AI lead scoring is quietly becoming one of the most powerful tools in a revenue team’s stack.

Instead of relying on gut instinct or outdated point systems, it uses real-time behavioral and firmographic data to surface your most promising prospects before your competitors even know they’re ready.

The difference is night and day: more focus, less guesswork, and higher conversion rates across the board.

But great lead scoring doesn’t just happen.

You need the right data, the right signals, and an AI model that actually learns from your sales outcomes - not just form fills.

In this article, I’ll break down what AI lead scoring is, how it works, and how to set it up the right way in 2025.

Let’s dive in.

What is AI lead scoring & how does it work?

AI lead scoring is the process of using machine learning models to automatically evaluate and rank leads based on how likely they are to convert.

Instead of assigning arbitrary points for actions like email opens or job titles, AI-powered scoring looks at patterns across thousands of historical deals, analyzing:

1. What qualified leads do.
2. Who they are.
3. How they behave across channels.

The AI model pulls in data from your CRM, website, email engagement, LinkedIn, intent signals, and more, and continuously learns what separates high-quality leads from dead ends, updating your scoring model in real time.

This means your team isn’t stuck adjusting static rules or second-guessing who to prioritize. 

With AI lead scoring, the system tells you who’s ready and why, so you can act faster, personalize outreach, and focus your energy where it matters most.

What are the benefits of AI lead scoring?

AI lead scoring isn’t just about saving time (though it does plenty of that).

It’s about making better decisions at every stage of the funnel.

Here’s what that looks like:

1. More accurate prioritization - AI looks at behavioral trends, not just surface-level data, giving you a much clearer picture of who’s sales-ready.
2. Higher conversion rates - Reps engage the right leads, at the right time, with the right message based on real-time insights.
3. Shorter sales cycles - By focusing on high-intent prospects, you avoid wasting cycles on leads that were never going to close.
4. Better marketing-to-sales alignment - Everyone works from the same source of truth about lead quality, meaning there’s no more guesswork or finger-pointing.
5. Continuous improvement - AI models evolve as your business changes, improving accuracy over time without manual reconfiguration.

What are the different use cases for AI lead scoring?

AI lead scoring isn’t one-size-fits-all. 

Instead, it adapts to different GTM strategies. 

Some of the most common use cases include:

• Inbound lead routing - Automatically send high-scoring leads to sales while lower scores go into nurturing workflows.
• Outbound prioritization - Use AI to surface warm accounts in your target list that match the profile of past closed-won deals.
• Product-led growth (PLG) - Score users based on in-app behavior to identify when they’re ready to convert or expand.
• ABM campaigns - Focus efforts on accounts that not only fit your ICP but are showing intent or engagement signals in real-time.
• Lead reactivation - Find old or cold leads that are warming back up, even if they haven’t hit obvious conversion triggers yet.

This means that when done right, AI lead scoring doesn’t just help you sort leads - it helps you uncover revenue you would’ve otherwise missed.

How can you implement AI-powered lead scoring in your sales operations?

AI lead scoring sounds powerful - and it is - but getting it right means more than just turning on a tool.

To make it work for your team, you need the right data foundation, alignment across departments, and a feedback loop that keeps the model learning and improving.

Here’s how to implement AI-powered lead scoring in a way that drives actual results:

1. Start with clean, connected data

Before any AI model can score leads accurately, it needs a clear and complete picture of your pipeline. 

That starts with data hygiene and system integration.

AI lead scoring thrives on context, so the more relevant, timely, and connected your data sources are, the better your results will be. 

If your CRM is missing key fields, your web analytics aren’t tied to individual leads, or marketing and sales data are siloed, your model will struggle to deliver meaningful insights.

Here’s what you need to do:

• Unify your GTM tools - Connect your CRM (like Salesforce or HubSpot) with your marketing automation, email platforms, product analytics, ad tools, and any enrichment platforms (e.g., tools like Clearbit and ZoomInfo).
• Standardize key fields - Make sure lead and account records include accurate firmographic, demographic, and behavioral data. Clean up duplicates, fix inconsistent formatting, and remove outdated entries.
• Track engagement everywhere - AI models rely on signals like page views, email opens, demo interactions, product usage, and social engagement. If these aren’t consistently captured and linked to leads/accounts, you’re flying blind.
• Ensure time-based accuracy - Timestamped events matter. AI uses them to model deal velocity, buying stages, and urgency. If timestamps are missing or incorrect, it can mess up everything.

2. Define what a “qualified” lead looks like

Before AI can start ranking leads effectively, it needs a target. 

That target is your definition of a qualified lead, but not just any definition. It needs to reflect real-world buying behavior, not just marketing assumptions.

Many teams skip this step or rely on outdated criteria like job title + company size. 

The result? AI models that optimize for leads that look good on paper, but don’t convert.

Instead, ask: What behaviors, characteristics, or patterns consistently show up in deals that move forward?

Here’s how to build a definition that works:

• Study your best customers - Look at the paths they took, including how many touchpoints, what content they consumed, how long they stayed in each stage, and who was involved.
• Include firmographic + behavioral signals - Ideal leads are not just a good company fit. They’re the accounts that take meaningful actions. Your definition should combine traits (like industry or role) with behaviors (like attending a webinar, inviting colleagues, or hitting usage milestones).
• Segment by funnel stage - What qualifies a lead at the top of the funnel (MQL) may be very different from mid-funnel (SQL) or late-stage handoffs. Define these thresholds clearly so the AI model can recognize stage-specific intent.

Once your team is crystal clear on what “qualified” really means, that becomes the blueprint the AI model uses to learn and score accordingly.

3. Choose the right AI scoring platform

Not all AI lead scoring tools are created equal, and choosing the wrong one can mean messy data, unclear insights, and wasted time.

The right platform should do more than just assign a number to each lead. 

It should help you understand why that score exists, surface the right actions to take next, and evolve as your GTM operations evolve.

Here’s what to look for:

• Custom scoring models trained on your data - Avoid black-box tools that apply generic logic across all companies. 

Look for platforms like Warmly that train on your historical wins and losses, so scoring reflects what works in your pipeline.

• Real-time scoring and updates - Lead scores should evolve as new data comes in, such as visits, replies, firmographic changes, or product usage. 

If your platform updates weekly (or not at all), your reps are flying blind.

• Score explainability - Your team should see why a lead scored high or low. 

The best platforms highlight which signals contributed to the score, e.g., intent activity, firmographics, website visits, so reps can take smarter, more personalized actions.

• Native integrations with your GTM stack - Scoring data is only useful if it flows where your team works, such as your CRM, marketing automation, Slack, or sales engagement tools. 

➡️ Prioritize platforms with seamless bi-directional integrations.

• Workflow activation - Look for tools that don’t just score leads but trigger next steps, such as routing, enrichment, notifications, and outbound sequences, based on those scores.

And most importantly, choose a platform that your sales and marketing teams will use. 

If the scoring model lives in a dashboard nobody checks, it’s not driving revenue - just noise.

P.S. I’ll cover some of the best tools for AI lead scoring further below, so make sure you stick to the end.

4. Feed the model your historical wins (and losses)

AI lead scoring isn’t magic - it’s simple pattern recognition. 

However, to find the right patterns, your model needs high-quality training data that reflects what success and failure actually look like for your team.

That means going beyond surface-level lead data and digging into the full buyer journey, from first touch to closed-won (or lost). 

The more context your AI has, the better it can distinguish between leads that look promising and ones that convert.

Here’s how to feed your model effectively:

• Use full-funnel data, not just top-of-funnel metrics - Don’t train your model only on form fills or MQLs. 

Include deal outcomes, sales notes, product usage data, and post-sale retention metrics. A lead that converted but churned in 30 days may not be your ideal profile.

• Include both good and bad examples - It’s just as important for your AI to learn what a bad lead looks like. 

Include no-shows, cold leads, bounced emails, and stalled deals so it can learn the differences in patterns and engagement.

• Cover a meaningful sample size - For most models, the more training data, the better. 

You don’t need millions of records, but aim for at least several hundred examples of both wins and losses for the model to work with confidence.

• Map multiple signal types - Feed the model a mix of structured (firmographics, campaign engagement, CRM fields) and unstructured data (notes, call transcripts, product behavior). 

The richer the context, the sharper the scoring.

Get this part right, and the model becomes your most insightful sales analyst, running 24/7, without ever burning out.

5. Align scoring with real-time sales operations

Scoring leads is only half the battle. 

If those scores aren’t tied to actionable next steps inside your sales workflow, they’ll just sit in a dashboard collecting dust.

The real power of AI lead scoring comes from what it activates: instant routing, prioritized outreach, tailored messaging, and faster follow-ups, all based on how hot or cold a lead is right now.

Here’s how to make scoring data drive actual pipeline:

• Auto-route hot leads - Push high-intent leads directly to the right rep based on territory, persona, or product line. Don’t make someone manually sort through them in the CRM.
• Trigger smart alerts - Send real-time Slack or email notifications when a lead crosses a score threshold or takes a key action, like visiting a pricing page, watching a demo, or inviting a teammate.
• Personalize outreach based on score drivers - Show reps why a lead scored high. Was it job title, company size, or engagement behavior? Use that intel to craft targeted messaging fast.
• Adapt cadences based on lead score - High-scoring leads might skip nurture steps and go straight to personalized outbound. Lower scores might trigger slower, long-term drips or enrichment.

The goal here is simple: score → surface → act.

Lead scoring should accelerate your team’s speed to engage, not add another layer of complexity.

When it’s fully wired into your motion, AI lead scoring becomes a tactical advantage: your team always knows who to talk to, when to reach out, and what to say.

6. Monitor results and refine

Once your AI lead scoring is live, the job’s not done. 

To make sure it’s truly driving impact, you need to treat it like a living system, constantly monitored, tested, and improved.

Because here’s the truth: even with clean data and a well-trained model, things can break. 

Maybe reps aren’t acting on scores. Maybe high-scoring leads are stalling. Maybe your ICP has shifted subtly without anyone realizing.

That’s why ongoing refinement is key. 

Here’s how to keep your scoring system sharp:

• Track conversion rates - Are your top-tier leads closing more often? Is there a drop-off between high and mid scores? 

Use these insights to recalibrate scoring thresholds and priorities.

• Analyze rep behavior - Are reps engaging with high-scoring leads quickly? Are they using the insights surfaced by the model? 

If not, you may need to revisit how scoring is integrated into workflows.

• Compare pre- and post-scoring performance - Look at KPIs like win rates, average deal size, sales cycle length, and lead response times before and after implementing AI lead scoring. 

The difference should be clear.

• A/B test scoring-driven automations - Run experiments with and without score-triggered workflows (e.g., routing, cadences, alerts) to understand what’s actually moving the needle.

And don’t worry, refinement doesn’t mean overhauling everything every month.

It means staying curious, asking the right questions, and using performance data to continuously tighten the loop between scoring and outcomes.

With regular monitoring and iteration, AI lead scoring becomes a vital asset that improves not just how you prioritize leads, but how you close them.

The 4 best AI lead scoring tools on the market in 2025

So, if you're ready to implement AI lead scoring, choosing the right tool is where it all begins. 

Below are four of the best platforms on the market in 2025, each offering distinct strengths and primary use cases.

Let’s start with the solution that was built from the ground up for modern GTM teams.

1. Warmly - Best for dynamic, real-time lead scoring tied to sales action

Warmly isn’t just another lead scoring tool.

It’s a full-stack AI GTM engine that helps your team identify, prioritize, and convert high-quality leads faster.

Unlike traditional systems that rely solely on firmographics or generic fit scores, Warmly uses advanced AI agents to build custom ICP models, monitor real-time buying signals, and trigger intelligent routing workflows, so the right rep is alerted at the exact right moment.

Standout Features

• AI-powered ICP identification - Warmly uses AI to continuously analyze historical wins and surface the true patterns behind your best customers, helping you define your ICP based on real customer traits, beyond demographics.
• Real-time signal monitoring - Warmly combines warm lead signals, firmographics, and intent data from 10+ enrichment sources. Scores update in real-time with no lag or guesswork.
• Intelligent lead routing & alerts - The platform routes hot leads instantly to the right rep and triggers Slack alerts so your team can engage while interest is high.
• AI-orchestrated outreach workflows - You can automatically direct every lead to the next best step based on their real-time intent, whether that’s a personalized outbound sequence, a tailored ad campaign, or an instant on-site offer. 

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:

1. 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.
2. 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.
3. Enterprise: Custom pricing, custom number of visitors, everything in Business, plus custom signals and warm calling.

2. MadKudu - Best for fast-growing SaaS companies

MadKudu focuses on predictive lead scoring tailored to high-volume inbound funnels. 

It uses historical conversion data to predict which leads are likely to become customers, particularly in product-led or content-driven growth environments.

Standout Features

• Predictive lead scoring - MadKudu analyzes your historical customer data (e.g., closed deals, trial conversions, and retention metrics) to build predictive scoring models that surface high-potential leads early in the lead generation funnel.
• Fit + intent model segmentation - Leads are categorized by both fit (firmographics, role, industry) and intent (behavioral signals like email engagement or product usage). This dual approach helps teams prioritize leads who both match your ICP and are showing real buying signals.
• Rich in native integrations - Seamlessly integrates with tools like Salesforce, HubSpot, Marketo, and Segment, pushing lead scores directly into workflows your team already uses. 

Pricing

MadKudu doesn’t publish prices for its product.

You can book a demo and request a custom quote based on your specific requirements.

3. 6sense - Best for ABM-focused enterprise teams

6sense is a robust B2B revenue platform that blends lead scoring with deep intent data and account-based orchestration. 

Its AI surfaces buying-stage insights at the account level, making it great for enterprise sales processes.

Standout Features

• Account-based predictive scoring - 6sense scores not just leads, but entire buying committees, using AI to evaluate engagement across contacts within an account. 
• Buyer stage identification - The platform automatically detects which stage each account is in (researching, considering, or ready to buy), based on behavior across digital channels, making it easier to time outreach effectively.
• Deep intent signal tracking - 6sense captures and analyzes behavioral intent data from across the web, including third-party research activity, website visits, and ad interactions, to identify hidden demand and surface in-market accounts early.

Pricing

6sense’s pricing has a free plan that provides 50 research credits/month and features like contact & company search, sales alerts, and a Chrome extension.

If you need more, you can upgrade to one of three plans:

1. Team: Includes everything in Free plus technographics, psychographics, and web, CRM, and SEP apps.
2. Growth: Everything in Team plus keywords intent, 3rd party intent, and corporate hierarchy.
3. Enterprise: Everything in Growth plus predictive AI model, AI-recommended actions, and CRM & MAP activity.

However, there are no prices disclosed for any of the plans, meaning you’ll have to contact 6Sense’s sales team for details.

4. Leadspace - Best for data-rich B2B orgs with complex ICPs

Leadspace focuses on B2B lead and account intelligence, offering AI-powered scoring based on a vast array of firmographic, technographic, and intent data sources. 

It’s especially useful if you’re targeting nuanced enterprise buyer personas.

Standout Features

• Unified lead, contact, and account scoring - Leadspace combines data across individuals and entire buying groups, scoring not just leads but accounts and personas based on how well they match your ideal customer profiles and buying behavior.
• Extensive data enrichment - Leadspace builds a complete, real-time picture of every lead or account, leveraging firmographic, technographic, intent, and third-party enrichment data from multiple sources, ensuring your scores are based on the fullest context possible.
• Custom AI scoring models - Lets you tailor your scoring to your GTM strategy with models trained on your internal data and aligned with your specific sales outcomes.

