listen ratios, missed buying signals, and competitive mentions.
But the real power is in how AI turns these insights into action.
Instead of waiting until the end of the quarter to review performance, enablement leaders can now deliver coaching in the moment, flagging teachable moments, recommending talk tracks, and helping reps self-correct before it costs a deal.
Reps can review top-performing calls filtered by persona, vertical, or objection type, learning from the best without needing to sit in live.
AI can even highlight moments where deals were won or lost, helping teams spot patterns and replicate success.
While platforms like Warmly don’t currently handle full post-call transcription analysis like Gong, its AI Copilot plays a complementary role by guiding reps mid-process, suggesting messaging, surfacing context, and ensuring that reps always know what to do next.
When paired with conversational intelligence tools, it creates a continuous improvement loop: insights from calls fuel better outreach, which in turn drives better conversations.
Together, these AI capabilities enable a culture of high-performance coaching - one that’s proactive, precise, and rooted in the actual voice of the customer.
6. Adaptive sales playbooks that evolve with every conversation
Static playbooks don’t cut it anymore.
In fast-moving sales cycles, a rep flipping through a 30-page PDF often amounts to a missed opportunity.
Today’s buyers expect personalized, relevant conversations, meaning your reps need enablement materials that adapt in real-time to what’s actually happening in the deal.
AI is making that possible with dynamic sales playbooks - living systems that update based on CRM activity, deal stage, buyer behavior, and even competitor involvement.
Instead of following a rigid script, reps get contextual guidance: which case study to share next, what objection might come up, who else to bring into the conversation, and when to push for a meeting.
These AI-powered playbooks serve as real-time sales assistants, surfacing what to do, say, or send at each touchpoint.
They remove guesswork, shorten decision-making, and align sales execution with the strategy that’s proven to work.
Imagine this: A rep is mid-cycle with a fintech buyer.
The AI system detects that similar deals recently included security-related concerns and suggests a relevant security one-pager before the buyer even raises the issue.
Or it flags that the VP of Ops just joined the opportunity in Salesforce and recommends a tailored email sequence for that persona, along with talking points from past successful deals.
Some platforms even layer in win-loss data, product updates, and pricing changes to keep guidance current.
That means your reps are never outdated, out of sync, or out of touch with what’s actually converting right now.
This shift from rigid manuals to intelligent, real-time guidance is probably one of the most impactful changes AI has brought to sales enablement.
It empowers reps to sell with confidence, alignment, and precision, no matter their experience level.
7. AI-powered onboarding that learns on the go
Getting new reps up to speed has always been one of sales enablement’s biggest challenges.
Traditional onboarding with slides, playbooks, and shadowing can feel bloated, inconsistent, and painfully slow.
And when reps take months to learn the ropes, pipeline suffers.
AI is changing all that for the better.
In 2026, top-performing sales orgs are using AI-driven onboarding platforms to accelerate learning, personalize coaching, and replicate what their best reps are already doing right.
Instead of one-size-fits-all training paths, AI tailors the experience to each individual.
It analyzes a rep’s performance in real time, identifies knowledge gaps, and dynamically serves up the right content, whether that’s objection-handling videos, competitor battlecards, or call recordings from top performers handling similar scenarios.
Some tools go further, simulating real conversations using generative AI and giving reps a safe space to practice discovery, handle tough objections, and get immediate feedback before ever talking to a prospect.
Others track engagement and results to build a profile of each rep’s strengths and weaknesses, helping enablement leaders fine-tune coaching at scale.
This isn’t just about speeding up onboarding time (though it does that too).
It’s about creating a high-performance culture from day one where every new hire learns faster, sells smarter, and starts contributing to quota sooner.
And while Warmly isn’t an onboarding platform in the traditional sense, it does give new reps a massive edge right out of the gate.
With features like AI Copilot, real-time lead insights, and automated outreach suggestions, even first-week reps can show up with context, confidence, and the kind of relevance that usually takes months to develop.
Put simply: AI flattens the learning curve.
It helps you turn new hires into high-performers faster, and at scale.
8. Pipeline clarity without the guesswork
Managing a sales pipeline used to involve a lot of gut feeling, spreadsheets, and crossed fingers.
But today, sales leaders aren’t relying on end-of-quarter hunches. They’re using AI to see what’s really happening across the funnel, in real-time.
AI-driven pipeline analysis transforms how teams track, assess, and act on pipeline movement.
Instead of simply showing what’s in the funnel, AI tools can forecast deal outcomes, spot risks early, and recommend specific actions to keep opportunities moving forward.
By continuously analyzing CRM activity, engagement signals, email sentiment, and historical patterns, AI identifies the warning signs that humans often miss, such as:
A key stakeholder going silent.
Reduced email frequency.
Fewer website visits.
Even subtle changes in tone during outreach.
It also helps revenue leaders understand why deals are stalling and how to intervene, whether that’s looping in a new persona, adjusting the messaging, or revisiting pricing strategy.
In short: AI enables proactive deal management instead of reactive clean-up.
For enablement teams, this visibility is gold.
It allows them to spot patterns across reps, increase support where it's needed most, and fine-tune content or playbooks based on actual deal behavior.
And for reps, it means getting clear, actionable recommendations without digging through dashboards or second-guessing what to prioritize.
While Warmly doesn’t position itself as a forecasting platform, it does equip reps with the real-time insights and intent data that power smarter pipeline decisions.
From identifying which accounts are heating up to nudging reps when it’s time to follow up, Warmly ensures every deal gets the attention it needs before it’s too late.
Simply put, AI turns pipeline from a black box into a strategic asset.
And in high-stakes B2B sales, that visibility is a competitive advantage you can’t afford to ignore.
9. Prospecting on autopilot
Prospecting has always been a necessary evil in sales.
It’s time-consuming, repetitive, and often the first thing reps push to the bottom of their to-do list.
But what if your best SDR never slept, never got tired, and never missed a signal?
That’s what AI-powered prospecting delivers, which is why it’s quickly becoming a non-negotiable for high-performing teams.
Instead of spending hours scraping LinkedIn, filtering intent platforms, or writing manual outreach lists, AI prospecting tools now automate the entire process by:
Identifying ideal accounts.
Sourcing verified contacts.
Prioritizing prospects based on real-time buying signals.
Warmly’s AI SDR agent is the perfect example of how AI can enhance prospecting on scale.
It acts as an always-on outbound assistant, continuously scanning for new accounts and leads that match your ICP, not just by firmographics, but by deeper behavioral and intent signals.
Using connected data sources and proprietary enrichment, Warmly surfaces decision-makers across your TAM, scores them for fit and interest, and immediately kicks off tailored outreach sequences, all without human input.
You can prospect into hundreds (or thousands) of accounts simultaneously while keeping it personal, timely, and relevant.
The result? Massive productivity gains for your human sellers.
Instead of manually researching and list-building, your reps start their day with qualified conversations already in motion.
More booked meetings. More pipeline. More deals closed, and all that with far less grind.
In a world where speed to lead and precision targeting can make or break your quarter, AI prospecting isn’t just a shortcut - it’s a superpower.
10. AI-powered video that connects
In a crowded inbox or noisy buying committee, sometimes the fastest way to build trust is with your face.
Video is one of the most powerful tools in modern sales enablement - not just for top-of-funnel engagement, but for building credibility, accelerating deals, and creating human connection at scale.
And AI is making it easier than ever to deliver the right video, to the right person, at the right time.
AI-powered video tools help reps record once and personalize infinitely, automating greetings, inserting dynamic data (like name, company, or pain point), and recommending the best video content based on persona or deal stage.
Some tools even analyze video engagement, like watch time, replay points, or drop-off moments, to guide smarter follow-up.
Warmly takes this a step further with real-time video engagement.
Through Live Video Calls, reps can jump directly from a live chat session into a face-to-face video call, seamlessly transitioning from digital to personal when a lead shows strong intent.
It’s not just reactive either.
AI can suggest when to use video, who’s most likely to respond to it, and what format works best, whether that’s a quick Loom-style intro, a product walk-through, or a short reply to overcome a late-stage objection.
But it’s not just for buyers. Video is also transforming internal enablement.
AI-curated video libraries now allow reps to ramp up by watching top-performer calls filtered by topic, persona, or stage.
Generative AI can summarize long videos into highlight reels or surface teachable moments automatically, so new hires don’t have to sit through hours of footage to get to the good stuff.
Sales leaders can also use AI to quickly generate training videos and personalize them on the go, allowing for easy repurposing and creating evergreen onboarding content.
In short, with the right AI-powered systems in place, video has become a scalable asset for both engaging buyers and enabling reps from day one to a closed deal.
5 best AI-powered sales enablement tools on the market
There’s no shortage of tools promising to “boost sales,” but in 2026, the ones that stand out are deeply AI-native - built not just to automate tasks, but to truly enable reps with intelligence, timing, and precision.
Whether it’s finding in-market buyers, personalizing outreach at scale, or delivering smarter coaching and content, the right AI-powered sales enablement tools help teams move faster and close with confidence.
Below, I’ve rounded up five best AI-powered sales enablement tools, including everything from automated prospecting and guided selling to dynamic video creation for outreach and training.
Let’s take a look at what’s driving results for modern revenue teams!
1. Warmly - AI-powered sales enablement for high-intent outreach
Warmly is an AI-native sales platform built to help GTM teams find, prioritize, and engage in-market buyers with precision and scale.
From identifying high-fit leads to orchestrating personalized multichannel outreach, Warmly gives reps the context, timing, and automation they need to drive more meetings and close more deals without the manual work.
Standout features
AI SDRs- These automate prospecting and personalized outreach across web chat, email and LinkedIn, triggered by real-time buying signals.
AI-powered ICP scoring - Defines your true ideal customer profile based on behavioral and firmographic patterns, then targets matching accounts automatically.
Intent signal monitoring - Tracks 1st, 2nd, and 3rd party intent signals to identify which leads are warming up and which are ready to buy right now.
AI Copilot - Suggests exactly who to reach out to, why they're interested, and what to say, turning guesswork into guided selling.
Warm Chat + live video handoff - Lets you instantly transition from on-site chat to live video calls with high-intent prospects, enabling real-time engagement at peak interest.
Smart lead routing - Sends hot leads to the right rep in real time, with alerts delivered via Slack or CRM integrations so no opportunity slips through the cracks.
Pricing
Warmly offers a free forever plan that allows you to reveal up to 500 monthly visitors, set up ICP filters to quickly identify high-quality leads, and automate basic lead routing.
If you need more, there are three tiers to choose from:
Data Only: Starts at $599/mo when billed monthly or $5,000 when billed annually, lets you identify up to 5,000 monthly visitors, first-party intent signals, alerts, and access to Warmly’s B2B prospecting database.
Business: Starts at $19,000/year for up to 10,000 visitors or $45,000/year for up to 75,000 visitors, everything in Data Only, plus third and second-party signals, sales orchestration, AI Chat, and lead routing.
Enterprise: Custom pricing, custom number of visitors, everything in Business, plus custom signals and warm calling.
2. Synthesia - Scalable AI video creation for sales training and outreach
Synthesia is a leading AI video generation platform that enables sales and marketing teams to create high-quality, personalized videos in minutes without cameras, microphones, or editing software.
Whether you're building training content, prospecting videos, or product explainers, Synthesia makes it easy to scale video production while keeping messaging sharp and consistent.
Standout features
AI avatars and voiceovers - Choose from over 150 AI-generated avatars and voice styles to create professional videos without the need for live recordings.
Text-to-video builder - Turn scripts, slides, or plain text into engaging videos with just a few clicks, which is perfect for sales enablement materials or outbound messaging.
Team collaboration tools - Manage video projects, review scripts, and collaborate across GTM teams in one shared workspace.
Pricing
Synthesia has a free plan suitable for individuals that includes just 1 user, 3 minutes of video per month and 9 AI avatars.
Agencies will need more, so you can choose from three paid plans:
Starter: $29/mo, everything in Free, 1 editor and 3 guests, 10 minutes per month, AI video assistant, etc.