Pricing

Leadspace has three packages:

1. Profile Essentials: Includes 250K profiles (companies and people), 12 intent topics, enrichment data across sources, standard persona library for profile discovery and scoring, intent models, integrations, etc.
2. Funnel Essentials: Everything in Profile Essentials, plus AI-driven features that let you precisely target the right people at the right time, such as custom persona and intent model.
3. Funnel Optimization: Everything in Funnel Essentials, plus a custom FIT model. This is the most advanced package designed for complete funnel management. 

Leadspace also opted against publishing prices for its products, so you’ll have to contact its team for a quote.

What you should keep in mind when implementing AI lead scoring

Finally, while AI lead scoring can dramatically accelerate pipeline quality and sales velocity, it can only do that if it’s implemented with the right foundation and mindset.

AI lead scoring works best when it's treated as a living part of your GTM engine, not a static checkbox.

So, if you get the essentials right and keep refining, you’ll unlock a sharp competitive edge that gets stronger every month.

Here are key considerations to keep in mind as you roll it out:

1. Your data quality is everything

AI models are only as smart as the data you feed them. 

If your CRM is full of duplicates, missing fields, or outdated records, scoring accuracy will suffer. 

Prioritize data hygiene and integration before expecting great results.

2. Lead scoring ≠ lead qualification

AI scoring helps prioritize leads, not qualify them. 

It’s a directional tool, not a replacement for good sales judgment. 

Make sure reps treat the score as context, not something set in stone.

3. Scoring needs to align with your GTM goals

Are you optimizing for demo bookings? Self-serve conversions? Expansion? 

Your scoring model should reflect your core objectives and evolve alongside your GTM strategy.

4. Reps need visibility into why a lead scored high

If your team doesn’t trust the score, they won’t use it.

Make sure your platform offers transparent insights, so reps know exactly which signals are driving lead prioritization.

5. Scoring is only useful if it triggers action

High scores sitting in a CRM don’t drive revenue. 

Make sure you’re connecting your scoring engine to the right workflows, such as lead routing, Slack alerts, personalized outreach, nurture sequences, and more, for optimal results.

Next steps: Turn your lead scoring into revenue

By now, you understand that AI lead scoring isn’t just another tool to bolt onto your tech stack.

Instead, it’s a strategic lever that can shift how your entire GTM team operates.

Done right, it helps you cut through noise, focus on the right prospects, and engage at the exact right moment.

But to unlock its full potential, you need more than a scoring engine. 

You need a system that connects scoring to action by surfacing insights, routing leads, and kicking off workflows in real time.

And that’s exactly what Warmly was built for, with its wide range of AI-driven functionality that helps you identify who your warmest leads are right now and engage them in personalized outreach while they’re still hot.

So, if you’re ready to stop guessing and start prioritizing the leads that will actually convert, Warmly’s AI-powered lead scoring engine is your edge.

Book a demo to see how Warmly can help you drive more pipeline on autopilot.

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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:

1. Higher productivity - Reps spend more time selling and less time updating fields, chasing leads, or writing repetitive emails.
2. Smarter lead prioritization - AI scoring models surface the highest-intent prospects so teams can focus where it matters most.
3. Personalization at scale - Automated messaging no longer feels robotic, as AI can tailor content to personas, behaviors, and deal stage.
4. Faster follow-ups - AI systems can detect intent signals and trigger timely outreach within minutes, not hours or days.
5. 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:

1. Autonomous sales agents are running entire sequences from lead research to initial outreach to reactivation.
2. Context-aware workflows use real-time signals (intent data, website activity, calendar behavior) to trigger hyper-relevant actions.
3. Cross-channel orchestration means your AI can coordinate across LinkedIn, email, calendar, and CRM - not just one platform or channel.
4. Continuous learning lets your system refine itself over time based on what’s working (and what’s not).
5. 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?

1. Reps waste less time on unqualified leads.
2. Sales cycles shorten thanks to faster response times.
3. 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. 

The AI just did it faster.

Warmly’s AI SDR takes this even further.

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:

1. 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.
2. Higher reply rates - Because the messaging feels relevant, not like another mass-blast template.
3. 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:

1. Smart scheduling suggestions - AI recommends the best time to reach out based on both parties’ availability, time zones, and meeting history.
2. 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.
3. Rescheduling automation - When a meeting gets canceled or rescheduled, the AI picks it up and handles the follow-up with no rep needed.
4. 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:

1. Reps improve faster with targeted, actionable feedback instead of vague performance reviews.
2. Managers scale coaching without listening to every call.
3. 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:

1. Proactive deal management instead of reactive firefighting.
2. Higher win rates thanks to early intervention on risky deals.
3. 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?

1. Cleaner data without burning rep hours.
2. Higher pipeline confidence at every level.
3. 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:

1. 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.
2. 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.
3. 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:

1. Engage: Includes email assistant, automations, CRM sync, templates, etc.
2. Call: Includes sales dialer, live sales monitoring, AI-powered call summary, etc.
3. Meet: Includes real-time call recording and transcription, AI-powered meeting assistant, automated summary and action items, etc.
4. Deal: Includes AI-powered deal assistant, deal health score, deal overview with activity history, etc.
5. 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.

If you need more, you can upgrade to one of three paid plans:

1. Basic: $59 per user per month.
2. Professional: $99 per user per month.
3. Organization: $149 per user per month.

4. Clari

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.

Read more

• 8 Examples of AI Marketing Automation in 2025 - Real-world examples of how AI is automating everything from email campaigns to lead nurturing.
• AI Lead Generation: How to Use AI to Get More Leads in 2024 - Learn how to use AI tools and workflows to attract, qualify, and convert more leads in less time.
• 10 Best AI Marketing Tools for B2B Teams [2025] - Discover the top AI tools B2B marketers are using in 2025 to streamline campaigns and scale results.
• 10 Best AI Lead Generation Tools for B2B Teams [2025] - A curated list of the best AI platforms for automating lead capture, scoring, and outreach.
• Agentic AI For Marketing: Best Use Cases & Software In 2025 - Explore how agentic AI is transforming marketing by executing full campaigns with minimal human input.
• 10 Best AI Agents for Digital Marketing in 2025 [Reviewed] - A breakdown of the most effective AI agents built to plan, launch, and optimize digital marketing.
• AI for Sales Prospecting: How to Use It? [2025] - Learn how AI can help you find the right accounts, personalize outreach, and book more meetings.
• AI GTM: Top Use Cases, Software, & Examples [2025] - See how AI is powering smarter go-to-market operations, from ICP targeting to full campaign orchestration.
• AI in B2B Marketing: How To Use It? [2025] - A practical guide to using AI in B2B marketing, from content creation to pipeline acceleration.
• AI Agentic Workflows: Definitions, Use Cases & Software In 2025 - Get a clear overview of what agentic workflows are and how they’re reshaping modern marketing ops.
• AI Marketing Strategy: Top Use Cases and Tools - Build a smarter strategy with this guide to AI marketing use cases, platforms, and best practices.
• AI for Sales: Best Tools & Tips [2025] - A tactical guide to the best AI tools and strategies to drive sales productivity and pipeline growth.
• 10 Best AI Sales Assistants in 2025 [Reviewed] - Compare the top AI sales assistants helping reps manage outreach, follow-ups, and deal tracking.
• AI in Sales and Marketing: How to Get More Sales? - Discover how AI is helping sales and marketing teams align, automate, and close more deals.

AI prospecting tools are changing the game for modern sales teams in 2025.

Instead of spending hours scraping lists, guessing timing, or firing off cold messages into the void, reps now have AI-powered co-pilots that do the heavy lifting, such as:

• Finding the right accounts.
• Surfacing buying signals.
• Starting meaningful conversations. 

The result? More booked meetings, less busywork.

But with so many tools flooding the market, knowing which ones actually deliver can be tough. 

That’s where this guide comes in.

I’ve reviewed the 10 best AI prospecting tools in 2025, singling out the platforms built to help you identify, engage, and convert your highest-value leads faster. 

Let’s dive in and help you find the right one!

Factors to consider when choosing AI prospecting tools & software

Not all AI prospecting tools are built the same. 

Some will help you land high-intent meetings on autopilot, while others will add noise to your stack and slow your team down.

Before you commit, here are the key factors to evaluate to ensure you're picking a tool that actually drives pipeline.

1. Data accuracy and enrichment

Prospecting success starts with clean, enriched data. 

The best tools offer verified emails, up-to-date company info, and real-time insights like job changes or buying intent, so you’re not wasting time on dead ends.

2. Multi-channel outreach capabilities

Top-performing reps don’t stick to one channel - they mix email, LinkedIn, and calendar-based touchpoints to stay top of mind. 

Choose a platform that lets you orchestrate multi-channel sequences without switching tabs or tools.

3. AI-powered personalization

It’s not just about automation, it’s about relevance. 

Look for tools that use AI to craft context-aware messages that reference a lead’s role, company, or recent activity, making your outreach feel thoughtful, not templated.

4. Integration with your sales stack

Your AI prospecting tool should work with your CRM, email, and LinkedIn - not against them. 

Strong integrations keep your data synced, your team aligned, and your workflows running smoothly.

5. Lead scoring and prioritization

The smartest tools help you work smarter, not harder. 

With AI-powered lead scoring, you can focus on the highest-intent accounts first, based on engagement, fit, and timing signals that move the needle.

6. Ease of use and workflow design

Even the most powerful software falls flat if it’s clunky or confusing. 

Make sure your team can easily build, launch, and tweak prospecting flows without needing constant admin support.

7. Pricing that scales with your team

Some platforms look affordable until you add a few reps or unlock key features. 

Choose a pricing model that fits your team size and usage now, while giving you room to grow without surprises.

What are the best AI prospecting tools & software in 2025?

Here are the 10 best AI prospecting tools on the market after taking into consideration 30+ tools:

1. Warmly: AI-driven agents that detect buying signals and initiate outreach on your behalf.
2. Apollo.io: Comprehensive AI sales platform integrating prospecting, enrichment, and multi-channel outreach.
3. Cognism: Accelerates prospecting with AI-powered search, delivering compliant B2B data and intent signals. 
4. Clay: Combines data from over 100 sources with AI to automate personalized outbound campaigns. 
5. Seamless.AI: Real-time AI engine that finds and verifies B2B contact information to build accurate lead lists. 
6. Outreach: AI Prospecting Agent automates lead research and engagement, customizing actions to your sales motion. 
7. Salesloft: Conductor AI guides sellers by prioritizing actions based on opportunity data and intent signals. 
8. Lusha: Provides AI-driven recommendations and accurate B2B data to streamline prospecting efforts.
9. OneShot.ai: Combines AI agents with human oversight to automate outbound prospecting and lead qualification.
10. ZoomInfo: Offers AI-powered insights and comprehensive contact data to enhance sales prospecting strategies.

1. Warmly

Best for: Using advanced AI to orchestrate intent-based prospecting and multi-channel engagement at scale.

Who is it for: B2B sales and marketing teams looking to scale outreach, free up SDR time, and engage high-intent accounts without manual effort.

Warmly is a sophisticated AI GTM engine built to automate your entire sales funnel. 

Instead of just handing you a list, it acts like an always-on SDR, identifying your true ICP, detecting buying signals in real-time, and launching multi-threaded sequences to the right contacts automatically. 

Whether you’re warming up cold accounts or capitalizing on live intent, Warmly makes sure you never miss a revenue opportunity.

Here are some of its key prospecting features:

Feature #1: AI-powered ICP identification 

Warmly uses AI to reverse-engineer your best customers, not just by industry or title, but by identifying deeper behavioral, technographic, and firmographic traits that consistently lead to revenue. 

Its AI Marketing Ops agent continuously runs thorough research to define the true characteristics of your best customers based on all the historical data and intent signals combined. 

Once your true ICP is modelled, Warmly continuously scans the market to find net-new prospects that match those same traits, so every outreach is targeted, not spray-and-pray.

Feature #2: Real-time data & signal monitoring

Warmly’s AI marketing agents constantly monitor live intent data, website activity, job changes, tech stack shifts, and de-anonymized visitor insights from over 10+ enrichment sources. 

As a result, you’ll know at a glance who your hottest leads are right now.

This lets reps prioritize the right accounts at the right moment based on actual buying signals, not hunches.

Moreover, Warmly lets you detect all the key stakeholders within a single account.

The platform integrates with Apollo, ZoomInfo, Demandbase, or uses its proprietary data to automatically uncover all decision-makers and influencers within a target account, so you’re not stuck selling to a single contact.

Feature #3: AI-powered outreach

Imagine a tireless SDR running thousands of sequences on autopilot. 

That’s what Warmly’s AI SDR agents do. 

They identify ideal contacts, personalize messages, trigger outreach based on real-time signals, and continue to follow up, all without human intervention.

As a result, your team can scale outbound 10x without burning out your SDRs, allowing human reps to focus on live conversations and qualified leads instead of being stuck in the weeds of repetitive outreach.

Feature #4: Generative AI chat

Warmly’s website chat doesn’t just say “Hi”.

Instead, it sells. 

The AI chatbot engages visitors in real time with customized messaging based on who they are, what pages they’ve visited, and where they are in the funnel. 

Moreover, it’s fully trainable, meaning you can fine-tune it to fit your brand’s style and tone of voice to perfection.

The chatbot learns on the go, so the more data you feed into it regarding your business and ideal prospects, the better it will handle all interactions.

Feature #5: Coldly contact database

Coldly is Warmly’s AI-powered B2B contact database with over 200M+ accounts and contacts. 

Whether you’re targeting niche verticals or broad enterprise segments, Coldly gives you a head start with validated contact data that’s ready for outreach.

Coldly refreshes its records every 2–4 weeks to keep your outreach laser accurate. 

That means fewer missed connections and no more chasing people who left the company three quarters ago.

Furthermore, with Coldly, you’re not stuck with rigid filters. 

The database lets you define custom search criteria based on your exact ICP, so you’re not just working any leads, you’re working the right leads.

Since it is deeply integrated with the rest of Warmly’s tools, you’ll be able to run complex prospecting workflows on autopilot.

For example, once Coldly identifies the right stakeholders, Warmly’s AI agents will engage each of them with personalized outreach until they’re ready to talk.

As easy as that!

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:

1. 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.
2. 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.
3. Enterprise: Custom pricing, custom number of visitors, everything in Business, plus custom signals and warm calling.

Pros & Cons

✅ Autonomous AI agents that let you prospect and sequence without human input.

✅ Identifies website visitors at both company and person levels.

✅ Real-time buying signal monitoring with deep intent insight.

✅ Powerful ICP modelling beyond basic firmographics.

✅ Extensive B2B database that’s updated daily.

✅ Seamless integrations with top sales data platforms (Apollo, ZoomInfo, 6Sense, etc.)

✅ Reveals your hottest leads and surging accounts in real-time.

✅ Live text and video chat enable your reps to tune into the most relevant conversations straight from Warmly’s dashboard.

❌ Its most advanced agentic AI features are available only on the paid plans.

2. Apollo

Best for: Leveraging AI to automate and personalize outbound sales at scale.

Who is it for: Sales and marketing teams aiming to streamline their prospecting efforts and enhance engagement through AI-driven insights.

Apollo is an AI-powered sales platform that combines a vast B2B database with advanced automation tools. 

It enables teams to identify ideal prospects, personalize outreach, and manage the sales pipeline efficiently.

Key features

• AI-powered prospecting - Apollo's AI surfaces the right personas, companies, and buying signals instantly, allowing teams to focus on strategy rather than manual research. 
• Personalized outreach -  The platform's AI assists in crafting personalized emails, incorporating company news and insights to match your brand voice, enhancing engagement rates. 
• Lead scoring and prioritization - With real-time AI-generated scores, Apollo helps prioritize high-value leads, enabling teams to focus on prospects most likely to convert. 

Pricing

Apollo’s pricing has a free forever plan that includes 100 email and mobile phone finder credits, basic filters and prospecting, and two sequences.

If you need more, you can upgrade to one of three paid plans:

1. Basic: $59 per user per month.
2. Professional: $99 per user per month.
3. Organization: $149 per user per month.

Pros & Cons

✅ Comprehensive B2B database with over 210 million contacts.

✅ Seamless integration with existing sales tools.

❌ Occasional interface lags, particularly when managing large-scale data, which is why some people are looking for Apollo alternatives.

3. Cognism

Best for: Accelerating B2B prospecting with AI-driven prospect search and GDPR-compliant contact data.

Who is it for: Sales and marketing teams seeking high-quality, European-based B2B data to optimize their prospecting process.

Cognism is a sales intelligence platform that leverages AI to streamline the prospecting process. 

It offers a vast database of verified contact information, including mobile numbers and email addresses, enriched with firmographic, technographic, and intent data.

Key features

• AI Search - NLP-driven engine that analyzes vast data sets and creates targeted lists using just natural language commands, allowing users to quickly identify high-value leads.
• Sales Companion - This AI-driven tool helps identify high-potential accounts, prioritize leads, and predict revenue with greater accuracy. 
• Diamond Data® - Cognism offers exclusive on-demand verified cell phone and email data, ensuring high-quality, compliant information crucial for outbound sales. 

Pricing

Cognism has two pricing plans:

1. Grow: Includes Diamond Data, firmographic data, CRM integrations, etc.
2. Elevate: Includes everything in Grow, plus granular intent data and Diamonds on Demand (lets you select high-priority prospects and submit them for advanced verification).

However, the platform doesn’t disclose prices for any plan, so you’ll have to contact sales for a quote.

Pros & Cons

✅ High-quality, GDPR-compliant contact data.

✅ Real-time CRM data enrichment.

❌ Pricing may be on the higher side for smaller businesses, which is why start-ups have been looking for Cognism alternatives.

4. Clay

Best for: Automating hyper-personalized outbound campaigns using AI and real-time data from 100+ sources.

Who is it for: RevOps teams aiming to help sales and marketing streamline their prospecting efforts and enhance engagement through AI-driven insights.

Clay is a data-powered outbound engine built for precision.

Instead of relying on a static database, Clay lets you connect 100+ real-time data sources, enriching every lead with context like job changes, funding rounds, and tech stack, and then uses AI to personalize outreach at scale. 

It’s built for teams who want full control over targeting, personalization, and automation without needing a full-time data ops team.

Key features

• Claygent - Clay’s AI-powered web scraper that automates manual research tasks, such as visiting websites, extracting relevant information, and summarizing findings, helping you find relevant company details and competitor information. 
• Waterfall data enrichment - Clay integrates data from over 100 sources, pulling information from multiple platforms in a specific order you set, ensuring you have the most accurate and relevant data at your fingertips. 
• CRM integrations and data hygiene - Clay connects seamlessly with your CRM, ensuring that your data is always up-to-date, improving prospecting efforts across levels.

Pricing 

Clay has a free forever plan that provides 100 monthly search credits and limited access to its features.

If you need more credits and features, you can choose from 4 pricing tiers:

• Starter: Starting at $149/mo.
• Explorer: Starting at $349/mo.
• Pro: Starting at $800/mo.
• Enterprise: Custom pricing.

Pros & Cons

✅ Comprehensive data integration from over 100 sources.

✅ Highly customizable workflows that can be tailored to various prospecting needs and processes.

❌ Credit-based pricing system doesn’t scale well.

5. Seamless.AI

Best for: Real-time B2B lead generation with AI-powered contact search and enrichment.

Who is it for: Sales and marketing professionals who need access to high-volume, accurate lead data in seconds.

Seamless.AI is a sales prospecting platform that leverages artificial intelligence to provide real-time access to verified B2B contact information. 

It ensures that users have the most current data by continuously uploading its database, helping sales teams connect with decision-makers more effectively.

Key features

• Real-time search engine - This feature ensures that sales professionals can instantly reach the most recent and relevant contact information, enhancing the efficiency of their outreach efforts.
• AI Assistant for sales content creation - Helps you craft personalized sales scripts, emails, and social media messages quickly by analyzing customer data and preferences.
• Total AI confidence scoring - The platform assigns a confidence score to each contact's data, indicating the reliability of information like email addresses, helping you improve outreach success rates.

Pricing

Seamless.AI has a free forever plan that provides you with 50 credits for finding prospect data.

For more credits and extra features, subscribe to one of two plans:

1. Pro: Includes everything in Free, daily credit refresh, access to AI features, etc.
2. Enterprise: Includes everything in Pro, plus API and extra support.

However, Seamless.AI doesn’t disclose prices for its packages, so you’ll have to contact its sales team.

Note: Some of its best features, including all the AI-powered ones, are listed as add-ons. This means that you may need to pay extra for them, regardless of your subscription to any of the platform’s paid plans.

Pros & Cons

✅ Provides real-time, verified contact information.

✅ User-friendly interface with a helpful Chrome extension.

❌ Limited international data coverage compared to competitors.

6. Outreach

Best for: Enterprise-grade sales engagement and execution powered by AI across the entire revenue cycle.

Who is it for: B2B sales teams, revenue operations leaders, and customer success teams seeking a unified platform to streamline prospecting, deal management, and forecasting with AI-driven insights.

Outreach is a leading sales execution platform that integrates AI to enhance every stage of the sales process. 

From automating prospecting tasks to providing real-time deal insights and forecasting, Outreach empowers teams to engage more effectively and close deals faster.

Key features

• AI prospecting agent - This agent identifies potential leads, qualifies them based on predefined criteria, and reaches out to best-fit prospects through personalized messages. 
• Smart email assist - This feature leverages AI to draft initial sales engagement emails, email replies, or meeting follow-ups based on the provided context.
• Smart deal assist - Outreach predicts whether a deal will close with high accuracy rates and recommends actions to keep it on track using unique engagement signals across emails, calls, and meetings. 

Pricing

Outreach has five different product packages:

1. Engage: Includes email assistant, automations, CRM sync, templates, etc.
2. Call: Includes sales dialer, live sales monitoring, AI-powered call summary, etc.
3. Meet: Includes real-time call recording and transcription, AI-powered meeting assistant, automated summary and action items, etc.
4. Deal: Includes AI-powered deal assistant, deal health score, deal overview with activity history, etc.
5. 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.

Moreover, each package has distinct pricing, meaning the costs can easily add up if you want to use several of Outreach’s products.

Pros & Cons

✅ Robust automation capabilities for emails, follow-ups, and meeting scheduling.

✅ Deep insights into deal health and buyer sentiment.

❌ Opaque pricing.

7. Salesloft

Best for: AI-guided sales execution and pipeline acceleration across the entire revenue cycle.

Who is it for: Mid-market to enterprise sales teams that need structured, signal-based workflows to guide reps through multi-touch outreach, improve pipeline hygiene, and consistently hit targets.

Salesloft is a revenue orchestration platform that turns buyer signals into clear next steps. 

With AI-driven prioritization, automated cadences, and real-time conversation insights, it helps reps stay focused, act faster, and close more deals without the guesswork. 

From first touch to final signature, Salesloft keeps the entire sales process aligned and accountable.

Key features

• AI sales agents - These agents handle activities such as account research, buyer identification, and deal summarization, allowing sales teams to focus on high-impact actions. 
• Conductor AI for guided selling - Conductor AI evaluates seller activity, buyer engagement, and opportunity data to guide sellers on the most effective next steps, enhancing productivity and deal progression. 
• Cadence automation - Salesloft allows users to create automated, multi-step sequences for engaging prospects through various channels, ensuring timely and consistent communication. 

Pricing

Salesloft has two pricing plans:

1. Advanced: Provides access to all Salesloft features except forecasting and revenue management.
2. Premier: Everything in Advanced, plus forecasting and revenue management.

However, Salesloft doesn’t publish prices for either plan, meaning you’ll have to contact its team for a quote.

There’s no free trial, but you can book a demo to see it in action.

Pros & Cons

✅ User-friendly interface.

✅ Integrates smoothly with major CRM systems like Salesforce, enabling efficient data sync and workflow management.

❌ The email templates it provides have limited customization options, making it difficult to tailor them to specific prospecting needs.

8. Lusha

Best for: Streamlining B2B prospecting with AI-driven lead recommendations and real-time contact enrichment.

Who is it for: Sales and marketing teams of various sizes seeking accurate contact data, automated outreach capabilities, and AI-powered prospecting features.

Lusha is a B2B sales intelligence platform that empowers teams to connect with decision-makers by providing verified contact information, AI-driven lead recommendations, and integrated outreach tools. 

With features like Lusha Engage and AI Recommendations, it simplifies the prospecting process, allowing users to focus on building relationships and closing deals.

Key features

• AI recommendations - These act as a personal prospecting assistant, analyzing user activity to suggest high-quality leads tailored to your ideal customer profile (ICP), helping you prioritize high-impact prospects from the get-go. 
• Advanced prospecting filters - You can filter prospects by criteria such as industry, company size, job title, and location, in addition to using intent data, job change alerts, and technology filters to identify and target high-potential leads more effectively. 
• Chrome extension - This allows you to access contact information while browsing LinkedIn or company websites. 

Pricing

Lusha has a free forever plan that includes 70 search credits, basic intent signals, AI recommendations, funding and technology filters, etc.

If you need more, you can subscribe to one of three paid plans whose price depends on the number of credits you need:

1. Pro: Starts at $29.90 for 300 credits per month and goes up to $59.90 for 600 credits, includes everything in Free, plus 3 users, intent, technology, job change alerts, etc.
2. Premium: Starts at $59.90 for 800 monthly credits and goes up to $798.55 for 10,000 monthly credits, includes everything in Pro, plus 5 users, CSV enrichment, advanced analytics, etc.
3. Scale: Custom pricing, includes a custom number of credits and users, and higher usage limits.

Note: Finding a phone number takes 10 credits, whereas finding an email address takes only one.

Pros & Cons

✅ High data accuracy and freshness.

✅ Integrated email sequencing within the platform, so you don’t have to switch between tools.

❌ The credit system can be restrictive, especially for teams with high-volume prospecting needs.

9. OneShot.ai

Best for: End-to-end AI-powered outbound execution combining automation with human expertise.

Who is it for: B2B sales teams seeking to scale personalized outreach without increasing headcount.

OneShot.ai is a sales execution platform that blends AI agents with on-demand human experts to automate and manage outbound prospecting tasks. 

OneShot handles the entire sales process, from lead research to personalized messaging and campaign deployment, enabling sales teams to focus on closing deals.

Key features

• AI agents - OneShot creates and deploys custom AI agents for various tasks, including prospect research, message personalization, and performance optimization that work alongside human experts for optimal results. 
• Expert network - The platform includes a broad network of GTM experts from various areas that are vetted and chosen by AI agents based on their expertise and looped into specific workflows to ensure human oversight.
• Robust integrations - OneShot integrates with popular CRM systems like Salesforce, HubSpot, and Outreach, ensuring seamless data synchronization and workflow continuity.

Pricing 

OneShot has three pricing tiers:

1. AI Sidekick: Designed for individuals focused on 1-1 meetings, includes 300/1,000 message generations per month, AI-powered research and hyper-personalized emails, basic AI assistance, and some CRM integrations. 
2. Scaled Research & Messaging: Designed for teams scaling outreach across thousands of prospects, includes 2,000 message generations per month, all AI Sidekick features, plus custom prompting and workflow automation.
3. Fully Autonomous Prospecting 24/7: $1,995 per month, designed for teams of any size who want their outbound prospecting done for them with no manual effort, includes 2,000 fully autonomous leads per month, everything in Scaled Research, plus more advanced features, such as fully autonomous agentic AI automation.

The pricing for the other two plans is not published. 

Pros & Cons

✅ Combines AI automation with human expertise for comprehensive campaign execution.

✅ Supports multichannel outreach, increasing engagement opportunities.

❌ Limited customization.

10. ZoomInfo

Best for: Enterprise-grade B2B prospecting powered by AI-driven insights and one of the largest U.S.-focused contact databases.

Who is it for: Sales, marketing, and revenue operations teams at mid-market to enterprise companies seeking to scale outbound efforts with enriched data, buyer intent signals, and AI automation.

ZoomInfo is a comprehensive GTM intelligence platform that combines a vast B2B contact database with AI-powered tools like Copilot. 

It enables teams to identify high-intent prospects, personalize outreach, and automate workflows, enhancing efficiency and effectiveness in sales and marketing efforts. 

Key features

• Copilot - ZoomInfo’s AI-driven assistant that analyzes CRM data and ZoomInfo’s extensive database to detect opportunities, personalize emails, suggest actions, and provide detailed profiles.
• Advanced filtering - You can filter prospects based on various criteria, such as industry, company size, location, and job role. 
• Intent data - ZoomInfo tracks online behavior to identify companies showing interest in specific products or services, helping you prioritize outreach to prospects who are actively researching solutions similar to yours. 

Pricing

ZoomInfo has three plans for Sales, Talent, and Marketing teams, all of which have custom pricing and lock you into 12-month contracts.

These custom packages differ primarily in the app integrations and features they include, as each is geared toward distinct user types.

Since ZoomInfo doesn’t disclose actual prices, you’ll have to contact ZoomInfo’s team for a custom quote or check our in-depth ZoomInfo pricing guide.

Pros & Cons

✅ Extensive and up-to-date B2B contact database, particularly strong in the U.S. market.

✅ Intuitive interface, despite having numerous features.

❌ Data accuracy may vary for regions outside the U.S, which is why some brands have been looking for ZoomInfo alternatives.

Next steps: Turn intent into pipeline automatically

The tools in this list prove that AI is no longer just a support function in sales - it’s a driver.

From finding ICP-fit accounts to launching entire outbound sequences and surfacing live buying signals, AI prospecting software in 2025 gives teams a serious edge.

But the real differentiator is execution.

You don’t just want data, you want action. 

You don’t just need contacts, you need context, timing, and personalized follow-through at scale.

That’s where Warmly stands out.

With AI agents that detect intent, reach out automatically, and engage every key stakeholder - plus a real-time data engine and full prospecting suite - Warmly helps you go from signal to meeting without lifting a finger.

Ready to see it in action? 

Book a demo and find out how Warmly’s AI can handle your outbound while your team focuses on closing.