Creator: $89/mo, everything in Starter, 1 editor and 5 guests, 30 minutes per month, 5 personal avatars, video dubbing, etc.
Enterprise: Custom pricing, everything in Creator, unlimited video minutes, 1-click translation, unlimited personal avatars, etc.
3. Convin - AI conversation intelligence for smarter coaching and better calls
Convin is an AI-powered conversation intelligence platform that analyzes sales calls, demos, and meetings to help revenue teams improve rep performance, uncover coaching opportunities, and close more deals.
It brings visibility into what’s actually happening in your pipeline, so leaders can coach based on real conversations, not assumptions.
Standout features
Conversation analysis - Identifies objection handling, competitor mentions, missed cues, and talk-time ratios, giving managers detailed insights into rep performance.
Real-time agent assistance - Offers live suggestions and prompts during calls, helping reps respond more effectively and stay on message.
Automated QA - Reviews every call for quality and rep performance, so your team has a clear idea of what needs to be improved.
Pricing
Convin has several distinct product suites with different plans and tiers:
CX Suite: Includes customizable and multilingual AI agents, call summaries, automated multichannel campaigns, etc.
Real-Time Suite:
Real-Time Agent Assist: Includes agents real-time monitoring and guidance, searchable AI knowledge base, call script guidance, etc.
Supervisor Assist: Supervisor Assist dashboard, real-time conversations visibility, observes real-time changes in sentiment trends, etc.
3. Voice of Customer: Includes AI-driven insights, AI summarization, lead propensity, CSAT, and collection scoring, etc.
4. Post Interaction Suite:
Automated Quality Assurance: Includes 100% automated conversation scoring, omnichannel QA on calls, chats, and email, etc.
Automated Agent Coaching: Includes AI-driven coaching session assignment, personalized and targeted sessions, peer-to-peer coaching, etc.
AI Learning Management System (LMS): Includes agent readiness assessments, leaderboard and gamification, etc.
However, no prices are disclosed for any of the packages, so you’ll have to contact its team for a custom quote based on your needs and the features you want.
4. Dashworks - AI-powered knowledge assistant for sales teams
Dashworks is an AI-enabled search and knowledge management platform that helps sales reps find the information they need instantly.
By connecting to tools like Google Drive, Slack, Notion, and CRMs, Dashworks acts as a central hub for sales content, competitive intel, product info, and more, eliminating time lost in digging through docs and Slack threads.
Standout features
Unified search across tools - Reps can instantly find answers across platforms like Slack, Salesforce, Google Docs, and Confluence from a single search bar.
AI-powered Q&A assistant - Instead of just retrieving documents, Dashworks answers sales reps’ questions directly, surfacing snippets, links, and relevant data.
Smart onboarding - Helps new hires ramp faster by serving up curated content, answers, and best practices as they learn the sales stack.
Pricing
Dashworks has three plans:
Team: $12 per seat per month, no seat minimum, provides unlimited usage, core integrations, file uploads, Slackbot, etc.
Business: $15 per seat per month, minimum 10 seats, includes everything in Team, plus custom bots, org-wide integrations, AI customization, etc.
Enterprise: Custom price, includes everything in Business, plus SSO and SCIM, API access add-on, advanced analytics, etc.
5. Docket - Real-time AI sales engineer for technical selling at scale
Docket is an AI-powered platform designed to support sales teams during complex or technical sales cycles by acting as an always-available sales engineer.
It helps reps respond to product questions, customize documents, and surface the right information during live conversations without needing to loop in experts every time.
Standout features
AI sales engineer assistant - Instantly digs up and provides answers to technical product questions during calls, emails, or demos, reducing reliance on solution engineers.
AI Seller - AI-powered chatbot that leverages your product data and customer insights to instantly answer on-site questions, provide relevant collaterals, etc.
Auto-generated sales documents - Creates personalized RFPs, one-pagers, and solution briefs based on natural language prompts and your data.
Pricing
Docket doesn’t publish any information regarding its pricing or packages.
You can book a demo to get more details.
Next steps: Bring AI into your sales enablement motion
Modern sales enablement isn’t about giving reps more tools. It’s about giving them smarter ones.
AI is no longer a future play.
It’s how top-performing teams are already finding better leads, moving faster, personalizing deeper, and winning more deals with less lift.
Whether you’re scaling a lean team or enabling a 200-rep org, the right AI stack can transform how your GTM engine operates day to day.
If you’re looking for a platform that does more than just automate tasks - one that actually helps your team engage with the right buyers, at the right moment, with the right message - Warmly is built for that.
Book a demo and see how Warmly can power your sales enablement motion from first touch to close.
What is 10 Ways To Use AI For Sales Enablement In 2026 [Reviewed]?
10 Ways To Use AI For Sales Enablement In 2026 [Reviewed] refers to the concepts and strategies covered in this article. Understanding these fundamentals helps B2B teams improve their sales and marketing effectiveness.
Why is 10 Ways To Use AI For Sales Enablement In 2026 [Reviewed] important?
This matters because it directly impacts pipeline generation and revenue. Teams that master these concepts see better results from their go-to-market efforts.
How can I implement this?
Start with the strategies outlined above. For B2B teams, combining these tactics with tools like Warmly—which identifies website visitors and automates engagement—can accelerate results.
What tools help with 10 Ways To Use AI For Sales Enablement In 2026 [Reviewed]?
Several tools can help, depending on your specific needs. Warmly is particularly useful for identifying high-intent website visitors and engaging them before they leave your site.
What are the best practices for 10 Ways To Use AI For Sales Enablement In 2026 [Reviewed]?
Key best practices are covered throughout this article. Focus on the fundamentals first, measure your results, and iterate based on data rather than assumptions.
Outbound Sales Automation: Use Cases, Best Practices & Software
Time to read
Chris Miller
Today, outbound sales isn’t about grinding through lists or blasting cold emails into the void.
It’s about precision, timing, and showing up with the right message - on autopilot.
That’s precisely where outbound sales automation comes in.
The best teams aren’t just working harder. They’re working smarter, using AI-powered workflows to handle prospecting, outreach, follow-ups, and more.
The result? More meetings, less manual effort, and way fewer leads slipping through the cracks.
In this guide, I’ll break down what outbound automation actually looks like in practice, including real use cases, proven best practices, and the tools top teams are using to scale outbound without burning out.
Buckle up, and let’s dive in!
What is outbound sales automation?
Outbound sales automation is the process of using sophisticated technology to streamline and scale manual outbound tasks like prospecting, cold outreach, follow-ups, and lead tracking.
As a result, sales reps have more time to focus on what actually moves the needle, such as building lasting relationships and closing deals.
So, instead of having reps juggle dozens of tabs, lists, and reminders, automation tools take care of all the repetitive stuff in the background.
Think of it as a system that consistently works the pipeline while your reps stay focused on the high-value parts of your sales funnel.
And remember: it’s not about removing the human touch.
It’s about making sure the right message hits the right person at the right time automatically.
And today, outbound sales automation isn’t just about scheduling emails.
Outbound automation - and automation in general - has become smarter, more personalized, and increasingly AI-driven.
Let’s dig into some of the real benefits.
What are the benefits of outbound sales automation?
When done right, outbound sales automation does more than save time - it changes the entire sales game.
Here’s how:
Scales outreach without scaling headcount - You can run personalized, multi-step sequences across hundreds of leads without adding more reps to the team, allowing you to stay lean as you grow.
Keeps deals moving - No more dropped follow-ups or forgotten touchpoints. Automation ensures leads stay warm and momentum doesn’t stall.
Improves consistency and timing - Messages go out when they should, not when someone remembers to send them.
Frees up your reps - Your team spends less time copying data between tools and more time on real conversations and nurturing high-value leads.
Boosts conversion rates - With AI and real-time signals in the mix, automation helps you hit with the right message when it actually matters.
What are the key components of outbound sales automation?
When talking about outbound sales automation, it’s important to understand that it isn’t just one tool or one workflow - it’s a system made up of several moving parts working together.
Here’s what that system typically includes:
Lead sourcing and enrichment - Automatically finding new contacts and enriching them with firmographic, technographic, or behavioral data, so you’re not starting with a blank list but with a highly targeted one.
Multi-channel outreach - Coordinated touchpoints across email, phone, and even calendar, all scheduled and executed automatically, based on pre-set rules or triggers.
Sequencing & follow-up - Pre-built flows that send the right message at the right time, with logic that adapts based on replies, opens, clicks, or no response.
Personalization at scale - Tools that dynamically insert custom snippets from job titles to recent company news without needing a rep to write each email manually.
Analytics & performance tracking - Dashboards that show what’s working, what’s not, and where to optimize, covering everything from sequence steps to subject lines to timing.
In 2025, the best systems go beyond just automation - they’re proactive.
Which brings us to the next point.
How has AI revolutionized outbound sales automation in 2025?
AI hasn’t just improved sales automation - it’s transformed it into a fully adaptive, self-optimizing system that works behind the scenes to maximize results with minimal manual input.
A few years ago, automation meant setting up static sequences based on pretty rigid rules and triggers and hoping for replies.
Now? AI actively listens, learns, and adjusts based on what’s actually happening everywhere, including your inbox and across your CRM.
Here’s what that looks like in practice:
Smarter prospecting - AI surfaces the most relevant accounts by analyzing buying signals, role changes, tech stacks, and engagement trends in real-time, so reps can strike gold every single time.
Dynamic personalization - Instead of sending the same template to everyone, AI tweaks tone, length, and content to match each prospect’s profile, company news, social activity, and even previous behavior and interactions.
Self-optimizing sequences - AI continuously tests and improves subject lines, timing, messaging, and channels without needing a human to make manual tweaks.
Full-cycle orchestration - Some teams are using AI agents to manage the entire outbound workflow, from identifying the lead to booking the meeting, all on autopilot.
The result? Outbound that feels less like blind guessing and more like an actual human conversation at scale with minimal human input.
8 outbound sales automation plays that you can set up now
So what does outbound sales automation actually look like in the real world?
It’s not just tools and triggers , it’s repeatable, scalable workflows that let your team reach the right people at the right time, without reinventing the wheel for every new lead.
Below are 8 high-impact outbound automation plays that modern B2B sales teams are using to drive more pipeline with less manual effort.
These aren’t theoretical. They’re working right now, and most can be set up in hours, not weeks, with the right tools and know-how.
1. Intent-based lead generation + awareness activation
Not every outbound motion should start from a cold list.
Some of the best leads are already “warm”, they just haven’t heard of you yet.
Namely, if a buyer is researching your competitor, that’s not a loss - it’s an opportunity.
In fact, it's one of the most powerful triggers in outbound right now.
So, this warm outbound automation play taps into third-party intent data to identify companies actively researching solutions in your space, whether they’re:
Googling key industry topics.
Visiting competitor websites.
Reading articles about tools in your ecosystem.
The goal? Reach these accounts with relevant messaging before your competitors do.
Here’s how the play works:
Start by identifying accounts that are actively researching your competitors.
You can do this using:
Intent data (Bombora, G2), surfacing accounts spiking on competitor-related topics.
Keyword alerts, using platforms like Google Alerts or SparkToro to flag public activity.
Engagement signals, tracking job changes or new hires at competitor customers.
Since Warmly integrates with Bombora and monitors social signals, it allows you to pinpoint which companies are showing a spike in interest around specific topics, like “sales engagement software,” “revenue orchestration,” or even your direct competitors.
So, you can start by monitoring intent signals based on a custom topic list (e.g., your competitors, your integration partners, or pain points your product solves) to detect high-intent leads.
Automatically trigger a specific action when a company matches your chosen criteria (for example, if they’re showing high intent around a competitor's name or researching specific industry keywords).
Send personalized emails or DMs and connection requests to them.
💡Note: You will need to be on one of Warmly’s paid plans to gain access to AI Prospector. For ARC, the ROI was 200% over 6 months.
For instance, if a company starts showing intent around one of your key competitors, Warmly can automatically route them into a LinkedIn Ad campaign titled “Still using [Competitor]? Here’s what to know first.”
This gets your brand in front of decision-makers with messaging that’s highly contextual and timely.
Why this play works:
Precision timing - You’re not guessing who might need your product. Instead, you’re engaging accounts when their interest is spiking.
Frictionless awareness - These leads haven’t filled out a form or talked to sales yet, but you’re already warming them up with ads that match their research behavior.
Always-on pipeline building - Because Warmly’s Orchestrator continuously listens for new intent signals and automatically triggers outreach sequences, your audience is constantly updated with net-new accounts.
2. AI-powered prospecting & cold outreach
Tools: Warmly AI Marketing Ops Agent + Warmly AI SDR Agent.
Outbound sales has always started with one question: who should we talk to next?
But in 2025, instead of spending hours building lists, researching job titles, or figuring out which messaging will resonate, you can leverage AI.
This play uses autonomous AI agents to handle top-of-funnel work end to end from identifying the right accounts to booking qualified meetings.
Instead of relying on outdated ICP assumptions or static targeting criteria, the Marketing Ops Agent builds a dynamic profile of who you should be targeting based on:
Your best-performing accounts to date.
Closed-won opportunities across segments.
Real-time firmographic and technographic data.
Live engagement signals from your CRM or website.
It then continuously refreshes this target list as new data comes in, meaning your outbound engine is always aligned with your highest-converting audience.
Step 2: Generate a prospect list automatically
With the ICP defined, the agent identifies new accounts and contacts that fit your ideal criteria.
This list can be sourced from Warmly’s cold outreach database, public databases, third-party enrichment tools, or integrations you’ve already connected (Apollo, or ZoomInfo).
Step 3: AI SDR launches multichannel outreach
Now the baton passes to Warmly’s AI SDR Agent - a fully autonomous outbound rep trained to:
Craft cold emails and messages tailored to each persona and company.
Adjust tone, length, and CTA based on audience segment.
Monitor opens, clicks, and replies to guide follow-up.
Automatically book meetings when a lead shows interest.
You define the high-level campaign objective (e.g., “book demos with HR tech buyers in EMEA”), and the AI SDR handles execution across channels.
It doesn’t just follow a script - it reacts and adapts in real time.
This results in a truly autonomous outbound engine, one that knows your audience, finds the right leads, delivers tailored messaging, and fills your pipeline while your team focuses on closing.
3. Automated multistep cold outreach sequences
Tools: Warmly, Apollo, Outreach, Salesloft, or any modern sequencing platform.
Cold outreach only works when it’s structured.
One-and-done emails? Dead on arrival.
In 2025, outbound teams are using automated multi step sequences to create consistent, personalized, and persistent outbound motions that don’t rely on memory or manual tracking.
This play is about setting up outreach sequences that run themselves while still feeling human on the receiving end.
To make it work, you first need to build a structured campaign made of typically 6-10 touchpoints across email, and optionally phone or SMS.
Each step is automated, but designed to feel intentional and natural.
With Warmly, you get hyper-personalized outreach workflows.
Namely, the platform continuously monitors intent signals and other relevant data, which allows it to generate perfectly tailored messaging for each audience segment.
Here’s a basic breakdown of a fully orchestrated workflow using Warmly’s Orchestrator:
Day 1: Intro email, short, personalized, focused on pain or value.
Day 3: Message.
Day 5: Follow-up email with relevant resource (case study, blog, etc.).
Day 7: Personalized DM.
Day 10: Breakup-style email or light check-in.
Optional: Call task or voicemail drop if that’s part of your mix.
Each step triggers automatically based on time or engagement signals.
And the best part?
If a prospect replies, clicks a link, or connects, the sequence pauses, and a human can jump in.
With this play, no leads will ever fall through the cracks. You never forget to follow up. And the system runs in the background while your reps focus on what’s working.
4. Using job change trigger for personalized outreach
Tools: Warmly, Clay, UserGems, Apollo, or any enrichment + sequencing combo.
One of the highest-converting outbound triggers in B2B today is a new decision-maker stepping into a fresh role.
When someone in your ideal customer profile gets promoted or changes jobs - especially into a leadership or budget-holding position - they’re often looking to make early wins, evaluate existing vendors, or bring in solutions they trust from past roles.
And that is your window.
This play is about detecting those job changes automatically and engaging new-in-role contacts before your competitors even notice.
How it works:
Monitor for job changes using tools like Warmly that tracks (via saved leads + alerts), Apollo (job change filters + enrichment feeds), etc.
Trigger a tailored outbound sequence as soon as the change is detected. This could look like:
Day 1: Short email congratulating them on the new role, offering insights into how similar leaders are driving quick wins in their first 90 days.
Day 3: Message referencing their new position or company shift.
Day 6: Follow-up email with a playbook, template, or short guide relevant to their team/function.
Day 10: Final nudge - offer a quick call to share what’s working in their space.
Why this play works:
They’re open to change - People in new roles are often re-evaluating tools and strategies, especially in the first 60-90 days
They want early wins - Help them look good fast, not with a sales pitch, but with insights or proven strategies that they can find useful.
They may already know you - If they were a previous user or engaged with your brand in a past role, the trust barrier is already lower.
Some ghost after the first conversation. Others go dark mid-sequence. And many just never respond at all.
But that doesn’t mean they’re dead leads. It just means the timing wasn’t right yet.
This outbound automation play focuses on re-engaging cold or silent leads with fresh messaging and zero manual effort, turning your “maybe later” pile into real pipeline.
This play starts by identifying leads who:
Opened emails but didn’t respond.
Replied once, then went quiet.
Attended a call or demo but didn’t convert.
Were enrolled in a sequence more than X days ago with no result.
Using your sequencing tool or CRM filters, segment out these “ghosted” leads and drop them into a reactivation campaign - a light-touch, multi-channel flow designed to restart the conversation without sounding like a chase.
A simple reactivation sequence using Warmly’s Orchestrator or another sequencing tool of choice might look like:
Day 1: Email: “Still thinking about [pain point]?” - include a new case study, stat, or feature.
Day 3: Follow-up in a casual, helpful tone: “Let me know if you want a new take on [problem X]”.
Day 6: Final check-in email or breakup message, offering an async resource or “no pressure” opt-out.
Bonus messaging angle tips:
Lead with something new - Product update, new case study, customer win, or fresh stat, not “just checking in”.
Make it low-friction - Suggest a Loom, short async reply, or quick scan resource instead of pushing for a call straight away.
Give them an out - Sometimes the polite breakup email gets the reply that the sales pitch didn’t.
By keeping the tone helpful, relevant, and lightly persistent, this play quietly revives deals that would otherwise be lost, all without reps manually digging through their old outreach history.
When a company announces new funding, everything changes, such as priorities, budgets, urgency.
It’s a classic outbound trigger, but with automation, it becomes a reliable and scalable play to get in early, while decisions are still being made and wallets are open.
This play is about spotting funding events as soon as they happen and triggering a timely, relevant outbound motion that connects your solution to their next stage of growth.
To begin, track funding events in your target accounts using Crunchbase, Warmly Signals, Clearbit, etc.
And then, trigger a custom outbound sequence when a funding event hits.
The messaging should reflect:
The size or type of round (e.g., “Congrats on your Series B - big things ahead!”).
The use case your tool supports at that stage (e.g., hiring, scaling ops, GTM efficiency).
Social proof that makes your solution a no-brainer for growth-stage teams.
A typical flow could look like:
Day 1: Email: “Scaling post-Series B? Here’s what to avoid”.
Day 3: Message: “Saw the announcement - congrats! I work with similar teams scaling GTM ops after raising…”
Day 6: Follow-up with a relevant case study or hiring-focused angle.
Day 9: Final nudge: short video or async message offering insights.
Bonus messaging tips:
Focus on efficiency, scalability, and speed, all key concerns for teams post-raise.
Don’t make it all about the funding - make it about what comes next (e.g., hiring, GTM execution, onboarding challenges).
Offer a path to value quickly, like something that shortens time to results.
With this automation in place, your system can track funding news, enrich the contact, personalize the messaging, and kick off outreach often within hours of the announcement going live.
Sometimes, outbound needs to feel less like a campaign, and more like a conversation.
This play is a lightweight version of full-blown ABM.
It’s designed for your top 25–50 target accounts - the high-intent, high-fit prospects that deserve more than just a cold email template.
The idea is to use automation and AI to build personalized, multi-step outreach that speaks directly to each account’s context, pain points, and buying triggers at scale.
To make it work, start by creating a curated list of high-value accounts, such as best strategic fits or companies in a buying window (based on job changes, intent, funding, etc.) using a tool like Warmly’s Demand Gen Agent that lets you build highly targeted audience segments.
Then use a data enrichment platform to enrich each account with data such as:
Recent company news.
Tech stack.
Job openings.
Activity.
Leveraging that data, generate dynamic, account-specific messaging snippets using an AI tool and push those into your outbound platform of choice to build a multichannel sequence tailored to the account’s profile:
Day 1: Email with a personalized opening line + value proposition tied to their specific context.
Day 3: Message referencing their company milestone, industry trend, or relevant news.
Day 5: Email with a short case study that mirrors their use case.
Day 8: Final follow-up that offers a custom teardown, async video, or resource drop.
Why this play works:
You show up prepared - Prospects immediately see that you’ve done your homework, even if the message is AI-assisted.
It balances quality with scale - You can hit 50+ strategic accounts in parallel, without writing every email from scratch.
It earns replies - Personalized outreach consistently outperforms generic messaging, especially with mid-market and enterprise buyers.
8. AI-powered cold calling assistant
Tools: Koncert, Orum, Balto, Anybiz, or custom AI agents.
Cold calling has always been time-consuming and hard to scale, but in 2025, voice AI is changing that.
This play automates parts of the cold call process using AI dialers and voice agents that can either assist reps or carry out entire calls on their own.
Keep in mind that this is not about replacing human reps completely.
Instead, it is about using automation to cover more ground, qualify leads faster, and route only real conversations to your team, letting your reps focus only on the things that actually move the needle.
Here are some examples of how you can set up an outbound cold call automation play:
Use an AI-powered dialer (e.g., Koncert, Orum) to rapidly call through prioritized lead lists - These tools can detect voicemails, skip bad numbers, and connect reps only when a real person answers, massively increasing talk time.
Deploy an AI voice agent that follows a scripted flow, handles objections, qualifies interest, and even books meetings. These agents use natural-sounding voices and can manage basic cold call conversations end-to-end.
Optionally, you can layer in a real-time coaching assistant (e.g., Balto) that can support live reps with on-call prompts, recommended responses, and objection-handling guidance.
As a result, you’ll get more conversations per day combined with smooth handoffs, as your reps will handle only the warmest leads instead of wasting time on a bunch of cold ones.
Bonus tip: Combine cold calls with multichannel follow-up.
Even if a prospect doesn’t convert on the call, hearing your name builds familiarity.
When they later see your name in their inbox, the message feels warmer, and your response rates go up.
The 4 best outbound sales automation software on the market
You’ve seen what great outbound automation looks like in action, and now let’s talk tools.
With dozens of platforms out there promising to automate your outbound, knowing which ones actually deliver is a challenge.
So we’ve narrowed it down for you.
Below are the top four outbound sales automation platforms in 2025, each with a unique strength, from full-funnel orchestration to cold call automation.
1. Warmly - Best for automating intent-driven outbound workflows with AI-powered agents
Warmly is purpose-built for outbound teams that want to act on real buying signals, instead of just sending cold emails.
It connects third-party intent data, website behavior, and persona-based triggers with AI agents that prospect, engage, and book meetings autonomously.
This makes Warmly ideal for teams looking to scale personalized outreach without scaling headcount.
Standout features
AI agents for autonomous prospecting and engagement - Warmly has several distinct AI agents that can manage your entire outbound funnel, from defining your ICP and building targeted lead lists to engaging warm leads and pushing them further down the pipeline.