Read more

• AI for Sales Prospecting: How to Use It? [2025] - A step-by-step guide to using AI tools and workflows to find and engage high-quality leads more effectively.
• 10 Best AI Lead Generation Tools for B2B Teams [2025] - Explore the top AI platforms helping B2B teams automate lead capture, enrichment, and conversion.
• 10 Best AI Sales Tools for B2B Teams [2025] - A curated list of the best AI-powered tools built to streamline outreach, boost productivity, and close deals faster.
• 10 Best AI Marketing Tools for B2B Teams [2025] - Discover the top AI tools transforming how B2B marketers create content, run campaigns, and generate demand.
• B2B Prospecting: Ultimate Guide for 2024 - Everything you need to know about modern B2B prospecting, from strategy and channels to tools and tactics.
• Best 10 AI Sales Agents In 2025 [Reviewed] - A breakdown of the leading AI sales agents that can automate prospecting, outreach, and deal progression.
• 10 Best AI Agents for Digital Marketing in 2025 [Reviewed] - Meet the top AI agents helping digital marketers scale campaigns, personalize outreach, and drive growth.
• 10 Best AI Agents for Small Businesses in 2025 [Reviewed] - AI-powered tools built specifically to help small businesses market, sell, and scale without hiring big teams.
• 10 Best AI Email Agents in 2025 to Drive Pipeline [Reviewed] - The best AI email agents to help teams automate cold outreach, boost response rates, and book more meetings.

Demand generation software has become essential for modern B2B marketing teams, but not all platforms are created equal. VPs of Demand Generation, CMOs, and Marketing Operations leaders must scrutinize potential platforms through a clear checklist of criteria.

From data quality to AI capabilities, today’s demand gen solutions (eg, 6sense, Demandbase, Mutiny, Warmly, and others) promise AI-driven insights and automation—but savvy buyers will evaluate these claims against concrete requirements.

The following sections present six key checklist items to assess before investing in a demand generation platform, each grounded in methodology and supported by industry data.

Finally, we’ll examine how Warmly stacks up against these criteria in practice.

Sources

The statistics in this article are supported by research from Gartner, Forrester, McKinsey, and other industry analysts, as cited throughout, to provide an objective foundation for each checklist recommendation.

Demand Generation Software Checklist

1. Data Quality & Enrichment Capabilities

• Data freshness ✅
• Data validation ✅
• Privacy-first ✅

2. Intent Signal Accuracy and Coverage

• Waterfall approach ✅
• Lead Filtering ✅
• Speed to signal ✅

3. AI and Automation Capabilities

• AI Lead Scoring ✅
• AI personalization ✅
• Routing & Alerting ✅

4. Omnichannel Engagement and Orchestration

• Channels supported ✅
• Orchestration complexity ✅
• Contact data for Person-Based Marketing ✅

5. CRM and Marketing Stack Integration

• CRM custom fields read/write ✅
• Extended channel integration ability ✅
• Export, webhooks & AIs ✅

6. Agentic Future

• Human-in-the-loop ✅
• AI marketing agents ✅
• AI sales agents ✅

1. Data Quality and Enrichment Capabilities

Bad or incomplete data can derail marketing efforts, leading to wasted outreach and missed opportunities. Gartner estimates that poor data quality costs organizations an average of $12.9 million annually. This is unsurprising given how quickly B2B data grows stale—B2B contact data accuracy can erode by up to 70% annually without ongoing updates.

When evaluating demand generation software, buyers should investigate how a platform handles:

Source and Freshness of Data

Does the platform provide data enrichment via reputable providers?

Data Cleansing and Validation

What mechanisms exist to de-duplicate records, correct errors, and fill in missing fields?

Privacy-First Approach to Data

Beyond basic SOC2 compliance, businesses handling data globally should be registered in the US as data brokers and be GDPR compliant. As of publishing, at least five US states have state-specific privacy regulations, and you should ask for a list of all the platform’s sub-processors to ensure compliance.

Resources

How to Evaluate Data Vendors (by Warmly’s in-house data expert)

The leading demand generation platforms differentiate themselves with robust data ecosystems. For instance, some solutions bundle data from multiple sources—in one case, a vendor might offer “all-in-one” access to enrichment and intent data (eg, combining Clearbit, Bombora, etc.) as part of the package. The goal is to ensure your team is acting on high-quality, current data about accounts and prospects.

2. Intent Signal Accuracy and Coverage

Most advanced demand gen platforms tout their ability to identify buying intent, surfacing signals that a prospect is “in market” or showing interest. However, buyers should critically assess how accurate and comprehensive these intent signals are. An intent-driven program is only as good as the relevance of its alerts.

Key considerations include:

Signal Sources

Does the software capture first-party intent (e.g., website page visits, content downloads), second-party intent (e.g., social media site signals), and integrate third-party intent data (industry web research, review sites, Bombora intent topics, etc.)?

Relying on multiple sources can improve accuracy. In fact, over 70% of companies using intent data leverage multiple providers to broaden their coverage, and almost half ingest intent data from three or more sources. A platform that aggregates or waterfalls several intent feeds (for example, combining data from providers like Demandbase, G2, Bombora) will likely paint a fuller picture of prospect interest.

Precision and Filtering

How does the platform determine which intent signals truly indicate a qualified buyer? Top solutions use AI models or rule-based scoring to filter out false positives. For instance, 6sense and Demandbase (leaders in account-based marketing) apply predictive analytics to intent data, helping prioritize accounts with statistically higher conversion propensity. When testing a tool, ask for evidence of its signal precision—case studies or metrics showing improved outbound response rates or sales productivity can indicate reliable intent modeling.

Timeliness and Context

Real-time or near-real-time intent is valuable for acting at the right moment. Check if the platform updates intent insights continuously and provides context (e.g., which topics a target account is researching). Without context, sales teams might struggle to tailor their outreach even if they know an account shows “surging” intent. Note that real-time signals mean within a minute or two, whereas some platforms only update their signals daily, weekly, or monthly.

Resource

Understanding 1st, 2nd & 3rd Party Intent Data (by Warmly’s in-house data expert)

Signal accuracy has a direct impact on pipeline generation. In a Forrester survey, over 85% of companies using intent data reported achieving business benefits such as higher outbound email response rates and more successful sales prospecting.

However, the same research also noted gaps between expectations and outcomes when intent data is not used correctly, indicating that simply having data isn’t enough; it must be accurate and actionable.

Buyers should therefore validate the platform’s intent signals in a pilot or proof-of-concept and favor vendors with transparent methodologies. Said another way: Blackbox intent models used by the same industry leaders (6sense & Demandbase) are outdated in the ABM 2.0 landscape.

3. AI and Automation Capabilities

Nearly every demand generation tool today claims some form of artificial intelligence or automation. The challenge for buyers is to cut through the hype and determine how advanced and useful a platform’s AI capabilities truly are. At a baseline, demand generation software should automate repetitive tasks, but best-in-class platforms use AI to drive smarter decision-making and personalization at scale. Consider the following when evaluating AI and automation:

AI Lead Scoring

Does the AI analyze data to predict which leads or accounts are most likely to convert? Ingesting your CRM data and data about your ICP should allow a good AI Lead Scoring model to predict propensity to buy, allowing marketing and sales to focus on the highest-value targets. 

AI Personalization

Some solutions incorporate AI to dynamically personalize outreach—for example, Mutiny’s platform uses AI to tailor website headlines and calls-to-action for each visitor, and other tools use AI to generate email copy or sales scripts. Evaluate whether the AI can actually create or adapt content in a meaningful way (e.g., using large language models for personalization) or if it’s limited to simple rule-based personalization. High-performing marketing organizations are already leveraging generative AI for content; 84% of “high-performing” marketing teams use generative AI in creative work, according to Gartner’s latest CMO survey.

Routing & Alerting

The platform should automate multi-step workflows. This could mean automatically adding a lead to an email nurture, alerting a sales rep on Slack when an account hits an intent threshold, or launching a targeted ad campaign—all without manual intervention. Look into how routing rules work. Can you send leads to reps based on territories (geographic, size-based, industry)? You never know when your RevOps team will redo how territories are done.

Lastly, if the platform aims to help you with inbound lead conversion, ensure that round-robin’ing is built into the platform for the even distribution of leads. Default is a good example of a demand generation tool that does this well.

Resource

How to Use AI in Your Sales and Marketing Tech Stack (by Warmly’s in-house data expert)

Artificial intelligence is now used in virtually every aspect of marketing operations, so look for platforms that weave AI into the core product (not as an afterthought add-on). While evaluating AI features, request demos of specific use cases, e.g., an AI-driven campaign launch or a discussion of how the system adapts over time.

Additionally, ask about the results current customers see—are they actually reducing manual work or increasing conversion rates thanks to AI?

Remember that AI is not a goal in itself; it’s a means to increase efficiency and effectiveness. Adoption is growing fast (only about 10% of marketers now say they have no plans to use AI in their work), so your chosen platform should be keeping pace with this trend. The right solution will augment your team, acting as a “force multiplier” that handles analysis and repetitive tasks so your people can focus on strategy and creativity.

4. Omnichhannel Engagement and Orchestration

Today’s B2B buyers traverse many channels—from search and social media to webinars, review sites, emails, and beyond. To generate demand effectively, your software must enable an omnichannel engagement strategy. This means reaching and nurturing prospects across the channels they frequent, with a consistent message, and coordinating those touches for maximum impact. Key questions to evaluate:

Channels Supported

Identify which channels the platform can manage or integrate with. Common ones include email marketing, digital ads (display, LinkedIn, Google), website personalization, chatbots or live chat on your site, SMS/text, and even direct mail or outbound calling via integrations. A robust demand gen platform will allow multi-channel campaign builds—for example, serving a LinkedIn ad to a target account, then emailing them a whitepaper, and later triggering a sales call task, all as part of one workflow. If a solution only handles one channel (say, just email automation), it may limit your ability to engage buyers who prefer other touchpoints.

Orchestration Logic

Beyond supporting channels, how well does the tool coordinate them? Look for features like journey orchestration or sequencing that adjust the next touch based on buyer behavior. For instance, if a prospect interacts on LinkedIn but not via email, the system might shift budget toward LinkedIn ads for that person. Account-based marketing platforms such as Terminus, Salesloft & Outreach shine here by enabling coordinated, multi-channel sales & marketing campaigns from one interface.

Contact Data for Person-Based Marketing

Most intent data signals (especially from legacy ABM platforms) only generate account-level leads. This is helpful, but you need to reach out to an actual person to build pipeline. Going to the person-level is relatively new but exciting in Demand Gen. Providers like Warmly, RB2B, and Vector are the first in the space to offer person-based marketing. If your signals offer account-level leads, then you’ll need to make sure they provide high-quality contact data to

Clear trends back the importance of omnichannel engagement: 94% of B2B decision makers now view omnichannel as equally or more effective than traditional single-channel sales models. Buyers flip between self-service digital research and human interactions, so your demand generation platform should empower you to be everywhere your buyer is.

For example, some tools integrate a chatbot on your website that ties into email nurture streams, while others can trigger LinkedIn InMail via automation. The recommended checklist includes verifying integrations or native capabilities for social media (especially LinkedIn for B2B), content syndication, ads, email, events/webinars, and direct sales touches.

5. CRM and Marketing Stack Integration

No demand generation platform exists in isolation. It must fit into your existing sales and marketing tech stack, with CRM integration being especially critical. For most B2B organizations, the CRM (e.g., Salesforce or HubSpot CRM) is the central source of truth for leads, contacts, accounts, and pipeline. If your new software doesn’t seamlessly integrate with CRM and other key systems, you risk data silos and process breakdowns.

As you evaluate options, make sure to check:

CRM integration depth & custom fields

Does the platform offer a native, real-time integration with your CRM? Essential capabilities include syncing leads/contacts and their activities, updating account or opportunity fields (such as intent scores or campaign responses), and creating tasks or alerts for sales reps. Ask for any history of downtime or overwriting data—these would have catastrophic effects on your GTM system. Good demand generation software errs on the side of writing new fields to CRM but not overwriting any data.

Lastly, you will want to use custom fields to be able to sync in additional signals or lead scoring logic. For example, a common need is to route based on BDR owner vs Account owner (AE). Account owner is the default field, whereas your sales operations team might have an additional custom field called BDR owner. You’ll need to be able to utilize this throughout a good demand generation platform.

Extended channel integration ability

Beyond CRM, consider other systems like your marketing automation platforms (Marketo, HubSpot Marketing, Pardot, Drift, Qualified, etc.), email systems, webinar software, and sales engagement tools (Outreach, Salesloft). Good demand generation software could either replace some of these or connect to them. For example, Warmly has built a native AI chatbot to replace legacy chatbots like Drift. However, it does not have a native email sequencer and is instead integrated with Outreach & Salesloft.

Export, webhooks & APIs

Even if native integrations are limited, does the platform allow flexible data export, webhooks, or open APIs? This will determine if your engineering team can connect it to your data warehouse or custom workflows, or middleware providers like Clay.

Look for customer references or demos illustrating data flowing from the platform into Salesforce or HubSpot in real time, and read help-center documentation. Finally, consider the vendor’s partnership ecosystem: a strong ecosystem is a sign that integration has been proven out by many other customers. In short, verify that the “plumbing” works—it may not be the flashiest checklist item, but it’s arguably one of the most important to get right for long-term success.

6. Agentic Future

The next evolution of demand generation is driven by AI-powered agents—tools that automate, personalize, and optimize marketing and sales activities without human bottlenecks. This shift enables leaner, smarter teams to produce greater results.

Forrester found that companies using AI-powered sales and marketing automation tools saw 1.5x faster pipeline velocity.

And at the same time that agentic future (as of 2025) isn’t quite “here” yet, there is still great value in demand generation marketers utilizing human SDRs to do work.

What are AI Marketing & Sales Agents?

AI Marketing Agents and AI Sales Agents work together to:

• Continuously monitor buyer intent signals
• Dynamically generate person-level campaigns
• Automate multi-channel outreach (email, LinkedIn, chat, calls, ads)
• Trigger human involvement only at key inflection points

AI GTM agents can and will provide huge boosts to marketers:

• Creating dynamic TAM with AI Marketing Ops Agents (traditional firmographics miss up to 50% of potential market due to outdated filters)
• Reducing conversion leakage from inbound forms with AI-powered chatbots and timely pop-ups with AI Demand Gen Agents.
•  Running omnichannel outbound campaigns that A/B test & update on learnings with AI Demand Gen Agents.
•  Saving money on agencies & SDRs, but instead using much more cost-effective AI SDR agents.
•  Accelerate the future where marketers will own all of pipeline generation (managing SDRs and owning full-funnel acquisition)

So when evaluating a Demand Generation Platform, look into:

Human-in-the-loop

How does the system alert humans to leads and notify humans of errors? Where can humans put in input, and how can you segment humans to work on the hottest and most enterprise-y leads?

Does your human team enjoy using the platform or believe it will save them time/effort? Are there AI co-pilot features that assist your human team?

AI marketing agents

Which parts of your marketing GTM system can you add marketing agents to? Is there agentic data collection? Lead scoring? Orchestration? If not, will this be on their roadmap in the coming year or two?

AI sales agents

Which parts of your sales GTM system can you add sales agents to? Is there an agentic inbound follow-up? Content personalization? Account prospecting? Outbound emailing or LinkedIn DM’ing? If not, will this be on their roadmap in the next year or two?

How Does Warmly Stack Up to This?

Having outlined the core checklist for evaluating demand generation software, let’s assess Warmly against these criteria. Warmly position ourselves as an agentic demand generation platform, and we compete broadly in the account-based marketing and sales space.

Here’s how Warmly measures up on each checklist item:

In summary, Warmly aligns very well with the buyer’s checklist we outlined. We offer high data quality by aggregating top data sources, accurate intent signals through multi-source monitoring, sophisticated AI automation that acts on insights instantly, authentic omnichannel engagement (spanning email, ads, chat, and social), and seamless CRM integration to close the loop.