Orchestration of multichannel sequences - The Orchestrator and AI agents tackle entire outreach sequences across channels, making sure that each message and email are personalized and optimized for success.
Real-time signal monitoring - The platform constantly monitors on and off-site signals, identifying leads most likely to convert right now.
Automated CRM syncing, enrichment, and cleanup - Keeps your pipeline clean and up to date by automatically enriching lead data, logging every touchpoint, and eliminating duplicate or messy records.
B2B database - Coldly holds data on 250M+ companies and contacts, powering your outbound across levels.
Pricing
Warmly offers a free forever plan that allows you to reveal up to 500 monthly visitors, set up ICP filters to quickly identify high-quality leads, and automate basic lead routing.
If you need more, there are three tiers to choose from:
Data Only: Starts at $599/mo when billed monthly or $5,000 when billed annually, lets you identify up to 5,000 monthly visitors, first-party intent signals, alerts, and access to Warmly’s B2B prospecting database.
Business: Starts at $19,000/year for up to 10,000 visitors or $45,000/year for up to 75,000 visitors, everything in Data Only, plus third and second-party signals, sales orchestration, AI Chat, and lead routing.
Enterprise: Custom pricing, custom number of visitors, everything in Business, plus custom signals and warm calling.
2. AnyBiz.io - Best for automating cold calls and outbound engagement with autonomous AI agents.
AnyBiz is one of the first platforms to offer fully autonomous outbound agents capable of running multichannel campaigns, including cold calls, without human intervention.
Unlike traditional dialers that only connect reps faster, AnyBiz’s agents can handle entire conversations, qualify leads, and adapt to prospect responses on the fly.
It’s built for scale, especially if you're targeting large volumes of accounts and want to test cold calling as part of a broader automated strategy.
Standout features
AI agents capable of running cold calls autonomously - AnyBiz’s AI agents can initiate, carry, and conclude cold call conversations using natural-sounding voice AI, handling objections, asking qualifying questions, and booking meetings without the need for a human rep.
Lead qualification without rep involvement - The AI scores leads using pre-defined logic and routes only interested or high-fit leads to your calendar, eliminating the need for manual vetting.
Multichannel outreach across email and phone - The platform supports fully automated outreach across multiple channels, enabling AI agents to sequence emails, send messages, and follow up with calls in a coordinated workflow.
Pricing
Anybiz has four pricing plans:
Starter: $499/month, includes just one outreach channel (email).
Business: $949/month, includes two outreach channels (email +social).
Expert: $1,745/month, the first plan to include all three outreach channels (email + cold calls).
Super Agent: $2,795/month, includes everything in Expert, plus higher usage limits.
3. Salesforge - Best for AI-driven cold email outreach with high deliverability and personalization at scale.
Salesforge is an AI-powered sales execution platform designed to automate lead sourcing, personalize email outreach, and manage follow-ups.
Its AI sales assistant, Agent Frank, helps sales teams engage prospects effectively without increasing headcount.
Standout features
AI-generated personalized emails - Salesforge uses its AI agent, Agent Frank, to craft unique, human-sounding cold emails tailored to each prospect’s persona and context, helping you stand out in crowded inboxes without writing each message by hand.
Warmforge to improve email deliverability rates - The platform emphasizes inbox placement, using warmed-up sending domains, inbox rotation, and smart sending schedules to improve deliverability and avoid spam folders.
Automated follow-up sequences - You can build and launch multistep follow-up campaigns that run on autopilot, adjusting based on engagement (opens, clicks, replies) to keep conversations moving forward without manual nudges.
Pricing
Salesforge has two plans for users who don’t want to leverage its AI agent:
Pro: $48/mo, includes 1 user, mailbox rotation, sentiment analysis, etc.
Growth: $96/mo, includes unlimited users, everything in Pro, plus exclusive features such as A/B testing, AI email reviews, and higher number of active clients and credits for sending and validating emails.
Keep in mind, though, that you can purchase more credits on both plans if you need them, meaning that the final cost might be higher than the base price.
If you want to hire Salesforge’s AI Agent, you’ll pay $499/month (billed quarterly) for up to 1,000 contacts and $24,950 for up to 50,000 active contacts.
4. Apollo - Best for all-in-one outbound prospecting
Apollo shines as a self-contained outbound engine: it gives reps access to a huge database of verified contacts and then lets them launch multistep sequences without ever leaving the platform.
This makes it great for teams who want to eliminate data silos and simplify the outbound stack.
Standout features
Massive contact database with advanced filters - Apollo gives you access to over 260 million verified contacts and powerful filtering tools.
Built-in email, call, and task automation - You can launch fully automated outreach sequences that combine emails, phone calls, and custom tasks all managed from a single dashboard.
Chrome extension for workflows - This lets you prospect directly from scraping contact data, adding leads to sequences, and logging activity without switching tabs.
Pricing
Apollo has a free forever plan that includes 100 email and mobile phone finder credits, basic filters and prospecting, and two sequences.
What are the key metrics to consider for your outbound sales automation campaigns?
Finally, it’s important to understand that automation helps you scale - but it’s the right metrics that tell you whether your outbound is actually working or just creating noise.
These are the core KPIs modern outbound teams track to measure performance, uncover bottlenecks, and optimize results over time.
1. Email open rate
This tells you whether your emails are making it past the inbox and into your prospects’ attention.
It’s especially useful for testing subject lines, sender names, and delivery timing.
Why it matters: If no one’s opening your emails, the rest of your sequence doesn’t stand a chance.
2. Reply rate
This measures how many people respond to your outbound, regardless of whether they say yes, no, or “not now.”
It’s a quick indicator of whether your message is resonating and relevant.
Why it matters: A strong reply rate signals that your targeting and messaging are aligned with your audience’s priorities.
3. Positive response rate
Not all replies are created equal.
Tracking how many responses express genuine interest or request more information helps separate noise from actual pipeline potential.
Why it matters: This metric gets you closer to understanding how much qualified intent your automation is generating.
4. Booked meetings rate
This is one of the most concrete outcomes of outbound - how many leads end up scheduling a call, demo, or meeting.
It connects messaging, targeting, and timing into a single result.
Why it matters: Getting actual meetings is the clearest sign that your outbound sequences are doing their job.
5. Bounce rate
Bounce rate tracks how many emails couldn’t be delivered, usually because the address was invalid or inactive.
High bounce rates can damage your sender reputation and hurt deliverability across your domain.
Why it matters: Healthy lists are essential for keeping your outbound engine running smoothly and your emails out of spam.
6. Prospect-to-lead conversion rate
This measures how many outbound contacts turn into real sales opportunities.
It reflects not just response rates, but quality of targeting and follow-through from your sales team.
Why it matters: It tells you whether your automation efforts are generating real pipeline, not just conversations.
7. Speed to engage (time to first touch)
This is the time between when a lead enters your system (e.g., from an intent trigger or job change alert) and when your first outreach lands.
Automation should make this nearly instant.
Why it matters: Fast outreach increases the chances of catching a prospect while their interest or urgency is still high.
Next steps: Build smarter, faster outbound with automation
Outbound sales automation isn’t just about saving time - it’s about multiplying your impact.
From intent-based targeting to AI-powered cold calls, the right plays combined with the right tools can turn cold outreach into consistent pipeline.
But automation only works when it’s connected to real signals, and not just static lists.
The teams nailing outbound are the ones using AI to adapt in real time, personalize at scale, and engage prospects when it actually matters.
If you’re ready to stop guessing and start automating outbound the smart way, Warmly can help.
With AI agents, real-time intent triggers, and multichannel orchestration built in, you’ll go from chasing leads to booking meetings automatically.
Book a demo with Warmly and see how outbound can run (and win) on autopilot.
AI for Sales: Best Tools & Tips [2025] - A curated roundup of the best AI sales tools and tactical tips for teams looking to close more with less effort.
What is Outbound Sales Automation Use Cases, Best Practices & Software?
Outbound Sales Automation Use Cases, Best Practices & Software refers to the concepts and strategies covered in this article. Understanding these fundamentals helps B2B teams improve their sales and marketing effectiveness.
Why is Outbound Sales Automation Use Cases, Best Practices & Software important?
This matters because it directly impacts pipeline generation and revenue. Teams that master these concepts see better results from their go-to-market efforts.
How can I implement this?
Start with the strategies outlined above. For B2B teams, combining these tactics with tools like Warmly—which identifies website visitors and automates engagement—can accelerate results.
What tools help with Outbound Sales Automation Use Cases, Best Practices & Software?
Several tools can help, depending on your specific needs. Warmly is particularly useful for identifying high-intent website visitors and engaging them before they leave your site.
What are the best practices for Outbound Sales Automation Use Cases, Best Practices & Software?
Key best practices are covered throughout this article. Focus on the fundamentals first, measure your results, and iterate based on data rather than assumptions.
AI Lead Scoring: The Compound Score Method for B2B Sales [2026 Framework]
Time to read
Chris Miller
Every AI lead scoring tool tells you who's most likely to buy.
That's the wrong question.
The right question is: where should the AI spend its next unit of effort?
I've watched hundreds of B2B teams implement lead scoring. Most of them get a number, stare at it, and still don't know what to do. The score says 85. Great. Now what? Email them? Call them? Did someone already call them yesterday? Are there five other people at that company who also score 85 but nobody's contacted any of them?
AI lead scoring is the use of machine learning to automatically evaluate and rank prospects based on their likelihood to convert and their readiness for action, using patterns from historical data, real-time behavioral signals, and third-party intent data. Unlike manual scoring, AI models continuously learn from outcomes, improving accuracy over time. But the best scoring systems go further. They factor in what you've already done, how much effort remains, and where the next productive action lives.
Traditional lead scoring accuracy: 15-25%. AI lead scoring: 40-60%. That's a 2-3x improvement. But most companies never get there because they treat scoring as a conversion prediction instead of an action-readiness signal.
Your SDRs spend about 2 hours a day actually selling. The rest is research, admin, and chasing leads that were never going to close. 79% of marketing leads never convert. Only 27% are sales-ready on average. And 67% of lost sales come from improper qualification.
The scoring model is the first thing to fix.
I'm going to walk you through the Compound Score, a 7-dimension framework we developed at Warmly that answers "what should the AI do next?" instead of "who might buy?" It's the framework behind our AI sales automation and the reason we can turn website intent signals into pipeline in minutes, not days.
Quick Answer: Best AI Lead Scoring Tools in 2026
If you just want the list, here it is. Detailed comparison with honest assessments below.
Rank
Tool
Best For
Starting Price
1
Warmly
Signal-layered scoring with real-time action
$799-$1,999/mo
2
HubSpot Predictive Lead Scoring
CRM-embedded predictive scoring
$90-$150/seat/mo
3
Salesforce Einstein
Enterprise AI scoring
$215+/user/mo
4
6sense
Account-based predictive scoring
$25K-$100K+/yr
5
MadKudu
Transparent "glass box" models
$999+/mo
6
Clay
Enrichment-powered scoring workflows
$149-$800/mo
7
Demandbase
ABM buying group scoring
$25K-$75K+/yr
8
ActiveCampaign
Budget-friendly automation
$49+/mo
Warmly connects scoring directly to automated outreach, AI SDR agents, and AI Chat. Most tools stop at the score. We act on it. That said, we don't do pipeline forecasting or call recording, and if you need Salesforce-native everything, Einstein is the safer bet. Full pricing details here.
Why AI Lead Scoring Matters: The Numbers
The 42-Hour Problem
The average lead response time across B2B is 42 hours. And 30% of leads never get contacted at all.
That's not a sales problem. That's a scoring problem. When you don't know who matters, everyone gets the same (slow) treatment.
Responding in 5 minutes is 21x more likely to convert than waiting 30 minutes. Calling within 1 minute delivers a 391% conversion boost. And 78% of customers buy from the first company that responds.
The math is brutal. If your scoring system updates in batch cycles every 4-12 hours, you've already lost the deal to a competitor who scored and acted in real time. Pipeline automation only works when the scoring engine feeds it fast enough.