Our unique differentiator is combining all these strengths in an autonomous, AI-driven system tailored for smaller marketing teams. While many platforms might meet one or two of the checklist items, Warmly aims to check all the boxes in one solution.

If you’re a VP of Demand Generation or CMO evaluating your options, Warmly is worth comparing against well-known players like 6sense or Demandbase—especially if you seek a vendor-neutral, all-in-one platform that can rapidly turn intent signals into revenue without a large operations staff.

The checklist above can serve as your guide in that evaluation, ensuring whichever software you choose will empower your marketing efforts in a data-driven, efficient, and scalable way.

In the early 2010s, Account-Based Marketing (ABM) took over almost every B2B go-to-market strategy. ABM flipped the funnel to focus on specific companies, aligning sales and marketing around target accounts, often yielding better sales-marketing collaboration and efficient use of resources.

The term “ABM” was coined in 2004, but the approach didn’t gain momentum until the 2010s when new digital tools allowed for more precise account targeting. By treating each target company as a “market of one,” ABM promised personalized marketing at the account level and was seen as the antidote to generic mass marketing.

However, ABM has a fundamental flaw: it targets accounts, not people. To drive revenue in 2025, marketers need to go beyond account-centric thinking and instead utilize Person-Based Marketing (PBM): a bold new approach that puts individual buyers at the center of your strategy.

Let’s explore person-based marketing, why ABM is losing relevance for B2B sales and marketers, and why PBM is the next-generation strategy that fixes ABM’s blind spots.

ABM’s Rise and Its Biggest Blind Spot

ABM emerged as a popular strategy as it recognized that not all leads are equal and that you should give VIP treatment to the accounts that matter most. It made sense as a solution to long B2B sales cycles and complex deals. Marketing and sales teams rallied around a curated list of target companies, coordinating their efforts to engage those accounts with tailored content and outreach. This alignment did yield benefits as companies saw higher ROI by focusing on high-value accounts, and sales felt marketing was finally delivering quality over quantity.

But, again, there was a fundamental flaw: the account is not signing the contract; the person is. Each account is made up of individual stakeholders with different roles, interests, and influence on the purchase. By focusing solely on the account and not the person, ABM can easily misfire by aiming messaging at the wrong person or overlooking the true buying signals coming from specific individuals.

Consider the following scenarios:

Scenario A: Your ABM platform flags Acme Corp as “engaged” because someone from Acme downloaded a whitepaper. Sales gets excited and reaches out to their contacts at Acme. But the person who downloaded that whitepaper is an intern with no buying power. ABM did not and does not distinguish this nuance; it simply saw account activity and labeled the entire account as interested.

Scenario B: A senior decision-maker at a company not on your target list visits your pricing page and interacts with your chatbot. This person is showing intent to buy. ABM did not and does not notice because that company was not on the predefined list.

ABM’s account-centric strategy was built for an era when simply figuring out which company to pursue was the main goal. But now that we have the data and technology to get far more granular, sticking rigidly to account targeting is a liability in 2025. Marketers are finding that the traditional ABM approach is disconnected from how B2B buyers actually behave today. Let’s look at what’s changed.

Multiple Stakeholders per Deal

Buying decisions now involve an average of 6 to 10 decision-makers in a complex B2B sale. Those individuals each consume content and evaluate vendors on their own. ABM, which treats the account monolithically, might engage a couple of obvious contacts but falsely assume the account is covered. In truth, you need to influence many people within that company, and you can’t do that with a one-size-fits-all account blast. If your ABM strategy only reaches a few contacts or one department, you’re missing others who also hold sway.

Digital, Self-Directed Buyer Journeys

Today’s B2B buyers do extensive independent research. 67% of the buyer’s journey is now done digitally before a prospect ever speaks to a salesperson. Buyers read blogs, watch webinars, and compare reviews, leaving behind a trail of intent signals as individuals. ABM platforms that rely on firmographic fit and account-level web visits struggle to capture these person-level signals. An account might appear “cold” simply because the key individuals are quietly researching on third-party sites or under personal email addresses. By the time the account shows up on the ABM radar, it’s likely too late, as a competitor using a PBM platform has already engaged with the person and won the deal.

Expectation of Personalization

Modern buyers demand to be treated as people, not accounts. 80% of business buyers are more likely to purchase from a company that provides personalized experiences. Generic marketing to “ACME Corp” won’t cut it if it doesn’t speak to the pain points of the specific person you’re contacting. True personalization happens when it speaks to the person, not the account, like addressing Bob’s role as a CTO or Sarah’s unique interest in improving cybersecurity. ABM is not granular enough to deliver that at scale.

Real-Time Signals vs. Static Lists

Traditional ABM operates off static target account lists and quarterly planning. But people’s roles and purchase intent are fluid: individuals change jobs, new stakeholders emerge, and priorities shift. By one estimate, B2B contact data decays at a rate of about 30% per year, which means if you built a target list last year (or even last quarter), a third of those contacts are likely outdated. Legacy ABM platforms often don’t refresh contact data fast enough to keep up with the rapidly changing market, leading to wasted effort marketing to people who have left, and missed opportunities with new people who have entered the scene.

All these things point to a fundamental truth: you don’t sell to companies; you sell to people. It’s people who read our content, people who show up to our webinars, and people who ultimately make purchase decisions. ABM has struggled to adapt to this people-centric reality.

It’s like trying to win a modern, digital-era battle with a strategy designed for an older war. The intent was right, but the target is off. To engage today’s buyers, you need to target people and their intent first and foremost. This is where Person-Based Marketing comes in.

From Accounts to Individuals: What is Person-Based Marketing?

Person-Based Marketing (PBM) is the evolution of ABM as it corrects ABM’s core weakness: its inability to distinguish person from account.

PBM is exactly what it sounds like: marketing based on the person, not marketing based on the account. PBM markets and sells to the specific people at your target accounts who show interest, rather than the account as a whole.

As the co-founder of one PBM platform put it, “Traditional marketing was talking to target audiences, ABM is talking to accounts, while PBM talks to the person.” In practice, that means every campaign, every message, every touchpoint starts with an actual human buyer in mind: their name, role, pain points, and engagement history. Person-Based Marketing acknowledges that within any given account, you might have a dozen different individuals interfacing with your brand, and each one needs a tailored approach.

Key principles that distinguish PBM from ABM

Individualized Targeting

Where ABM starts with a list of companies, PBM starts with a list of people. Yes, those people work at companies you care about (your ICP accounts aren’t irrelevant), but PBM drills one level deeper. For example, instead of “ACME Corp” as a target, PBM identifies which stakeholders at ACME should be targeted, e.g., Jane Doe, Director of Finance, who has been researching cost-management solutions. PBM doesn’t waste effort on ACME employees with zero influence on the deal; it concentrates on the real decision makers and influencers.

Person-Level Intent Data

PBM systems ingest and analyze signals at the contact level. This can include first-party data like a visitor’s email address captured on a web form, third-party intent data showing content consumption by specific people (via cookies tied to business emails, for instance), engagement on social media, webinar attendance, etc. PBM builds an intent profile for each individual by stitching these signals together. This way, when John Smith from XYZ Inc. compares solutions on review sites or likes multiple LinkedIn posts about a relevant topic, PBM platforms alert you, even if John never filled out a form on your site. The focus is on who is showing intent, not just which account.

Hyper-Personalized Messaging

Because PBM knows exactly which person it’s addressing, campaigns can be deeply personalized. This goes beyond inserting a first name in an email. It means tailoring content and outreach to that individual’s interests and stage in the journey. For example, if PBM identifies that Jane Doe from Finance just read a blog on “ROI of cloud so ware,” the follow-up can be an email from a sales rep highlighting a case study relevant to CFOs, or even an AI-driven ad that addresses financial ROI of your product—served directly to Jane across her devices.

This level of personalization at scale simply wasn’t feasible in older approaches. And it pays off: personalized outreach dramatically boosts engagement and conversion, which is no surprise given that relevance is what B2B buyers respond to. (Recall that 80% of buyers are more likely to buy from a company that personalizes the experience.)

Multi-Channel, One-to-One Engagement

ABM often relies heavily on broad tactics like display ads and email nurtures to all contacts at an account. PBM, on the other hand, enables true one-to-one engagement across channels. That could mean displaying ads only to a set list of individuals (a capability some ad platforms and vendors like Influ2 pioneered), sending tailored LinkedIn messages or connecting via social when a person engages with certain content, and even orchestrating direct mail or event invitations to specific people. The outreach is orchestrated to feel personal because it is personal, having been triggered by that person’s actions.

Essentially, PBM treats each important contact as its own “market of one,” which was the original spirit of ABM, now executed at the contact level.

Dynamic and Real-Time

Person-based marketing continuously updates who is a priority based on real-time behavior. Instead of static account tiers that sales and marketing agreed on months ago, PBM will constantly surface new hot leads because someone new at Company X started surging on intent signals just yesterday. It’s adaptive. It also means PBM can react the moment an individual takes an action.

For instance, if a target person visits your pricing page, a PBM system could automatically alert their account rep and even trigger an AI-created email to that person within minutes, addressing what they might be looking for. Speed matters—research from 6sense shows 91% of buyers come to a sales conversation already familiar with your brand. If you wait weeks to respond to signals at an account because you’re on a fixed ABM schedule, you’re missing the window when interest is highest.

In many ways, PBM can be seen as ABM 2.0 as it takes the best parts of account-based strategy (focus and alignment on high-value targets) and updates the tactics to be people-centric and data-driven. ABM said,

“Focus on the best-fit accounts.” PBM says, “Yes, and now within those, focus on the actual people driving the deal and give them an experience that feels tailored just for them.” It’s a crucial shift in mindset from account-first to person-first.

Legacy ABM Tools vs. the PBM Approach

Traditional ABM platforms like 6sense and Demandbase emerged by focusing heavily on accounts, reflecting a previous era in B2B marketing. While helpful, marketers frequently run into limitations with these tools. It's one thing to know that a particular account, say ABC Corp, is interested; it's another to identify precisely who within ABC Corp is showing intent. Likewise, important buyers from companies not previously targeted can remain invisible using the traditional ABM approach.

Even established ABM vendors are beginning to pivot. Demandbase recently partnered with Warmly, a pioneer in Person-Based Marketing (PBM), to enhance its platform with detailed, individual-level insights. This collaboration enables marketers to identify specific website visitors and their buying intent, effectively overcoming the anonymity barrier. Industry insiders noted this partnership has significantly improved Demandbase’s ability to surface high-quality leads, even surpassing competitors like 6sense.

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However, despite these enhancements, platforms like 6sense and Demandbase still primarily revolve around account-level analytics. They’re excellent at identifying which companies fit the ideal customer profile or are actively shopping, but this remains a top-down process, starting with accounts and then drilling down to individuals. PBM reverses this model: it identifies engaged individuals first, then connects them back to their respective accounts.

To illustrate this difference clearly, traditional ABM tools provide a heatmap highlighting account-level intent, indicating something like “Acme Corp shows strong interest this week.” PBM tools, in contrast, provide a heatmap highlighting individual intent, pinpointing exactly who, like Jane Doe from Acme Corp, is actively researching solutions right now. The actionable value of person-level insights is clear.

Account-based models also struggle to keep pace with the rapid shifts in buyer activity. ABM platforms often rely on static account lists updated quarterly or annually, potentially missing key buying signals from accounts not previously identified as targets. PBM tools, however, continuously monitor and respond dynamically to signals from individuals, enabling marketers to identify and engage newly interested accounts immediately.

Moreover, traditional ABM tactics, such as broad account-based advertising, often waste budget by indiscriminately targeting large groups within an organization. PBM’s person-centric approach emphasizes precise targeting. Instead of delivering ads broadly to all company employees, PBM ensures tailored messaging reaches only the specific, relevant individuals. This approach boosts effectiveness, reduces wasteful spending, and minimizes the irritation caused by irrelevant advertising.

In summary, legacy ABM tools revolutionized B2B marketing when they emerged, but are now increasingly mismatched with modern buying behaviors. The industry faces a critical choice: persist with a rigid account-centric model or embrace the precise, dynamic, and person-centric approach that data favors.

Warmly and the Rise of Person-Based Marketing

Person-Based Marketing (PBM) isn't just theoretical; innovative platforms like Warmly are already implementing it. Warmly has positioned itself as a leader in PBM, leveraging modern technology and AI automation to address the limitations traditional ABM platforms face.

Here's how Warmly operationalizes person-based marketing.

AI Marketing Agents for Personal Outreach

Warmly uses AI-powered virtual sales assistants (AI SDRs) to engage directly with leads. These agents detect individual intent signals and automatically send personalized messages across channels like email, LinkedIn, or chat. For example, if a prospect views specific content on your website, Warmly’s AI can promptly reach out with a relevant message, ensuring timely and personalized interactions at scale. The AI undergoes strict quality checks, ensuring communications remain thoughtful, compliant, and on-brand.

“Waterfall” Data Validation & Enrichment

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Warmly's Person360™ Waterfall Enrichment integrates data from numerous reputable sources (like Clearbit, Bombora, and Demandbase) to identify and enrich lead information. If one data source lacks details, Warmly seamlessly moves to the next source, significantly improving match rates.

Unlike traditional ABM—which might only identify that "someone from Acme Corp visited"—Warmly pinpoints the specific individual (e.g., "Jane Doe, CFO of Acme Corp visited your pricing page at 10:30 am"). This process ensures accurate, current, and actionable contact data, reducing wasted efforts on outdated leads.

Real-Time Person-Level Signals Tracking

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Warmly tracks and de-anonymizes individual-level intent signals from website interactions and content engagement in real time. Rather than broadly increasing an account’s intent score over time, Warmly immediately identifies specific individuals demonstrating active interest, allowing sales teams to focus precisely on hot leads as opportunities arise. The platform essentially provides a continuously updated to-do list based on live buyer behavior.

CRM Integration and Workflow Automation

Warmly seamlessly integrates with CRMs and marketing automation tools such as Salesforce and HubSpot. It automatically pushes enriched data and intent signals directly into existing workflows, ensuring sales and marketing teams operate from the same, real-time data set. Warmly can trigger immediate follow-up actions, assign tasks, or initiate targeted outreach automatically, closing gaps between marketing signals and sales follow-up.

Privacy-Compliant Data Architecture

Since PBM involves personal data, Warmly has built compliance into its foundation. Warmly prioritizes compliance, ensuring data collection and enrichment adhere strictly to GDPR and CCPA regulations. It focuses exclusively on professional contact information, respects opt-out requests, and continuously updates contact records. This compliance-first approach allows marketers to confidently pursue personalized strategies ethically, respecting user privacy while providing relevant outreach.

Warmly’s rapid adoption highlights the growing industry demand for precise, person-level insights in B2B marketing. Its partnership with established ABM leaders like Demandbase further validates PBM's strategic importance, demonstrating an industry-wide shift towards personalized marketing tactics. Early adopters report improved lead capture, greater engagement effectiveness, and accelerated sales cycles, underscoring the transformative potential of Warmly’s person-based approach.

Conclusion: It’s Time to Put People First

ABM was a valuable step forward—it taught B2B marketers to prioritize quality over quantity and align closely with sales teams. But buyer behavior has evolved. It’s now more distributed, digital, and distinctly personal. Continuing to market solely at the account level is like reading only headlines while missing the critical details beneath. The future belongs to Person-Based Marketing.

Marketers who cling to outdated mindsets (“we target accounts, let sales worry about contacts”) will increasingly lose ground to competitors who engage buyers individually. The data clearly shows that buyers want and respond to personalization, they involve many colleagues in decisions, and they leave plenty of breadcrumbs when they’re interested. PBM is about picking up those breadcrumbs and acting on them person-by-person, rather than waiting for an account to hit some arbitrary score.

For marketing leaders—CMOs, VPs, Heads of Demand Gen—the message is clear: don’t just challenge the status quo, upend it. The tools and strategies available today make it possible to do what was once a marketer’s dream: talk to the right person at the right company at exactly the right time with a message that resonates. That’s the promise of person-based marketing. It’s not theory; it’s here now, and it’s working.

If ABM was about treating a big fish differently than a minnow, PBM is about recognizing which specific fish in the school is nibbling your line. It’s a more precise, surgical approach to B2B growth. It’s time to move beyond static account targeting and embrace this person-focused future. After all, marketers don’t sell to companies; they sell to people. When you market to people instead of logos, the companies (and revenue) will inevitably follow.

Looking to experience Warmly for yourself? Book a demo today.

RevOps and MarketingOps leaders face a critical question as AI promises to revolutionize go-to-market operations: How extensively should we deploy AI in our sales and marketing tech stack?

It’s tempting to jump on the AI bandwagon—especially as 88% of marketers believe AI and automation are essential for meeting customer expectations—yet reality shows organizations only use ~56% of the tools they buy.

The goal isn’t to be anti-AI, but to apply AI in a pragmatic, fit-for-purpose way backed by solid infrastructure and strategy. This article provides a data-driven framework to evaluate where AI (from AI SDRs and lead scoring to chatbots and content generation) makes sense in your stack, and where a human touch or process improvement might yield better returns.

We’ll explore a decision matrix for when full AI-driven outbound is appropriate vs. not, key questions to ask before deploying AI outreach, the risks and constraints to plan for (hallucinations, deliverability, brand control, etc.), and recommendations for AI tools across various categories. The guidance is analytical and realistic, targeted for RevOps, MarketingOps, and GTM architects who must ensure AI investments drive ROI and align with their team’s capabilities.

Table of Contents

1. The Role of AI in Today’s GTM Stack
2. Decision Framework: AI Marketing Agents
3. Decision Framework: AI Sales Agents
4. Key Questions to Answer Before Deploying AI in GTM
5. Risks and Constraints of Using AI
6. Tool Recommendations Across Key Categories
7. Conclusion: AI in Your GTM is a Goldilocks Approach

1.‎ The Role of AI in Today’s GTM Stack

AI is increasingly touching every part of sales and marketing. According to HubSpot’s State of AI report, AI adoption among sales teams has surged to 43% in 2024, up 9% from 2023. Sales professionals cite AI’s power to dramatically scale their efforts—half of the reps surveyed agreed, “AI enables scalability in ways that would otherwise be impossible.”

Where can AI enhance GTM?

Common high-impact use cases include:

AI SDRs & Outbound Prospecting: Autonomous agents or workflows that research prospects and send outreach (emails, LinkedIn messages) to book meetings.

AI-Powered Lead Scoring: Machine learning models that prioritize leads/accounts based on likelihood to convert, using intent signals or behavioral data.

Conversational AI & Chatbots: AI chatbots on your website or messaging channels that engage visitors, answer questions, and pre-qualify leads in real time.

Content Generation & Personalization: Generative AI tools that draft sales emails, social posts, or even landing page copy tailored to different segments, and AI-driven personalization of web experiences.

These AI capabilities can streamline workflows and improve personalization at scale. For example, the number one way B2B sales teams use AI today is for writing sales content and prospect outreach messages. Importantly, AI isn’t a complete replacement for humans—the vast majority of sales pros still edit AI-generated text before using it to ensure accuracy and brand alignment.

The upshot: AI can offload repetitive, data-heavy tasks (research, list building, initial drafting, data analysis) so your team focuses on strategy and relationships.

However, as we examine next, the extent to which you rely on AI vs. humans should be dictated by your business specifics—especially your TAM (Total Addressable Market) and ACV (Average/Annual Contract Value).

2.‎ Decision Framework: AI Marketing Agents

AI Marketing Agents can be used across your marketing motion and are more mature than AI Sales Agents. Not every organization will benefit equally from a “full AI inbound or full AI marketing” strategy. It depends on where you want to scale and how your marketing organization is set up.

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Using Top of Funnel AI Marketing Agents: AI Marketing Ops Agents for Cold Leads

AI Marketing Ops Agents focus on warming up the cold part of your TAM and are primarily used to perform TAM Analysis and Lead Scoring. Agentic TAM Analysis should help you understand the full set of accounts and contacts you could feasibly sell to by analyzing your market, competition, and adjacent verticals. Agentic Lead Scoring should help you score all these leads using signals demonstrating who is in market to buy now and creating a temporal view of how often each signal is exhibited.

By using AI against your Closed/Won customer data, agentic lead scoring can predict who is likeliest to close by matching it against what your customers did leading up to their closing in the past.

Using Middle of Funnel AI Marketing Agents: AI GTM Engineering Agents for Warm Leads

AI GTM Engineering Agents work on warming up the warm part of your TAM and primarily send targeted micro campaigns at scale using AI ads, emails, and LinkedIn messages. Since AI cold calling is mostly illegal, we don’t recommend this.

These agents should work on A/B testing your warm market and quickly verticalize and horizontalize your personas and verticals. The goal should be driving leads to your website for later conversion and pushing these leads to exhibit more and more signals.

Using Bottom of Funnel AI Marketing Agents: the AI Demand Gen & MDR Agents for Hot Leads

AI Demand Gen & MDR agents focus on inbound conversion to a booked meeting and tend to live on your website. These agents can generate landing pages and pop-ups, and utilize AI chatbots to interact live with your website visitors and A/B conversion flows.

A note on constructing your marketing team for AI Marketing Agent Readiness

Most marketing teams in the 2020s were structured vertically by channel: someone for paid, someone for SEO, someone for social, someone for events, etc. Across our customer base, we’re seeing a shift and a realization that this organizational structure is broken for an agentic future. This structure leads to disjointed handoffs, misaligned KPIs, and a lack of ownership over revenue.

Here’s what happens:

• The ads team wants to get a great ROAS and doesn’t want to share credit with the SDR org.
• The content team is focused on engagement metrics, not pipeline acceleration.
• The events team is optimizing for booth scans, not revenue influence.

No one admits to multi-channel attribution. The result: marketing celebrates success in their vertical while sales struggles to hit quota.

If you want to arm your marketing team with AI agents whose goal is to warm up your TAM and grow your pipeline, consider a horizontal integration of your marketing team:

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1. TOFU (Top of Funnel) Marketer:

Title: AI Awareness Marketer

Team: Owns awareness and audience growth on Cold Leads with AI Marketing Agents. Content, brand, paid, SEO, social → all focused on capturing attention and demand.

Goal: Maximize problem & market awareness

2/ MOFU (Middle of Funnel) Marketer:

Title: GTM Engineer

Team: Owns signal-stacking plays on warm leads with AI Marketing Agents. Marketing-led outbound with Automated Email, LinkedIn, targeted micro campaigns

Goal: Turn interest into website visits, omnichannel

3/ BOFU (Bottom of Funnel) Marketer:

Title: Demand Marketer

Team: Owns conversion & pipeline acceleration on Hot Leads with AI Marketing Agents. Directs SDR teams on where to focus and holds live chat conversations.

Goal: Book meetings that are qualified pipeline

Proof: Gong saw a 3X increase in pipeline velocity by aligning content, sales enablement, and demand gen under one BOFU team.

3.‎ Decision Framework: AI Sales Agents

AI Sales Agents can be used across your sales motion. While they can be deployed post-qualified opportunity (e.g., AI notetaker, researcher, and deal room), we will focus here on the pre-sales motion use of AI.

Not every organization will benefit equally from a “full AI outbound” strategy. It depends on your GTM motion.

‎In general, consider the following scenarios:

Small TAM + Low ACV (Bottom-Left)

If your reachable market is small (e.g., < ~5,000 accounts) and your deal sizes are low (< $10K ACV), fully automating outbound with AI is likely not worthwhile. You have a limited pool of prospects, and each isn’t very valuable—a spray-and-pray AI SDR will quickly spam your TAM for little return (Brendan Short posted on the topic). In this scenario, rather than an AI blitz, focus on highly targeted, human-led outreach or other channels (inbound marketing, referrals, etc)

AI use: maybe minimal (research assistance), if any.

Small TAM + High ACV (Top-Left)

With a narrow market but large deal sizes (e.g., enterprise accounts), outbound can work, but it should be highly orchestrated and personalized. A human-first approach is best, since each prospect is high-value. AI can assist reps with research, drafting customized messages, or augmenting an Account-Based Marketing (ABM) program–but not fully take over interactions. In practice, many companies avoid AI SDRs for true enterprise segments.

AI use: non-prospect facing.

Large TAM + Low ACV (Bottom-Right)

If you sell to a large volume of prospects but at low price points, you face a volume game. Here, automation is necessary to cover the ground economically. Think of this as more akin to marketing automation: you might use AI to personalize at scale, but you must ensure efficient workflows because each deal’s revenue is small. Many SaaS SMB segments fit this: thousands of potential small-business customers. An AI outbound system could generate lots of meetings, but you’ll need extremely efficient sales follow-up to make the economics work.

AI use: high for automation (sequencing, email personalization), plus strong deliverability management (to avoid spam issues when scaling volume).

Large TAM + High ACV (Top-Right)

A large market and high-value deals are the sweet spot for AI-driven outbound. With many potential customers and significant revenue per customer, scaling outreach via AI can unlock a tremendous pipeline. This is often the mid-market or commercial segment: enough accounts to go broad, deals worth enough to justify personalized touches at scale. Here, a well-orchestrated AI SDR can dramatically augment your team’s capacity, contacting far more prospects than humanly possible and feeding your sales team with opportunities.

AI use: very high—you’ll want to leverage AI for prospecting, multi-channel sequences, follow-ups, etc., while still monitoring quality.

4.‎ Key Questions to Answer Before Deploying AI in GTM

Implementing AI lead scoring, AI orchestration, or an AI SDR requires careful planning. Work through these questions with your team before launching your revamped GTM.

1. AI in Marketing: What signals will drive AI targeting and personalization?

AI is only as good as the data/triggers you feed it. Determine which prospects the AI should contact, and when. For example, will you trigger AI outreach when a lead hits specific intent signals (e.g., visits your pricing page, researches your competitors, or has a firmographic fit)? Define the criteria clearly to avoid random or redundant contacts.

Additionally, decide what data the AI will use to personalize messages—e.g,. {Lead Industry}, {Recent Blog Post Title}, or insights from the lead’s LinkedIn. Investing in good data (intent data, account insights) will make your AI outreach far more effective and targeted.

2. AI in Marketing: How big is our TAM, and what is our ACV?

As discussed above, TAM and ACV are fundamental. If you have fewer than a few thousand prospects or very low deal values, fully automated outbound may harm more than help (see more from GTM AI Expert Brendan Short).

Conversely, huge TAM or high ACV support a stronger case for AI. Calculate the potential ROI: For example, an AI SDR that can contact 10x the prospects of a human—does that yield enough pipeline to justify its cost, given your conversion rates and deal sizes?

3. AI in Marketing: Will our AI go multi-channel?

Consider whether your AI outreach will be email-only, or involve LinkedIn, calls, SMS, or even gifting. Some AI sales agents can coordinate multi-channel sequences—e.g., send an email, then a LinkedIn message, etc., based on response or engagement. Multi-channel can boost engagement, but also adds complexity and risk. If you do this, ensure each channel’s messaging is consistent and you don’t unnaturally bombard prospects from all angles. You may start with a single channel and expand once stable.

Also, ensure compliance with communication preferences (for instance, cold texting prospects might violate consent laws or norms in some industries).

4. AI in Marketing: How will the AI reflect our brand tone and voice?

Every outbound message is a reflection of your brand. If you deploy generative AI to write emails or LinkedIn messages, you must ensure it mimics your desired tone (e.g., friendly and helpful, or formal and consultative). This often means providing the AI with style guidelines or example outputs to follow. Some tools allow you to train a custom model or set a tone profile. Plan for a human to QA the initial outputs.

As one marketing leader said, don’t use AI as a quick fix. Integrate it and keep the human touch to make content authentic. Establish an approval process initially: perhaps AI drafts go to a manager or SDR for review until you’re confident in the AI’s voice.

5. AI in Sales: Does outbound currently work for us?

Be honest about your baseline. If your team is not getting traction with outbound today (emails, calls), identify why before layering AI on top. AI can accelerate execution, but it can also amplify poor targeting or messaging. Ensure you have product-market fit and some outbound playbook that generates meetings; otherwise, focus on fixing that first.

6. AI in Sales: What is our deliverability strategy?

High-volume outbound will wreck your email deliverability if not managed carefully. Plan for tools and practices to maintain inbox placement (warming up sending domains/IPs, rotating through multiple sender addresses, monitoring spam rates). Dedicated email routing solutions (see Tools section) can automate warm-ups and throttle sending. Never neglect this: even the best AI-written emails won’t matter if they land in spam folders. For instance, sending large blasts from one domain without warm-up can drop deliverability precipitously—even a 0.3% spam complaint rate can tank your domain reputation.

7. AI in Sales: Do we have reply management workflows in place?

Great, your AI sends 5,000 emails, and some prospects start replying—who handles those replies? You need a workflow for triaging responses: auto-filtering out-of-office or unsubscribes, quickly routing interested replies to a human rep, and handling objections or questions the AI can’t answer. An AI SDR might handle the first outbound message, but a human should probably take over the conversation when a prospect engages (at least until AI can reliably navigate complex dialogues). Make sure your team (or a queue in your CRM) is ready to follow up promptly, or else leads will go cold.

8. AI in Sales: What happens when (not if) the AI hallucinates?

Generative AI can sometimes produce incorrect or made-up information (so-called “hallucinations”). In outbound, this could mean an AI email referencing a fake statistic or a wrong fact about the prospect’s company—a brand disaster! Decide how you will prevent and catch inaccuracies.

Strategies include: restricting AI to specific knowledge (don’t let it freely generate factual claims), sticking to templates with merge fields for data you provide, and having humans review automated content periodically. Also, plan a fallback: if the AI does say something odd to a prospect, how will you respond or correct it? It’s wise to include an apology and clarification in your playbook for any AI-generated mistakes.

9. AI in Sales: How are we handling meeting scheduling and handoff?

If the AI’s goal is to book meetings, you should integrate a smooth scheduling mechanism. This could be a Calendly link inserted in emails or an automated handoff to a Default or Chilipiper routing system for inbound demo requests (especially if using an AI chatbot like Warmly for inbound). Define if meetings will be round-robin to reps or go to a specific owner. Also, when a meeting is booked, ensure an opportunity or deal gets created in your CRM for the sales team—the handoff should be seamless.

10. AI in GTM: How will we track attribution and influence?

When AI sends additional outbound touches, you’ll want to measure their impact. Set up attribution tracking so that if an AI email influences a deal, you know it. This may involve using unique tracking links, UTM parameters, or sequences that log activities in CRM. Leverage your attribution software (e.g., HubSpot’s attribution reports, or dedicated platforms like Dreamdata or HockeyStack) to see how AI-sourced or AI-nurtured leads progress. Establish KPIs—for example, meetings booked by AI and pipeline generated—and track adverse outcomes (unsubscribes, spam complaints) to gauge the true impact.