The Qualification Crisis
SDRs spend only 28-39% of their time on revenue-generating activities. That's about 2 hours a day actually selling. The rest is research, admin, and context-switching.
And when they do reach out?
67% say poor lead quality is their biggest frustration
79% of marketing leads never convert
Only 27% of leads are actually sales-ready
80% of MQLs aren't a good fit. Four out of five
The cost of bad data quality alone is $12.9M per year per organization, according to Gartner. That's not a typo.
The AI Shift
The market has already moved. 89% of revenue organizations now use AI-powered tools, up from just 34% in 2023 (Gartner 2025 Sales Technology Report). And 75% of B2B companies are projected to adopt AI-driven scoring by end of 2026.
The predictive lead scoring market hit $5.6 billion in 2025, up from $1.4 billion in 2020. Companies using lead scoring see 138% ROI compared to 78% without it.
This isn't early adopter territory anymore. If you're not using AI for scoring, you're the outlier.
The Compound Score: A 7-Dimension Action-Readiness Framework
Every scoring model on the market asks the same question: "Who's most likely to buy?"
The Compound Score asks something different: "Where should the AI spend effort next?"
That's a fundamental reframe. Most scoring tools give you a number and leave it at that. The Compound Score drives action. High score means act now. Low score means the AI should look elsewhere for productive work. It's a resource allocation system, not just a prediction.
Think about it this way. You have an account that scores 90 on fit and intent. Great. But your team already emailed the entire buying committee last week. LinkedIn requests sent. Ads running. Calls attempted. What's left to do? Nothing productive. The score should reflect that reality.
That's what the 7 dimensions capture.
Dimension 1: Fit (Long-Horizon Intent)
Firmographic, technographic, and demographic data. Company size, industry, revenue, funding stage, tech stack, job titles, seniority.
What it answers: "Would we want this company as a customer?"
The key insight I keep coming back to: fit is actually intent on a long time horizon. A company being 51-250 employees is a signal. It just moves slowly. It changes over months and years, not days. Fit signals are slow-moving intent signals that set the baseline.
At Warmly, our TAM Agent uses AI ICP classification that explains WHY an account is Tier 1, Tier 2, or Not ICP. Plus a web research agent that scrapes the actual company website for context beyond database firmographics.
From our data: less than 1% of website visitors match ICP. Automated Fit scoring eliminates 99% of noise before a human ever looks at the account.
What it answers: "Are they actively researching solutions in our category RIGHT NOW?"
And the complementary insight: intent is fast-moving fit. It tells you what's happening this week. While fit changes over months, intent changes over days. Together they form a complete picture across time horizons. They're not separate categories. They're the same signal on different time scales.
Signal weighting matters. A pricing page visit (strong signal) is worth more than a blog visit (weak signal). First-party signals convert at roughly 15x the rate of third-party alone. Buyer intent tools that don't weight these differently are leaving pipeline on the table.
Dimension 3: Engagement (What YOUR Team Has Done)
This is where every other scoring model stops short. They track what the prospect did. The Compound Score also tracks what your team did in response.
Did you reach out? Did you route leads to the right rep? Did you auto-generate and send email sequences? Track email opens, replies, link clicks. Chat conversations. LinkedIn connection requests and InMail responses.
What it answers: "After they showed intent, did we actually DO something about it?"
High intent with zero outreach = high Compound Score. You need to act. High intent with full outreach already sent = score adjusts. You already acted. This is the outbound automation feedback loop that most tools completely ignore.
Dimension 4: Committee Penetration (Buying Group Progression)
B2B deals need 6 to 13 stakeholders per deal. If only one person is high intent but you need five, you have to multi-thread.
Track: How many buying committee members identified? How many contacted? How far through the journey is each one? Role coverage: Decision Maker + Champion + Influencer + Approver. Are they all engaged, or just one junior researcher who downloaded every whitepaper?
What it answers: "How far through the buyer journey is this ACCOUNT, not just this person?"
One person at score 90 is weaker than five people at score 40 each. The compound effect of committee engagement is the strongest buying signal in B2B. And nobody scores for it. Demandbase mentions it briefly. Everyone else ignores it completely.
Dimension 5: Activity Saturation (What's Left to Do?)
How much sales and marketing activity has already happened on this account?
If you've emailed the entire buying committee, sent LinkedIn messages, run ads, and made calls, the marginal value of more action is LOW. You only have about 15 emails per inbox per day. Don't burn them re-hitting accounts you already exhausted.
What it answers: "Is there productive work left to do here, or should the AI look elsewhere?"
This is the dimension that makes the Compound Score fundamentally different. The score drops when you've already done everything you can. High intent + good fit + fully acted upon = low score. Because the score isn't predicting conversion. It's finding where effort creates value.
Dimension 6: Recency and Decay (Cooldown Cycles)
When did signals last fire? When did you last engage?
If you haven't emailed an account in 30-40 days and they haven't responded, the cooldown period has passed. Time to re-engage. Score rises again. If you emailed yesterday, score stays low. Give it time.
Signals decay. A pricing page visit from 90 days ago isn't worth the same as one from today. Implement 30/60/90 day decay curves. Without score decay, the system lies. A lead that engaged 90 days ago and went silent shouldn't sit at the top of your SDR queue.
What it answers: "Is this the right moment, or should we wait?"
How expensive is the next action on this account? AI tokens, ad spend, rep time.
Accounts that have been touched hundreds of times with diminishing returns might not be worth more spend. The same budget might create more pipeline if spent on fresh accounts in the TAM that haven't been explored yet.
What it answers: "Is this a good use of our limited resources?"
The score tells the AI not just WHAT to do but whether it's WORTH doing. Lead scoring is also resource allocation. This ties directly to how autonomous GTM orchestration works in practice. The AI scans the entire TAM, looking for productive work. When it finds an account where action creates value, it acts. When it doesn't, it moves on.
The Action-Readiness Insight
Every other scoring model stops at "this account looks good."
The Compound Score goes further: "This account looks good, you haven't acted on it yet, the buying committee is assembling, signals are fresh, and the next action is cheap and high-leverage. ACT NOW."
And when the Compound Score is low? That's fine. It means you've already done the work. Or the account isn't ready. Or more effort won't move the needle. The AI moves on to find accounts where productive work exists. Like a senior rep who instinctively knows which accounts need attention today. Except it's scanning your entire TAM continuously.
The Feedback Loop
When deals close (or don't), the full picture emerges.
What did you send? Which channel? What timing? What was your world model at decision time? What was the outcome?
Backtest the world model against outcomes. Refine scoring policies. The system gets smarter every cycle.
This isn't instant feedback like shipping code. But it's a complete loop that compounds over quarters. GTM is an infinite game with constant change. Prospects change, buyers change, competitors change, the market changes. The Compound Score adapts through faster feedback cycles and smarter policies, compounding like interest over time.
How AI Lead Scoring Actually Works Under the Hood
Most articles on AI lead scoring stop at "machine learning analyzes your data." That's like explaining a car by saying "the engine makes it go." So here's what actually happens.
The ML Models
A 2025 study published in Frontiers in Artificial Intelligence found that Random Forest and Gradient Boosting models achieve the highest accuracy for lead scoring. Not neural networks. Not deep learning. Those are overkill for most scoring use cases and add complexity without proportional accuracy gains.
The hierarchy looks like this:
Gradient Boosting (XGBoost/LightGBM): Highest accuracy when tuned. Best for teams with clean data and tuning resources
Random Forest: Robust, fast (parallel training), handles noisy features well. The workhorse
Decision Tree: Good accuracy, fastest training. Best for interpretability
Logistic Regression: Baseline model. Best when you need to explain every coefficient to sales leadership
For most B2B scoring, Gradient Boosting is the right call. Neural networks aren't worth the overhead.
Training Data Requirements
HubSpot requires a minimum of 500 contacts and 3 months of historical data before their predictive scoring kicks in. That's the floor. More data equals a better model, always. You need examples of both wins AND losses. A model that only sees closed-won deals can't learn what bad leads look like.
Include full-funnel data. Not just form fills and MQLs. Deal outcomes, sales notes, product usage, post-sale retention. A lead that converted but churned in 30 days may not be your ideal profile.
Real-Time vs. Batch Scoring
This is where it gets practical.
Real-time scoring updates within 5-15 minutes of a signal firing. A VP visits your pricing page. Score updates. Slack alert fires. Rep gets notified. AI Chat primes if the visitor returns.
Batch scoring runs every 4-12 hours. Overnight enrichment. Morning score refresh. By the time your rep sees it, the VP already talked to your competitor.
For high-velocity sales and inbound conversion, batch scoring means you miss the buying window. Salesforce Einstein scores leads every 6 hours. If an attribute changes, it re-scores within the hour. That's better than daily batch, but still not real-time.
Explainability
Sales teams need to trust the score. That means they need to see the reasoning.
MadKudu's "glass box" approach shows which features drove the score. "This lead scored 87 because: VP title (+15), pricing page 3x this week (+25), Bombora surge (+20), 2 buying committee members identified (+15), ICP Tier 1 (+12)."
A black box that says "87" and nothing else? Reps ignore it. They go back to gut instinct. And then you've wasted the entire implementation.
AI Lead Scoring Tools Compared (Honest Assessment)
I hate vague claims. "AI scoring improves results." How much? Compared to what? Here are actual numbers.
Industry Benchmarks
Metric
Without AI Scoring
With AI Scoring
Improvement
Lead scoring accuracy
15-25%
40-60%
2-3x
Lead generation ROI
78%
138%
77% lift
Lead-to-deal conversion
Baseline
+51% increase
Significant
Lead servicing time
Baseline
-31% reduction
Study of 88K+ leads
Named results:
- DocuSign: 38% increase in SQLs and 22x ROI within 2 months of implementing predictive scoring (AltexSoft)
- Fivetran: 121% increase in in-market account engagement using Demandbase scoring
- Product-qualified leads (PQLs) convert at 25% average, up to 39% for $5-10K ACV deals. That's 2.8x better than general free users. And only 24-35% of companies actually implement PQL scoring (McKinsey/OpenView)
Warmly's Data
I can share these because they come from our own platform metrics and anonymized customer data.
18,000 accounts uploaded into TAM Agent. ICP filter applied. 44 high-intent targets remained. That's 0.24% qualifying. Without scoring, SDRs would work all 18,000
Less than 1% of website visitors match ICP. Automated scoring eliminates 99% noise before a human touches anything
11% LinkedIn Ads CTR when targeting scored buying committees. The average LinkedIn Ads CTR is 0.4-0.6%. That's roughly 20x better targeting from scoring
43% of attributable pipeline from AI-orchestrated touches. Scoring feeds the agentic workflows. Workflows create pipeline
Customers report saving 30+ minutes per account on manual research. The AI does the research. The human does the selling
Speed-to-Lead Impact
Response Time
Conversion Impact
Within 1 minute
391% boost
Within 5 minutes
21x more likely than 30-min delay
Average without automation
42 hours
With real-time scoring + action
3 minutes
61% of the buying journey is already completed before first contact with sellers (6sense 2025 B2B Buyer Experience Report). You can't waste the 39% that's left with a 42-hour response time. Real-time scoring closes this gap from hours to minutes. Check our case study for more on how this plays out in practice.
How to Implement AI Lead Scoring (Step by Step)
Phase 1: Foundation (Week 1-2)
Audit your data. Do you have 500+ contacts with outcomes? 3+ months of behavioral data? If not, start collecting now. Your model is only as good as its training data.
Define "qualified" with sales. Not marketing's definition. Sales' definition. Sit both teams in a room and get agreement on what makes a good lead. If they disagree, you've found the real problem. This alignment is more important than the AI model itself.
Pick your scoring approach:
- Full AI platform (Warmly, 6sense) for end-to-end scoring plus action
- CRM-native (HubSpot, Einstein) if you're deep in that ecosystem already
- Custom build (Clay formulas, your own models) if you have engineering resources and specific needs
Phase 2: Model Building (Week 3-4)
Configure the 7 Compound Score dimensions with weights that match your business. Not every dimension matters equally for every company. A PLG company might weight product usage (Intent) higher. An enterprise sales team might weight Committee Penetration higher.