11. AI in GTM: How will we calculate ROI and scale if successful?

Define success metrics and the resources AI consumes (licenses, etc.), then calculate ROI. For example, if an AI SDR tool costs $X per month, how many meetings or deals must it generate to be worthwhile compared to hiring another human SDR? Plan to monitor this. Also, if the pilot is successful, how will you scale up—more AI agents, expanding to new segments?

Conversely, set criteria for pulling back if it’s not working (e.g., after 3 months, low meeting conversion rates might mean you pause and rethink). Having reporting in place (performance dashboards) will help manage this.

5.‎ Risks and Constraints of Using AI

Even with the right strategy and prep, you must navigate several risks and constraints when using AI in GTM. Understanding these upfront will help you mitigate them:

Hallucinations & Inaccuracies

As noted, generative AI can produce incorrect or nonsensical outputs if not appropriately guided. In a B2B context, that could mean an outbound email referencing a “recent acquisition” that never happened, or misstating the prospect’s company name—fast ways to lose credibility. To combat this, put quality controls in place.

You may also run AI outputs through an approval layer—even if it’s a quick skim by an SDR—early on. Remember that real-time AI (like a live chatbot) carries a higher risk of unvetted content, so start with conservative use cases or have an easy fallback to a human agent if the AI gets confused.

Awkward or Off-Brand Tone

AI-generated content can sometimes read as awkward or robotic. Prospects are quick to delete emails that feel like mass automation. There’s also a risk of the tone not matching your brand’s voice or the recipient’s seniority level. Mitigate this by training the AI on examples of your best-performing, on-brand emails. Many teams create prompt libraries or use tools that learn from your writing style.

Also, instruct the AI to keep things concise and natural. If you find outputs are still stiff, you might dial back the AI’s role to drafting bullet points or research insights, which a human rep then crafts into a normal-sounding email.

Data Privacy and Compliance

If your AI is contacting prospects, ensure you comply with email regulations (CAN-SPAM, GDPR, CASL, etc.). Just because an AI can send 1,000 emails, doesn’t mean it should—you still need proper unsubscribe links, honor do-not-contact lists, and potentially consent for specific regions. Also, be mindful of personal data usage: if you’re feeding the AI data about individuals, ensure you do it in line with privacy policies. Some AI tools may process data off your servers; verify vendors’ compliance if that’s a concern. Essentially, outbound rules still apply—AI is not an excuse to ignore them.

Internal Adoption & Perception

One risk is internal pushback or poor adoption of AI tools by your team. Sales reps might fear being replaced or be hesitant to trust AI outputs. To address this, involve your team in the AI rollout. Make it clear the AI is there to empower them, not compete with them. Provide training and share early wins to build confidence. Also, maintain transparency: show reps the data that drives the AI’s actions so it doesn’t feel like a black box. If people worry about job security, emphasize that while AI can automate tasks, human judgment and relationship-building remain irreplaceable (and the company will upskill reps to work with the AI).

Escalation & Exception Handling

Have a plan for any unexpected situations. For example, if the AI accidentally emails a customer under a sensitive account or triggers an uncomfortable response (“Is this a bot emailing me?”), how will you handle it? It’s wise to prepare a human response for prospects who ask if the outreach was automated—many companies opt for honesty with a human follow-up:

e.g., “Yes, we use an AI assistant to help introduce companies who fit certain criteria, but I (a real human) am reaching out now to assist you personally.” Most prospects will appreciate the transparency.

By proactively addressing these risks, you can significantly reduce the downsides of AI in your outbound. Many early failures of “AI SDR” have come from skipping these safeguards, resulting in off-brand spammy outreach that tarnishes the company’s reputation. On the flip side, when done thoughtfully, AI outbound can be a game-changer: it can free your team from grunt work, uncover new opportunities, and even improve consistency. The motto to remember: automate responsibly—move fast, but with guardrails.

6.‎ Tool Recommendations Across Key Categories

Assuming you’ve evaluated the fit and planned your strategy, what tools and platforms can help implement AI in your sales/marketing stack? Below, we highlight leading solutions in several categories, from AI SDRs to deliverability, personalization, and more. (Note: inclusion isn’t an endorsement—consider your requirements—but these are popular options in 2025.)

Categories:

1. AI in Marketing: Signal Data Aggregation & Lead Scoring
2. AI in Marketing: Website Conversation & AI Chat
3. AI in Marketing: Attribution & Analytics
4. AI in Marketing: Orchestrating Signal Data to Channels
5. AI in Sales: AI SDRs & Outbound Sequencing
6. AI in Sales: Content Writing
7. AI in Sales: Email Deliverability & Routing

1. AI in Marketing: Signal Data Aggregation & Lead Scoring

Warmly: Warmly uses AI to pull in 9 different types of warm lead signals and waterfalls them across data providers to ensure the data is accurate. Using agentic lead scoring, we will look across your 1st, 2nd & 3rd party signals to maximize the chance that the time spent engaging a lead (whether cold, warm, or hot) won’t be wasted. More on our Signal Data & Waterfalls.

Commonroom: Common Room helps GTM teams know who to target, when to engage, and how to convert through sophisticated signal capture, person and account identification and enrichment, and AI-powered activation agents. Common Room specializes in signal aggregation and is most well-known for its signal data for warm leads stemming from GitHub commits and Discord communities.

2. AI in Marketing: Website Conversation & AI Chat

Tools that help tailor content and engage prospects in a more personalized way, often using AI to adjust messaging or website content per audience.

Warmly: Our inbound marketing AI agents ensure you maximize conversion on your website. Our AI-powered chatbot is fed by website de-anonymization data and your training to be almost as good as a human rep. The AI is capable of fully booking meetings and can use round-robin rules and advanced routing based on CRM data. Our Warm Offers product lets you personalize pop-ups on your website, all in the service of converting more website traffic.

Mutiny: An AI-powered website personalization platform. Mutiny alters your website copy or offers dynamically based on who comes to your website. If your outbound AI sends someone to a landing page, Mutiny could ensure that the page speaks directly to their vertical or pain point, boosting conversion. Think of it as extending personalization beyond the email into the site experience.

Drift by Salesloft: Drift offers AI chatbots that can greet website visitors (often those driven by your outbound or ads) and engage them in conversation. Drift bots use AI to understand questions and qualify leads, handing off to sales reps or booking meetings when appropriate. Essentially, if your outbound email gets a click, a Drift bot on your site could continue the AI-driven engagement by answering the prospect’s questions in real time.

Default: An advanced scheduling and routing tool often used for inbound lead conversion (e.g., instantly routing form fills to a booking calendar). In an AI outbound context, if you drive prospects to a landing page or form, Default can immediately qualify and schedule them with the correct rep. It’s ideal if you want rules-based meeting routing—say, enterprise prospects schedule with a senior AE, mid-market with a junior AE, etc., automatically. Default can also insert scheduling options directly into emails (one-click booking for the recipient).

3. AI in Marketing: Orchestrating Signal Data to Channels

Warmly: Warmly has an orchestration ability that lets you feed in its 1st, 2nd, & 3rd party intent data and set up automated follow-ups to Ads, Emails, LinkedIn & chatbot. Warmly recommends sending ads to cold leads, emails to warm leads, and LinkedIn DMs to hot leads (or letting human sales reps handle those). The world is your oyster with Warmly, since it allows for flexible play building and comes with a dedicated CSM to assist your efforts.

Clay: Clay is an orchestration tool that integrates with hundreds of providers to enrich your data, automate personalized outreach, and implement any idea for GTM. Its flexible template model lets you copy what works best from other GTM influencers. While it is complex to get started, there are lots of good videos and guides on setting up automated outbound.

4. AI in Marketing: Attribution & Analytics

Tools to track multi-touch attribution and the influence of all your channels (including this new AI outreach) on pipeline and revenue.

Dreamdata: A B2B revenue attribution platform that connects and models data across your go-to-market stack. Dreamdata can pull in CRM data, marketing automation data, ad clicks, website visits, and more, then stitch together full customer journeys. This is powerful if you have a longer sales cycle with many touchpoints. You could see if an AI outbound email was the first touch that eventually led to a deal, even if that deal closed 6 months later after many other touches. In short, it gives credit where it’s due in complex B2B journeys.

HockeyStack: A newer entrant, HockeyStack is a revenue analytics and attribution platform with a strong focus on product-led growth (PLG) insights and marketing metrics. It can track attribution of self-serve signups, marketing campaigns, and sales touchpoints in one. If your model involves both product usage and outbound sales, HockeyStack can tie those together (e.g., your AI email brings someone back to sign up for a free trial, which usage then triggers a sales outreach, etc.). It also offers a flexible no-code report builder for slicing data. Use it to prove the influence of your AI-driven touches on pipeline generation and conversion rates.

5. AI in Sales: AI SDRs & Outbound Sequencing

Tools that act as automated sales development reps—researching prospects, sending outreach, and handling initial interactions.

Warmly + 11x: Warmly has teamed up with 11x to get you Human, Automated, or Full AI options for outbounding. Warmly will allow you to notify human sales reps of hot leads so they can personalize outreach. Warmly will also let your GTM leadership team set up workflows and triggered automations using existing sequences coming from existing reps’ inboxes. And 11x is fed Warmly intent-data so its AI SDR can learn by automating outreach to your warm and hot leads to book meetings.

Regie.ai: Regie offers an AI-driven sales engagement platform (SEP). Its AI Agents can determine who to target and what to say by analyzing intent, engagement, and CRM data. Regie automates multi-touch sequences (email, social, etc.) and even list-building.

6. AI in Sales: Content Writing

Lavender: An AI email assistant that works alongside sales reps to write better emails faster. It’s like a coach: as a rep composes an email (or an AI draft is prepared), Lavender suggests improvements – from simpler language to more personalized openings – and scores the email’s quality. It can also pull in prospect details (like recent news) to enrich personalization. Lavender is helpful even if you don’t go full “AI SD”; your human SDRs can use it to significantly improve their messaging. For AI-driven campaigns, you might use Lavender to QA the AI’s output for spam-trigger words or poor wording before sending.

Autobound: Autobound generates hyper-personalized emails instantly based on news, competitor trends, podcasts, social media, financial reports, shared experiences, hobbies, and more. Their AI assistant automates a 30+ minute research and writing process to 2- 3x your reply rate. When you first sign up, Autobound’s AI reads public information on your company to build out the starting messaging for your account. New users can then build out their writing style, toggle insights off/on, & more.

7. AI in Sales: Email Deliverability & Routing

Solutions to ensure your AI-driven emails reach inboxes, through warm-ups, load balancing across send accounts, and spam monitoring.

Smartlead: A robust cold emailing platform designed to manage multiple inboxes and domains, automate warm-ups, and optimize sending schedules. Smartlead helps you spread outbound emails over several sender accounts to avoid volume spikes on one address, and its ESP matching feature aligns your sending patterns to what email providers expect.

Instantly: An all-in-one AI-powered cold outreach tool that helps find leads, send at scale, and maintain high deliverability. Instantly allows unlimited email warm-ups (it simulates human-like email interactions to build sender reputation). It also offers AI personalization features. This is a good choice if you need to ramp up outbound quickly while minimizing spam risk.

Each of these tool categories addresses a piece of the AI GTM puzzle: finding the right contacts, reaching them effectively, engaging them personally, and measuring the results. One product rarely does everything well –you’ll likely have a stack (for example: use an AI SDR tool + an email warm-up service + a scheduling link + an attribution app). The good news is that many of these tools integrate with each other and CRMs to build a relatively seamless workflow.

7.‎ Conclusion: AI in Your GTM is a Goldilocks Approach

AI can be a force multiplier in sales and marketing, but only when applied in the right situations with the proper preparation. As a RevOps or MarketingOps leader, your role is to cut through the hype and ground AI initiatives in business reality. Sometimes, that means saying “not yet” to AI outbound if the fundamentals aren’t in place (small TAM or broken outbound motion). Other times, it means championing a promising AI pilot but ensuring process, data, and team readiness so it succeeds.

At the end of the day, AI in Your GTM is a Goldilocks Approach—there is such a thing as too much AI (at least here in 2025), and there is such a thing as not enough AI. Organizations that add AI to their GTM well set clear metrics, involve their teams, and iterate quickly. You should do the same. Start small, monitor results, and iterate. For instance, you might begin with AI handling a fraction of outbound emails for one segment and compare against a control group of human-only outreach.

Learn from the data and then expand if the ROI is proven.

Culturally, within your organization, foster a mindset that AI is here to stay in GTM, that it’s the present, not just the future, but also that human creativity, empathy, and judgment remain irreplaceable. The ideal scenario is a symbiosis: AI accelerates data crunching and initial engagement, humans build relationships and close deals. As one expert advised, let AI handle the data, but you handle the storytelling.

In summary, use AI where it fits your TAM and ACV economics, ensure you have the infrastructure (data, workflows) to support it, and manage the risks smartly. That’s how RevOps and MarketingOps leaders can make AI a practical success in the sales/marketing tech stack.

PS. Research for this article and tool recommendations was gleefully assisted with OpenAI’s ChatGPT Deep Research. Deep editing & rewriting gleefully assisted by human being Maximus Greenwald & design gleefully assisted by human being Beca Bagdocimo :)

Agentic AI use cases in 2025 are powering real outcomes across marketing, sales, and revenue teams every single day.

From launching entire outbound sequences to managing deal follow-ups without hand-holding, agentic AI isn’t just saving time - it’s making moves.

Unlike traditional automation, agentic AI doesn’t wait for instructions. It acts. It reasons. It adapts in real time. 

These are self-directed agents that take high-level goals (like “increase demo conversions” or “revive stale leads”) and execute multi-step workflows to make them happen with minimal human input.

And while some teams are still meddling with manual hacks, the most forward-thinking companies are already deploying AI agents to do what used to take entire departments.

In this article, we’ll explore 10 powerful agentic AI examples and use cases and show how modern revenue teams are using them to scale smarter.

Let’s dive in!

What are the different types of AI agents?

AI agents aren’t one-size-fits-all - they exist on a spectrum of complexity and autonomy. 

Understanding this landscape is crucial to unlock the full power of agentic AI use cases in your go-to-market motion.

Let’s walk through the main types of AI agents, starting with the basics and ending with the truly game-changing.

1. Reactive agents (aka reflex agents)

This is the most basic type of AI agent. 

They don’t remember past actions or adapt; they simply react to stimuli based on predefined rules.

Example: A chatbot that always replies “Let me check that for you” when someone asks about pricing.

Useful for: Straightforward and repetitive responses.

Limitation: No context, no memory, no learning.

2. Rule-based agents (logic-driven automations)

These follow structured “if-this-then-that” logic trees. 

Slightly more intelligent than reactive agents, they can handle branching flows and decision trees.

Example: An email sequence that routes leads to different reps based on job title and region.

Useful for: Predictable processes like routing, lead scoring, or form-based workflows.

Limitation: Can’t adapt beyond what’s been manually programmed.

3. Learning agents

These agents take it a step further by incorporating machine learning to improve over time. 

They use feedback loops to optimize outcomes, like identifying which messages or timing get the best response rates.

Example: An AI model that adjusts email subject lines based on open rates across segments.