Set score thresholds:
- 80+ = hand to sales immediately
- 60-79 = nurture sequence via AI marketing agents
- Below 60 = monitor and retarget with ads
Implement score decay. 30-day half-life for engagement signals. 90-day decay for intent. A stale high score is worse than no score at all.
Build negative scoring. Subtract points for: competitor domains, personal emails (gmail/yahoo for B2B), student/academic domains, low-quality form fills, non-buyer roles. This is critical for filtering noise.
Phase 3: Validation (Month 2)
A/B test. Route half of leads through AI scoring, half through your existing process. Track conversion rate, speed-to-lead, sales acceptance rate, and pipeline generated.
Validate against closed-won data. Are your top-scored leads actually converting at higher rates? If 50%+ of conversions come from leads your model didn't flag as top-tier, the model is wrong. MadKudu uses this as their diagnostic threshold.
Get sales feedback. Are reps trusting the scores? Are they acting on high scores quickly? If not, you have an adoption problem, not a scoring problem.
Phase 4: Optimization (Month 3+)
Connect scoring to automated workflows. High score triggers immediate outbound automation. Medium score goes into nurture. Low score gets ad retargeting only.
Add Compound Score dimensions progressively. Most companies start with Fit + Intent. Add Engagement tracking next. Then Committee Penetration. Activity Saturation and Cost Efficiency come last, once you have enough data on your own activity patterns.
Quarterly recalibration. Review conversion rates by score band, false positive/negative rates, and sales feedback. If conversion rates in your "high" band drop below expectations, the model has drifted. Retrain.
We've made these mistakes too. Our first scoring model over-weighted website visits and under-weighted buying committee signals. We caught it when single-person accounts kept scoring high but never closing. That's a $0 lesson if you learn it fast. Expensive if you don't.
Here are the 7 anti-patterns I see most often.
1. Scoring Individuals, Ignoring Buying Groups
The mistake: Treating each lead as an island. A junior researcher scores 85 because they downloaded three whitepapers. Meanwhile, five VPs at a better-fit company go completely unscored because no individual crossed the threshold.
The fix: Score accounts, not just leads. Use the Committee Penetration dimension. Five engaged contacts from Company ABC is a stronger signal than one highly scored individual from Company XYZ.
2. No Score Decay
The mistake: A lead engaged 90 days ago and went silent. They're still sitting at the top of the queue with a score of 82. Your rep wastes time chasing a ghost.
The fix: 30-day half-life on engagement signals. 90-day decay on intent. Signals decay because interest decays. Build the decay into the model or the model lies to you.
3. Over-Weighting Demographics
The mistake: A perfectly titled person who never visits your site, never opens emails, never engages with content. Score: 75, because VP of Sales at a 200-person SaaS company.
The fix: Behavioral signals (Intent + Engagement) should be 60%+ of total weight. A VP title matters. But a VP title with zero engagement is just a name in a database.
4. Ignoring Negative Signals
The mistake: Competitor employees researching your product score high. Students downloading whitepapers for class projects score high. Job seekers browsing your careers page score high.
The fix: Subtract points for competitor domains, personal emails, non-buyer roles, unsubscribes. Drift found this critical for filtering students and job seekers from their pipeline.
5. Building a Model Sales Doesn't Trust
The mistake: The model says "87." Sales says "Why?" You say "The AI decided." Sales ignores the score.
The fix: Glass box scoring that shows the reasoning. "This lead scored 87 because: VP title (+15), pricing page 3x this week (+25), Bombora surge (+20), 2 buying committee members identified (+15), ICP Tier 1 (+12)." Transparency creates trust. Trust creates adoption.
6. Scoring Without Action
The mistake: Beautiful scoring model. Scores update perfectly. Nobody does anything with them. The numbers sit in a CRM field that no one checks.
The fix: Every score threshold triggers a workflow. 80+ gets routed to sales immediately. 60-79 enters a nurture sequence. Below 60 goes into ad retargeting. A score that doesn't trigger action is analytics, not automation.
7. Never Recalibrating
The mistake: "We set up scoring in Q1. It's Q4. The model hasn't been touched."
The fix: Quarterly review. Check conversion rates in each score band. If your "high likelihood" leads aren't converting above your threshold over 30/60/90 days, the model has drifted. Markets change. Buyer behavior changes. Your ICP changes. The model needs to keep up.
Frequently Asked Questions
What is AI lead scoring?
AI lead scoring is the use of machine learning to automatically rank prospects by their likelihood to convert, using historical patterns, real-time behavioral signals, and third-party intent data. Unlike manual point-based scoring, AI models learn from outcomes and improve over time. The best systems go beyond conversion prediction to factor in action-readiness: what you've already done on an account and where the next productive action lives. This is the basis of the Compound Score framework.
How does AI lead scoring work?
AI scoring models analyze thousands of data points across your CRM, website behavior, email engagement, buyer intent tools, and third-party sources. They identify patterns that correlate with closed-won deals (and losses). The model assigns a score reflecting conversion probability, updating in real-time or batch cycles as new signals arrive. Advanced systems also incorporate score decay, negative scoring, and buying committee analysis.
What is the best AI lead scoring methodology for B2B?
For B2B, the most effective methodology combines fit scoring (firmographic/technographic ICP match), intent scoring (behavioral signals across 1st, 2nd, and 3rd party sources), and account-level buying committee analysis. The Compound Score method adds engagement tracking, activity saturation, recency decay, and cost efficiency to create a 7-dimension action-readiness framework. This outperforms single-dimension models because B2B deals involve 6-13 stakeholders and long sales cycles.
How do you implement an AI lead scoring system?
Start with data hygiene: clean your CRM, connect your GTM tools, and ensure 500+ contacts with outcome data. Define "qualified" with sales input. Choose a platform (full AI like Warmly, CRM-native like HubSpot, or custom-built). Configure score dimensions and thresholds (80+ = sales, 60-79 = nurture, below 60 = monitor). Validate with A/B testing against your existing process. Recalibrate quarterly. Most implementations take 2-8 weeks for basic scoring and 6-20 weeks for full Compound Score deployment depending on company size.
What data do you need for AI lead scoring?
At minimum: firmographic data (company size, industry, revenue), behavioral data (website visits, email engagement, content downloads), and outcome data (closed-won and closed-lost deals). Better models add technographic data (tech stack), intent data (third-party topic surges), product usage data (for PLG), and buying committee information. HubSpot requires at least 500 contacts and 3 months of historical data as a floor. More data always produces better models.
How accurate is AI lead scoring compared to manual scoring?
Traditional manual scoring achieves 15-25% accuracy. AI lead scoring reaches 40-60% accuracy, a 2-3x improvement. A 2025 peer-reviewed study in Frontiers in Artificial Intelligence confirmed that Random Forest and Gradient Boosting models significantly outperform manual methods. Companies using AI scoring see 138% ROI versus 78% without it. The accuracy gap grows with data volume: the more historical deals you feed the model, the better it performs.
What are the best AI lead scoring tools in 2026?
The top tools depend on your needs. Warmly ($799-$1,999/mo) for signal-layered scoring with real-time action. HubSpot ($90-$150/seat/mo) for CRM-native scoring. Salesforce Einstein ($215+/user/mo) for enterprise. 6sense ($25K-$100K+/yr) for account-based predictive scoring. MadKudu ($999+/mo) for transparent models. Clay ($149-$800/mo) for enrichment-powered custom scoring. Demandbase ($25K-$75K+/yr) for ABM buying groups. ActiveCampaign ($49+/mo) for budget-friendly automation.
How much does AI lead scoring cost?
Costs range from $49/month (ActiveCampaign) to $100K+/year (6sense enterprise). Mid-market options like Warmly start at $799-$1,999/mo. CRM-native options like HubSpot are $90-$150/seat/mo. Free tiers exist for basic functionality (HubSpot free CRM, 6sense free plan). The ROI typically justifies the investment: companies with scoring see 138% ROI vs. 78% without. DocuSign reported 22x ROI within 2 months of implementation.
What's the difference between lead scoring and lead grading?
Lead scoring measures behavioral engagement and intent: what a lead DOES (website visits, email clicks, content downloads, pricing page views). Lead grading measures demographic and firmographic fit: who a lead IS (job title, company size, industry, revenue). The best systems combine both. In the Compound Score framework, Fit covers grading (long-horizon intent) while Intent and Engagement cover scoring (short-horizon signals). You need both dimensions for accurate prioritization.
How often should you recalibrate your scoring model?
Quarterly at minimum. Review conversion rates by score band, false positive/negative rates, and sales feedback during each recalibration. If conversion rates in your "high" band drop below expectations over 30/60/90 days, the model has drifted and needs retraining. Self-learning models retrain automatically on new outcomes, but even these need human review quarterly to check for data quality issues, ICP changes, or market shifts.
Can AI lead scoring work without a CRM?
Technically yes, but it's significantly less effective. You can score based on website behavior, third-party intent, and enrichment data alone. But without CRM data on deal outcomes (closed-won, closed-lost), the model can't learn what good leads actually look like. For basic visitor scoring and visitor identification, you can start without a CRM. For Compound Score implementation, you'll need CRM integration to track Engagement and Activity Saturation dimensions.
What's the ROI of AI lead scoring?
Industry data shows companies with lead scoring achieve 138% ROI compared to 78% without, a 77% lift. DocuSign reported 22x ROI within 2 months. Fivetran saw 121% increase in in-market account engagement. A study of 88,000+ leads found AI reduced lead servicing time by 31%. The biggest ROI driver isn't usually model accuracy. It's speed-to-lead: real-time scoring that lets you respond in minutes instead of hours or days.
AI-Powered Sales Automation: Use Cases, Examples & Software
Time to read
Chris Miller
AI-powered sales automation is redefining how modern teams drive pipeline, close deals, and scale revenue without scaling headcount.
Instead of juggling endless manual tasks, reps now have intelligent systems that prioritize leads, personalize outreach, and trigger follow-ups in real time.
The best part is that the shift isn’t about replacing salespeople. It’s about amplifying what’s possible.
With the right setup, AI can act like a high-performing assistant that never sleeps, always knows what to do next, and helps your team spend more time selling instead of updating CRMs or chasing dead leads.
In this guide, I’ll break down the top use cases, real-world examples, and best software for AI-powered sales automation in 2025.
Whether you're building your stack from scratch or levelling up an existing process, you’ll walk away with practical ideas you can put to work today.
Let’s begin!
What is AI sales automation?
AI sales automation is using artificial intelligence to streamline, enhance, and often fully manage parts of the sales process that would otherwise require manual effort.
It goes beyond traditional rule-based automation by introducing machine learning, natural language processing, and predictive analytics to make smarter decisions and take dynamic action in real time.
Instead of just automating tasks like email scheduling or CRM data entry, AI can now score leads based on intent, recommend next-best actions, personalize outreach at scale, and even run entire outbound sequences autonomously.
Think of it as moving from static workflows to adaptive systems that respond to what’s happening in your pipeline.
And in 2025, the capabilities are no longer experimental - they’re driving real outcomes.
Whether it’s shortening sales cycles, reviving ghosted leads, or keeping reps focused on high-impact conversations, AI-powered sales automation is becoming the engine behind high-performing revenue teams.
Now let’s break down the benefits, plus what’s changing this year.
What are the benefits of AI-powered sales automation?
AI sales automation doesn’t just save time, as it also reshapes how sales teams operate.
Here’s what the top-performing teams are gaining:
Higher productivity - Reps spend more time selling and less time updating fields, chasing leads, or writing repetitive emails.
Smarter lead prioritization - AI scoring models surface the highest-intent prospects so teams can focus where it matters most.
Personalization at scale - Automated messaging no longer feels robotic, as AI can tailor content to personas, behaviors, and deal stage.
Faster follow-ups - AI systems can detect intent signals and trigger timely outreach within minutes, not hours or days.
Cleaner pipelines - AI keeps your CRM organized by enriching data, logging activity, and flagging stale or inactive deals automatically.
The result? More deals, less time wasted on manual tasks.
How has AI been changing the sales automation game in 2025?
In 2025, we’re seeing a major shift from static, rule-based automation to agentic AI that can take initiative.
These systems don’t just wait for inputs. They act. They reason. They adapt.
Here’s what’s new this year:
Autonomous sales agents are running entire sequences from lead research to initial outreach to reactivation.
Context-aware workflows use real-time signals (intent data, website activity, calendar behavior) to trigger hyper-relevant actions.
Cross-channel orchestration means your AI can coordinate across LinkedIn, email, calendar, and CRM - not just one platform or channel.
Continuous learning lets your system refine itself over time based on what’s working (and what’s not).
Collaborative AI tools like Warmly’s AI Copilot, for example, act more like trusted teammates than tools, making suggestions, surfacing insights, and even writing outreach based on deal context.
This evolution means sales teams are no longer just automating tasks.
Instead, they’re unlocking their full potential and gaining a huge competitive edge.
10 use cases of AI for sales automation
AI-powered sales automation is primarily about working smarter across the entire sales funnel.
From lead prioritization to post-demo follow-ups, AI is stepping in to handle what used to eat up hours of rep time.
The best part is that these aren’t theoretical use cases - they’re already powering real revenue results for modern sales teams.
Below, I’ll walk you through the most impactful ways companies are using AI to automate, optimize, and amplify their sales processes.
Let’s break them down one by one.
1. Intelligent lead scoring and routing
Not all leads are created equal, and treating them like they are wastes time, budget, and pipeline momentum.
That’s where AI-powered lead scoring comes in.
Traditional lead scoring often relies on fixed criteria: industry, company size, job title, or email engagement.
But these models are static and can’t adapt to shifting behaviors or real-time buyer intent. AI changes that.
Modern lead scoring models use machine learning to continuously analyze and rank leads based on a combination of historical win data, behavioral signals (like website activity or content downloads), intent data, and engagement patterns.
For example, if a VP of Sales visits your pricing page three times in one week and then clicks a demo CTA, AI knows that’s not just a good lead - it’s a hot one.
But it doesn’t stop at scoring.
Some advanced systems, like Warmly’s AI agents, can instantly route leads to the right rep, whether it’s by geography, segment, account ownership, or deal complexity.
This ensures that high-quality leads land in the right hands fast, instead of sitting in a generic queue.
The impact?
Reps waste less time on unqualified leads.
Sales cycles shorten thanks to faster response times.
Conversion rates increase because prospects are matched with reps who are most likely to close them.
It’s not just smarter - it’s a system that piles up value over time by learning what actually converts.
2. Hyper-personalized outreach
Generic sales outreach doesn’t cut it in 2025.
Buyers are overloaded, attention spans are short, and the fastest way to get ignored is to sound like everyone else.
AI changes the game by enabling hyper-personalized outreach at scale without the manual grunt work.
Here’s how it works:
AI tools can analyze a prospect’s job title, industry, website behavior, LinkedIn activity, previous engagement, and even company news or recent funding rounds.
It then uses that context to generate tailored messaging that speaks directly to the buyer’s needs, challenges, or goals.
Instead of sending “Hey {{first name}}, just checking in,” reps can send first-touch messages like:
“Saw you just launched a new partner integration - congrats! We’ve worked with several companies at that stage to streamline onboarding and reduce demo no-shows. Happy to share how.”
The difference? It reads like it was written by a human who did their research because, in a way, it was.
It not only drafts context-aware messages based on persona, stage, and previous activity - it can also suggest strategic talking points, tailor tone based on audience, and adapt messaging based on how a prospect responded to previous outreach.
As a result, you’ll get:
Faster onboarding time for new reps - No need to train them on how to write the “perfect email,” as the AI does the heavy lifting.
Higher reply rates - Because the messaging feels relevant, not like another mass-blast template.
Consistency across the team - Everyone sends messaging that aligns with your strategy and voice, without reinventing the wheel.
3. Follow-up automation triggered by buyer signals
Timing matters in sales, and nowhere is that more obvious than in follow-ups.
But too often, follow-up cadences are either rigid (send after X days) or forgotten altogether.
AI fixes this by triggering timely, relevant follow-ups based on real buyer behavior, not guesswork.
Modern AI systems track how prospects engage across multiple touchpoints, such as email opens, link clicks, meeting attendance, website activity, calendar interactions, and more.
Then, instead of waiting for a rep to notice a signal, the AI acts.
Let’s say a lead opened your pricing page three times in 24 hours but never responded to your last email.
AI can automatically:
Flag the lead as high intent.
Suggest a personalized re-engagement message.
Or trigger a new email sequence tailored to pricing questions.
If someone no-shows a call? The AI can reschedule automatically or follow up with a message like:
“Looks like the timing didn’t work - totally get it. Here’s a link to grab a new spot when it’s convenient.”
Warmly’s AI SDR excels here, layering follow-up logic across multiple data points (like meeting outcomes or email sentiment) so that messages feel natural and responsive rather than automated and forced.
This is what separates sales teams that stay top of mind… from those that get buried in inbox noise.
4. Meeting scheduling and optimization
In sales, meetings are gold.
But getting on a prospect’s calendar - and keeping that meeting - can be frustratingly inefficient.
Missed invites, double-bookings, late reminders, ghosting, it all adds up.
AI solves this by turning your calendar into a proactive sales asset, not just a scheduling tool.
AI-powered meeting optimization does more than just find free time slots.
It analyzes calendars, engagement history, deal stage, and even past no-show patterns to optimize when and how you reach out.
Here’s what that looks like in practice:
Smart scheduling suggestions - AI recommends the best time to reach out based on both parties’ availability, time zones, and meeting history.
No-show prevention - If a prospect has missed meetings in the past, AI can automatically send personalized reminders or even reschedule the call in advance.
Rescheduling automation - When a meeting gets canceled or rescheduled, the AI picks it up and handles the follow-up with no rep needed.
Context-aware prep - AI can also surface key info before a meeting (like account insights or LinkedIn activity) so the rep is fully briefed going in.
Warmly’s AI Copilot and SDR combined add even more firepower here, as they both help you book more meetings by automatically engaging leads and also preparing a detailed summary for each lead so you know exactly what to say every time.
The result?
Fewer no-shows.
Shorter time-to-meeting.
Smoother handoffs between stages.
Better-prepared reps.
5. Sales conversation analysis and coaching
Every sales conversation is full of signals, such as objections, buying intent, competitive mentions, and emotional cues.
But most of it gets lost the second the call ends unless someone manually reviews the recording (and let’s be honest - no one has time for that at scale).
That’s where AI steps in.
AI-powered conversation intelligence tools analyze sales calls, demos, and even emails in real-time.
They track everything from talk-to-listen ratios and filler words to competitor mentions and objection handling.
The result? A complete picture of how reps are performing without needing a manager to sit through hours of playback.
Here’s what these systems typically surface:
Performance insights - How well did the rep listen, handle questions, or transition between stages?
Opportunity spotting - Did the buyer mention a pain point or buying signal that was missed?
Coachable moments - Where could the rep have improved their delivery, tone, or messaging?
Deal risk indicators - Was the buyer disengaged? Did sentiment shift mid-call?
But it’s not just about reporting, it’s about real-time feedback.
Some tools now offer live cues during calls (e.g., “You’ve been talking too long,” or “Mention pricing before wrapping”), helping reps course-correct on the fly.
Why this matters:
Reps improve faster with targeted, actionable feedback instead of vague performance reviews.
Managers scale coaching without listening to every call.
Team-wide consistency improves, especially across remote or distributed sales teams.
In short, AI turns every conversation into a growth opportunity for both the rep and the revenue team.
6. Deal progression tracking and forecasting
Pipeline visibility is one of the biggest gaps in most sales orgs.
Reps often update deals based on gut feeling. Managers forecast with incomplete data. And leadership ends up making revenue decisions on shaky ground.
AI changes that by tracking deal progression in real-time, and turning forecasting into a science.
Instead of relying on rep-entered fields or outdated status labels, AI looks at what’s happening across channels, including:
Email and meeting frequency.
Stakeholder engagement.
Sentiment and intent signals.
CRM updates (or lack thereof).
Buying committee involvement.
Stage velocity compared to historical benchmarks.
From there, AI flags risks early.
If a deal hasn’t moved in days, key contacts stopped replying, or the engagement rate drops off after the demo, AI doesn’t wait - it alerts the rep or manager and suggests next steps to get things back on track.
Forecasting gets sharper, too.
AI can predict close probability based on deal behavior, not just pipeline stage.
It spots patterns (e.g., “Deals of this size usually take 14 days longer to close” or “This stakeholder profile tends to need an extra approval step”) and helps sales leaders forecast more accurately, down to the dollar.
This means your team gets:
Proactive deal management instead of reactive firefighting.
Higher win rates thanks to early intervention on risky deals.
More accurate forecasts grounded in actual behavior, not hope.
7. Pipeline cleanup and CRM hygiene
Your CRM should be a source of truth.
But for most sales teams, it’s more like a graveyard of stale deals, duplicate records, outdated contacts, and incomplete notes.
And when reps are drowning in admin or don’t trust the data, pipeline reviews turn into guesswork.
AI solves this by quietly doing the dirty work, such as cleaning, enriching, and maintaining your CRM without needing rep input.
Here’s how it works:
Auto-enrichment - AI pulls in missing fields like company size, tech stack, contact roles, and intent signals from external data sources.
Duplicate detection - It identifies and merges duplicate records based on matching patterns and not just email addresses, so you don’t end up with three versions of the same deal.
Dead deal detection - If a deal has been inactive for 30+ days, the AI can automatically flag it, downgrade it, or suggest an exit sequence.
Task cleanup - Missed follow-ups, outdated to-dos, or meetings that never happened? AI clears the clutter or nudges the rep with next steps.
Activity logging - Some systems can even auto-log emails, calls, and meeting notes by pulling from connected tools, meaning there’s zero manual entry needed.
This kind of behind-the-scenes automation is critical, especially as teams scale.
Dirty data leads to missed revenue, bad handoffs, and low forecasting accuracy.
And the longer it goes unchecked, the harder it is to fix.
Warmly’s AI Marketing Ops agent plays a role here, too, ensuring that your ICP is always fresh and relevant and enriching your CRM with up-to-date, accurate lead data.
The outcome?
Cleaner data without burning rep hours.
Higher pipeline confidence at every level.
Better decision-making from leadership based on accurate, real-time data.
8. Reactivation of ghosted leads
Every sales team has them - leads that showed interest, booked a demo, maybe even had multiple calls… and then vanished.
Ghosted deals clog up pipelines and drain rep morale. AI brings these leads back to life.
Rather than leaving it to reps to remember who to chase and when, AI tracks stalled opportunities and launches tailored reactivation plays automatically.
Here’s what that might look like:
Behavioral monitoring - AI flags when a lead hasn’t replied in X days, skipped a scheduled call, or disengaged from content.
Contextual nudges - Based on previous interactions, AI generates re-engagement messages that feel personal, not desperate. For example: “Totally understand things get busy. Just wanted to check in - happy to revisit when the timing’s better.”
Multi-touch workflows - AI can deploy a gentle sequence across email, LinkedIn, and calendar, adjusting tone and timing based on persona or stage.
Offer new angles - If the original messaging didn’t land, AI can pivot with new content (e.g., case studies, ROI calculators, product updates) based on the lead’s industry or role.
And it’s not just about rekindling the deal, it’s about learning.
AI tracks what reactivation strategies work best and adapts over time, getting smarter with every ghosted lead it revives.
Warmly’s AI SDR and Copilot are built with this in mind.
They help you detect when deals are slipping, craft the right message to pull them back in, and even flag leads who might be more ready now than they were months ago.
This means that AI doesn’t simply give up when a lead goes dark. Instead, it waits, listens, and re-engages when the time is right.
9. Real-time sales insights and recommendations
Sales moves fast.
Waiting until the weekly pipeline review to spot issues or find opportunities is too late.
AI gives your team a real-time edge by surfacing insights, recommendations, and next steps while deals are still in motion.
Instead of digging through dashboards or piecing together scattered notes, reps and managers get instant visibility into what’s working, what’s stalled, and where to focus next.
Here’s what AI can deliver in real-time:
Deal health alerts - “This prospect hasn’t responded in 10 days - follow up?”
Next-best-action suggestions - “Send pricing breakdown now,” or “Loop in a technical stakeholder.”
Rep performance feedback - “High talk ratio in last 3 calls - consider asking more discovery questions.”
Content recommendations - “Based on stage and persona, send this new case study.”
Live win/loss patterns - “Deals with VP-level involvement close 40% faster, escalate this thread.”
Warmly’s AI Copilot excels here, layering insights directly into your workflow, so there’s no switching tools or chasing reports.
Whether it’s surfacing who to follow up with today or flagging a deal that’s drifting off course, the intelligence is right where you need it, when you need it.
This is where AI really shines - not by replacing reps, but by making them sharper, faster, and more effective in every moment that counts.
10. Social media engagement automation
In B2B sales, LinkedIn isn’t just for recruiting.
It’s where conversations start, trust is built, and deals are often warmed even before the first DM.
But keeping up with social activity across dozens (or hundreds) of prospects is overwhelming.
AI solves this by automating thoughtful, timely engagement that drives pipeline, not just impressions.
Here’s how AI-powered social engagement works:
Post monitoring - AI tracks posts from target accounts or ICPs and flags the ones worth engaging with based on topic, relevance, or sentiment.
Comment suggestions - Instead of dropping generic “Great post!” replies, AI recommends meaningful comments that add value or spark conversation, aligned with the prospect’s role and your offering.
Engagement sequencing - AI helps build visibility before outreach by liking posts, commenting consistently, then timing the connection request or DM when familiarity has already been built.
Personalized DM suggestions - Based on public posts or shared content, AI can draft custom LinkedIn messages that reference recent activity naturally.
Warmly’s AI agents are already enabling this by sending personalized LinkedIn DMs and connection requests and monitoring for relevant social signals 24/7, such as posts mentioning you or the problems you solve, engagement, industry leaders’ activity, etc.
The bottom line?
In 2025, social selling is no longer optional, and AI is making it scalable, consistent, and human.
The 4 best AI sales automation solutions in the market in 2025
With so many AI-powered platforms on the market, it’s easy to get overwhelmed.
The best tools do more than just automate tedious tasks, as they’re built to actively drive revenue by helping reps sell smarter, faster, and with more context.
Below are the top AI sales automation solutions leading the charge in 2025.
1. Warmly
Warmly is built for modern B2B teams that want to automate sales outreach without losing the human touch.
Unlike legacy tools that rely on static rules or blast sequences, Warmly’s AI agents act with intent, driving outreach, follow-ups, and deal progression based on real-time buyer behavior.
Whether you’re looking to scale SDR operations, re-engage ghosted leads, or personalize follow-ups at speed, Warmly blends sales automation and intelligence seamlessly, without adding friction to your reps’ workflow.
Standout Features
4 specialized AI agents designed to own different GTM processes - From top-of-funnel prospecting to demand generation and marketing ops, each agent (AI SDR, Demand Gen, Copilot, and Marketing Ops) is purpose-built to handle specific parts of your sales funnel with precision.
Multi-channel orchestration across email, LinkedIn, and calendar - Outreach flows smoothly across the channels your buyers use, with AI coordinating the right message, in the right place, at the right time.
Persona-aware, context-driven outreach that adapts to real-time signals - Messaging is tailored automatically based on buyer role, intent signals, behavior, and stage, so your outreach always feels timely and relevant.
Seamless CRM integration with automated data cleanup and enrichment - Keeps your pipeline accurate and actionable by enriching lead data, logging activity, and cleaning up messy records without human input.
Buying signals tracking - The platform tracks first, second, and third-party buying signals at the person level, allowing you to easily and accurately detect your hottest leads in real-time and create smart audience segments.
AI Chat - AI-driven chatbot that engages and qualifies leads, books meetings, and provides collaterals while making sure that each interaction is highly personalized and tailored to each lead.
Pricing
Warmly offers a free forever plan that allows you to reveal up to 500 monthly visitors, set up ICP filters to quickly identify high-quality leads, and automate basic lead routing.
If you need more, there are three tiers to choose from:
Data Only: Starts at $499/mo when billed monthly or $4,000 when billed annually, lets you identify up to 5,000 monthly visitors, first-party intent signals, alerts, and access to Warmly’s B2B prospecting database.
Business: Starts at $19,000/year for up to 10,000 visitors or $45,000/year for up to 75,000 visitors, everything in Data Only, plus third and second-party signals, sales orchestration, AI Chat, and lead routing.
Enterprise: Custom pricing, custom number of visitors, everything in Business, plus custom signals and warm calling.
2. Outreach
Outreach has long been a staple in the sales engagement space, and its newer AI-powered capabilities take automation to the next level.
It combines email sequencing, call tracking, and forecasting tools, now enhanced with AI-driven insights that guide reps toward the right actions.
Ideal for mid-market and enterprise teams with more complex sales cycles, Outreach offers robust orchestration with enterprise-grade controls.
Standout Features
Smart email sequencing with reply classification and optimization - Outreach uses AI to detect sentiment and intent in replies, automatically adjusting sequence logic to optimize timing, tone, and follow-up content.
AI-powered deal health scoring and pipeline insights - The platform tracks engagement across email, calls, and meetings to assess which deals are on track and which need attention, giving reps and managers clear direction.
Sales forecasting backed by behavioral data - Instead of relying on static pipeline stages, Outreach leverages deal activity, response patterns, and rep behavior to produce more accurate, AI-driven forecasts.
Pricing
Outreach has five different product packages:
Engage: Includes email assistant, automations, CRM sync, templates, etc.
Call: Includes sales dialer, live sales monitoring, AI-powered call summary, etc.
Meet: Includes real-time call recording and transcription, AI-powered meeting assistant, automated summary and action items, etc.
Deal: Includes AI-powered deal assistant, deal health score, deal overview with activity history, etc.
Forecast: Includes AI projection, scenario planning, detailed dashboards, etc.
However, the platform doesn’t publish prices for any of its packages, so you’ll have to contact its sales team.
Keep in mind, though, that each package has separate pricing, meaning the costs can easily add up if you want to use more than one of its product suites.
3. Apollo.io
Apollo.io merges lead generation, engagement, and enrichment into one AI-powered platform.
It’s particularly strong for teams that need to build pipeline fast, offering access to a massive contact database and the ability to launch targeted outbound campaigns directly from the platform.
With built-in email personalization, sequencing, and sales intelligence, it’s a great choice for start-ups or scrappy sales teams looking for end-to-end automation.
Standout Features
Contact data enrichment + dynamic list building - Apollo gives you access to a massive, constantly updated B2B database and fills in missing lead details automatically, helping you build hyper-targeted lists in minutes.
AI-assisted email writing and personalization - Generates tailored outreach messages based on persona, company data, and recent activity, so every email feels like it was written just for that prospect.
CRM integration for seamless sync - Syncs directly with tools like Salesforce and HubSpot to keep your pipeline clean and up to date with no duplicate contacts or missed activity logs.
Pricing
Apollo has a free forever plan that includes 100 email and mobile phone finder credits, basic filters and prospecting, and two sequences.
Clari is less about outreach and more about revenue intelligence.
It uses AI to track deal movement, forecast more accurately, and highlight pipeline risks in real time.
This makes it perfect for RevOps leaders and sales managers who need visibility into what’s really going on with deals.
It doesn’t automate outbound, but it helps teams win more by ensuring focus is placed on the right accounts at the right time.
Standout Features
AI-powered forecasting based on multi-signal analysis - Clari pulls data from emails, CRM updates, meeting activity, and call notes to generate highly accurate sales forecasts based on real-time behavior, not gut instinct.
Pipeline visibility across reps, teams, and quarters - Get a crystal-clear view of what’s in pipeline, what’s at risk, and what’s likely to close, broken down by rep, team, segment, or period.
Revenue intelligence dashboards for RevOps - Turns data into action with dashboards that highlight conversion rates, deal velocity, forecast gaps, and rep performance trends.
Pricing
Clari doesn’t disclose its price or information about any distinct product packages.
You have to contact its sales team for a custom quote.
The emerging sales technologies you should be looking forward to
AI has already changed the game, but the biggest shifts in sales tech are still ahead.
What’s coming next isn’t just faster tools - it’s entirely new ways of selling, powered by intelligence, automation, and autonomy.
Here’s what’s on the horizon:
1. AI-native CRMs
Forget static fields and clunky dashboards.
A new generation of CRMs is being built from the ground up around AI, capable of automating data entry, surfacing insights proactively, and functioning more like a co-pilot than a record-keeper.
2. Buyer journey intelligence
Instead of tracking just emails and meetings, emerging platforms will map the entire digital journey, such as site visits, content consumption, and social engagement.
Those insights will then be turned into actionable plays for sales and marketing in real-time, equipping reps with all the info they need to close more deals.
3. Agent-to-agent selling
We’re entering a world where AI agents don’t just work for sellers - they interact with each other.
Your AI SDR may soon sync with a buyer’s procurement agent, scheduling meetings, aligning needs, and progressing deals with minimal human intervention.
4. Emotion-aware conversation tools
Advanced AI models are starting to detect tone, sentiment, and emotional shifts during calls and emails.
That means real-time coaching could soon include prompts like “She seems hesitant - pause and clarify pricing” as the conversation happens.
5. Autonomous campaign pilots
Imagine running a full outbound campaign, including strategy, copy, targeting, and optimization, entirely powered by AI.
These "autonomous GTM pilots" are already in testing and may soon redefine what one-person teams can do.
The best part?
All these tools won’t be replacing sellers. They will simply reshape what sales teams are capable of.
The next generation of sales tech won’t just support your sales. It’ll amplify it, adapt to it, and run it alongside you.
Next steps: Scale smarter with AI-powered sales automation
AI-powered sales automation isn’t just a shortcut - it’s a strategic advantage.
The teams winning in 2025 aren’t the ones sending more emails or updating CRMs faster. They’re the ones building systems that think, act, and improve on their own.
From intelligent lead scoring to LinkedIn-powered warmups and post-meeting follow-ups, the game has changed, and the best tools are doing more than automating steps.
They’re unlocking new ways to scale pipeline, revive deals, and close faster with fewer resources.
And with Warmly, you don’t need to duct-tape five tools together to get there.
Want to see what true sales automation looks like when it’s actually intelligent?
Book a Warmly demo and meet your next favorite teammate: AI that helps your pipeline run itself.
What is AI Powered Sales Automation Use Cases, Examples & Software?
AI Powered Sales Automation Use Cases, Examples & Software refers to the concepts and strategies covered in this article. Understanding these fundamentals helps B2B teams improve their sales and marketing effectiveness.
Why is AI Powered Sales Automation Use Cases, Examples & Software important?
This matters because it directly impacts pipeline generation and revenue. Teams that master these concepts see better results from their go-to-market efforts.
How can I implement this?
Start with the strategies outlined above. For B2B teams, combining these tactics with tools like Warmly—which identifies website visitors and automates engagement—can accelerate results.
What tools help with AI Powered Sales Automation Use Cases, Examples & Software?
Several tools can help, depending on your specific needs. Warmly is particularly useful for identifying high-intent website visitors and engaging them before they leave your site.
What are the best practices for AI Powered Sales Automation Use Cases, Examples & Software?
Key best practices are covered throughout this article. Focus on the fundamentals first, measure your results, and iterate based on data rather than assumptions.
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