Useful for: Content optimization, personalization at scale, and pattern recognition.

Limitation: Still requires humans to define goals, train models, and oversee results.

4. Goal-based agents

These agents can make decisions by evaluating actions against a specific desired outcome. 

They simulate different possibilities and pick the one that best moves the needle toward the goal.

Example: An agent that selects the best follow-up channel (email vs. LinkedIn) based on lead engagement patterns.

Useful for: Scenarios with multiple valid options where trade-offs must be considered.

Limitation: Not yet fully autonomous, still operates under a defined goal framework.

5. Agentic AI agents (the future - and the now)

Agentic agents are fully autonomous systems that go beyond execution.

They plan, act, monitor, and adjust in real time, often across multiple tools and steps.

They take a high-level objective like “book more meetings from warm leads” and independently decide how to get it done, whether that means launching a LinkedIn DM sequence, escalating an inbound hand-raiser, or tweaking the nurture flow.

Example: Warmly’s AI SDR that notices a prospect viewed your calendar twice but didn’t book, automatically sends a personalized reminder via email, then follows up with a LinkedIn DM 24 hours later if they still haven’t engaged.

Useful for: End-to-end workflow orchestration, outbound sequencing, lead revival, post-demo follow-ups, and more.

Superpower: They take initiative. They adjust. They self-correct. And they act as true teammates, not just tools.

10 agentic AI examples in 2025 per use case

Agentic AI is no longer just for early adopters. 

From logistics to recruiting, support to sales, agents are now working behind the scenes by planning, adapting, and executing multi-step workflows without constant human oversight.

Here’s how different industries are putting agentic AI to work in the real world.

1. Sales prospecting and lead activation

Industry: B2B Sales / GTM / SaaS

Agentic AI is redefining how sales teams handle prospecting in 2025.

The most forward-thinking companies aren’t just automating tasks but deploying fully autonomous AI SDRs that act like always-on teammates.

Instead of handing sales reps a static list of leads and hoping they find time to follow up, agentic SDRs proactively engage, qualify, and activate prospects across channels with minimal human involvement.

These agents can monitor signals (like site visits, job changes, and social activity), personalize outreach based on intent data, and orchestrate multi-touch follow-up across email, LinkedIn, and live chat. 

When the time’s right, they escalate to a rep or book the meeting.

Some of the key benefits include:

• 24/7 coverage and responsiveness.
• Infinite outbound capacity.
• Human-grade personalization at scale.
• Integrated multichannel sequencing.
• Direct meeting booking with no rep involvement.

Warmly’s SDR agents are the perfect example of AI agents built from the ground up to scale sales development without ballooning headcount. 

Here’s how they work:

1. AI-powered outbound - Warmly’s agent autonomously handles outbound prospecting across a limitless number of leads by researching, prioritizing, and personalizing outreach so your human reps can focus on high-value conversations. 
2. Automated lead nurturing - AI SDR follows up with smart sequences across email and LinkedIn, re-engaging leads who’ve gone quiet, keeping every deal moving through the funnel without the team lifting a finger.
3. Conversational AI for booking - Warmly also deploys an AI chatbot that lives on your website 24/7, engaging visitors in real time with dynamic, context-aware conversation. It adapts to each visitor’s intent and helps book meetings directly, before competitors get the chance.

2. Marketing campaign orchestration

Industry: Marketing / B2B SaaS / GTM Ops

Today, marketers are finally off the hamster wheel of building campaigns from scratch, exporting CSVs, uploading audiences, and manually stitching together ads, emails, and retargeting flows.

Agentic AI has changed the game by orchestrating multi-channel campaigns end-to-end.

These agents can do anything from identifying the ideal audience using dynamic data and launching personalized campaigns across platforms to tracking real-time signals and automatically shifting budget or creative based on performance.

They don’t just launch campaigns. They manage them like a strategist would: constantly optimizing for intent, timing, and ROI.

Warmly, for example, brings this agentic orchestration to life with two powerful agents designed to supercharge marketing execution and scale without headcount:

1. Marketing Ops agent handles campaign planning, segmentation, routing, and follow-up across your stack. Here’s how it works:

• AI-powered ICP identification - Warmly uses AI to uncover the true behavioral, contextual, and demographic traits of your best customers, then continuously finds new leads that match your ideal profile.
• Real-time data & signal monitoring - The agent pulls in signals from 10+ data providers, such as website activity, firmographic changes, job shifts, and more, to ensure every campaign reaches the right audience at the right time.
• Lead routing & notifications - When a lead reaches a threshold of buying intent, the agent sends the right Slack alert, routes to the right rep, and triggers the right action (campaign, outreach, or offer) in real time.

2. Warmly’s Demand Gen agent targets leads showing warm signals on and off your site and syncs those segments directly into your ad platforms. Here’s what it does:

• Signal-based ad targeting - Warmly uses dozens of real-time signals to build high-conversion lead segments and push them straight to Meta, LinkedIn, and Google for precise targeting.
• Automatic follow-up based on buyer readiness - Warmly’s agent sends each lead into the proper flow, whether a targeted ad, an email sequence, a chatbot offer, or a human outreach, based on how close they are to conversion.
• Warm offers - Each lead is presented with a personalized offer tailored to their behavior, past interactions, customer journey stage, etc.

3. Software development automation

Industry: Software / Engineering / DevOps

Agentic AI is rapidly transforming software development from a manual, linear process into a fluid, self-directed workflow. 

These agents don’t simply autocomplete functions. They understand the problem, outline a multi-step solution, write the code, debug errors, and even submit pull requests. 

They're goal-driven, not task-bound.

Take Devin by Cognition, for example.

Devin is widely considered the world’s first fully agentic AI software engineer. 

It takes high-level engineering tasks, like "build a web app with user authentication", and executes them independently like this:

• It breaks down requirements into subtasks.
• Writes clean, modular code across front- and back-end.
• Sets up dev environments, runs tests, and debugs on the fly.
• Collaborates via GitHub to track progress.
• Learns from feedback and iterates

This goes far beyond Copilot-style code suggestions.

Devin truly acts like a junior developer capable of owning and shipping scoped tasks end-to-end.

As a result, developer teams get:

• Dramatic reduction in dev time for repetitive or well-scoped tasks.
• More time for senior engineers to focus on architecture and innovation.
• Faster prototyping and iteration cycles.
• Enhanced productivity across levels.

4. Claims processing in insurance

Industry: Insurance / InsurTech / Risk management

Claims processing has long been one of the most resource-intensive and delay-prone areas in insurance. 

Traditionally, it has involved human adjusters reviewing lengthy documentation, assessing eligibility, and manually coordinating payouts, which was a slow, costly process prone to errors and inconsistencies.

But in 2025, agentic AI is stepping in to handle this complexity at scale.

These agents don’t just extract data from claim forms. 

They understand policy rules, assess damage using structured and unstructured data (including images and scanned PDFs), and autonomously manage the entire claims lifecycle from intake to payout.

As such, AI agents can tackle a wide range of tasks, from reading and extracting information from structured claims forms, emails, and third-party data sources to assessing evidence and detecting fraud red flags.

As a result, companies implementing AI agents in claims processing experience:

1. Faster claims resolution - Many straightforward cases are processed within minutes, not days.
2. Lower operational costs - Reduces the need for large back-office claims processing teams.
3. Consistency & accuracy - Eliminates human oversight errors and bias in evaluations.
4. Fraud detection - AI can cross-reference data across claims to flag anomalies.
5. Improved CX - Customers receive faster updates and less back-and-forth.

5. Retail and e-commerce optimization

Industry: Retail / E-commerce / DTC / Marketplaces

In today’s ultra-competitive digital retail landscape, success depends on making fast, personalized, and data-informed decisions across thousands (or millions) of SKUs, users, and touchpoints. 

That’s where agentic AI steps in.

Most common use cases in retail include:

• Personalized product recommendations - AI agents analyze individual shopper behavior, purchase history, session activity, and psychographic traits to dynamically display products most likely to convert, adapting in real-time.
• Dynamic pricing optimization - Agents monitor competitor pricing, demand trends, and stock levels, and autonomously adjust product prices to maximize margin or market share. 
• Cart abandonment recovery - When a user leaves with items in their cart, an AI agent determines the best recovery strategy: a reminder email, a retargeted ad, a real-time SMS with a discount, or a chatbot nudge when they revisit the site.

The outcome?

Higher conversion rates, better inventory and ad budget allocation, optimized pricing, and less manual grind.

6. Healthcare administration

Industry: Healthcare / HealthTech / Clinics / Hospitals

Healthcare has long struggled with heavy administrative overhead, such as appointment scheduling, insurance claims, regulatory compliance, and patient communication. 

These tasks are essential but time-consuming, often diverting attention away from patient care.

Agentic AI can help healthcare organizations streamline operations by handling these tasks autonomously. 

Where traditional RPA or workflow automation stops short, agentic AI takes initiative, coordinating across multiple systems, monitoring signals, and driving outcomes without waiting for human input.

Some of the real-world applications of agentic AI in healthcare include:

• Appointment scheduling and management - AI agents autonomously book, confirm, and reschedule appointments based on clinician availability, patient preferences, appointment urgency, and location.
• Compliance and documentation support - AI agents generate and archive required regulatory documentation, including audit trails and HIPAA compliance logs, reducing manual oversight and risk.
• Patient intake and digital onboarding - From collecting pre-visit forms to syncing data with the electronic health record (EHR), agentic AI streamlines intake, reduces front-desk workload, and improves patient experience.

By taking over time-consuming administrative tasks, agentic AI significantly reduces the operational burden on providers and staff, allowing clinical teams to dedicate more of their time and energy to patient care, rather than paperwork.

7. Customer support automation

Industry: SaaS / Ecommerce / Telecom / Financial Services

Customer support is one of the most natural fits for agentic AI. 

Why?

Because support interactions are often time-sensitive, multi-step, and highly repetitive, yet require enough reasoning and personalization that traditional automation often falls short.

Here are some of the tasks AI customer support agents can tackle:

• Contextual issue resolution - Agentic support agents understand the full context of a customer’s request, search across internal knowledge bases, past tickets, and third-party systems, then deliver tailored solutions, often before the customer even finishes explaining the problem.
• Self-improving response libraries - Agentic systems track which replies lead to successful resolutions and learn from every interaction, adapting their recommendations and behavior across support tiers and customer segments.
• Human-aware escalation - When a case exceeds their confidence level, agentic support agents don’t just pass the ticket upstream; they summarize the issue, attach all relevant context, and recommend the best course of action. 

This way, with agentic AI handling routine and complex support tasks, response and resolution times drop significantly, even for nuanced issues. 

This leads to more consistent, accurate service across every channel while lowering operational costs and freeing human agents to focus on higher-impact conversations requiring strategic thinking or empathy.

8. Human resources assistance

Industry: HR / Talent Acquisition / People Ops

HR teams today are expected to deliver seamless candidate experiences, manage complex onboarding processes, and support employee growth, often with limited staff and fragmented tools.

That’s where agentic AI saves the day, helping HR leaders scale these efforts with speed and precision.

Unlike legacy HR automation tools that rely on rigid workflows, agentic HR agents make autonomous decisions, adapt to each candidate or employee, and collaborate across departments, acting more like a strategic operations partner than a script executor.

This means they can independently handle a wide range of operations, such as:

• Candidate screening and shortlisting - AI agents autonomously review resumes, cross-reference with job descriptions, assess candidate fit based on prior experience, certifications, or even soft skills (via writing samples or public data), and create ranked shortlists for hiring managers.
• Interview scheduling and coordination - Once shortlisted, the agent reaches out to candidates, aligns interview times with panel availability, sends calendar invites, and follows up with reminders, minimizing recruiter back-and-forth.
• Employee onboarding - After a hire is made, the agent guides the new employee through IT setup, policy training, document submission, and first-week scheduling, ensuring nothing is missed and the process is personalized by role and location.

By doing all this, agentic AI improves time-to-hire, reduces administrative HR workload, and enhances employee and candidate experiences through consistent, proactive communication. 

As a result, HR teams can shift from reactive task handling to strategic people enablement.

9. Supply chain management

Industry: Logistics / Manufacturing / Retail Operations

Supply chains have always been complex, but agility and visibility are no longer optional in 2025 with ongoing global disruptions, labor shortages, and rising customer expectations. 

Agentic AI is stepping in to autonomously manage supply chain workflows, respond to real-time data, and ensure resilience at scale.

These agents don’t just alert managers about problems - they solve them by analyzing delays, rebalancing inventory, optimizing delivery routes, and rerouting logistics operations on the fly.

They can take care of the following operations:

• Demand forecasting - Agents analyze historical sales, seasonal trends, market signals, and external data (weather or news) to project future demand, then adjust procurement plans accordingly.
• Dynamic logistics coordination - When shipping delays or route disruptions occur, the agent identifies alternative carriers, reroutes shipments, and updates ETAs across systems, reducing delays and avoiding manual intervention.
• Supplier risk monitoring - By scanning supplier behavior, financial signals, and regional news, agents detect early warning signs of disruption, allowing businesses to diversify vendors or secure alternative sources before problems escalate.

The result?

Agentic AI reduces operational risk, shortens lead times, and increases supply chain responsiveness, freeing teams from reactive firefighting.

10. Academic research assistance

Industry: Higher Education / Scientific Research / Think Tanks / R&D

Academic research has always been a time-intensive process, including things like sifting through hundreds of papers, conducting literature reviews, compiling data, drafting manuscripts, and applying for funding. 

Today, agentic AI is being deployed as a powerful co-pilot across every phase of the research lifecycle.

These agents don’t just summarize articles or generate text. They reason, plan, and take action on behalf of researchers. 

For example, they can be used for:

• Automated literature reviews - Agents scan thousands of peer-reviewed articles across platforms like PubMed, JSTOR, and Google Scholar, identifying recurring themes, highlighting conflicting findings, and summarizing the current state of knowledge, cutting down what once took weeks to a few hours.
• Research gap identification - Beyond summarizing, these agents detect where gaps in evidence exist, suggesting original research questions or hypotheses based on what hasn’t been explored or sufficiently addressed.
• Data analysis and visualization - Agentic tools ingest raw research data, run statistical models, check assumptions, and create relevant graphs or visualizations, automating a large portion of quantitative analysis.

Why is this so important for the academic community?

Well, agentic AI dramatically reduces the time needed for literature synthesis, improves the quality of insights by surfacing non-obvious patterns, and supports researchers in producing more rigorous, complete, and timely work. 

It’s not a shortcut to thinking - the humans behind it still need to do the heavy lifting - but a partner in accelerating it.

Three real-life examples of agentic AI in action

We’ve explored how agentic AI is being applied across industries. 

But what does this look like in practice? 

In this section, we’ll dive into a few real-world examples of companies already using agentic AI to solve complex problems, automate multi-step workflows, and generate real business results. 

These aren’t experiments - they’re live deployments showing just how powerful autonomous agents can be.

1. Connecteam scales outreach with an 11x’s AI SDR without hiring a single rep

As Connecteam expanded into new verticals like healthcare, retail, and construction, their sales team faced a growing challenge: 

How do you scale personalized outreach without adding more SDRs?

Traditional outbound channels, such as email and SMS, weren’t cutting it. 

Engagement was low, reactivating old leads was difficult, and their team was stretched thin managing 120,000+ monthly calls while still booking 20 meetings per week. 

To break this cycle, Connecteam partnered with 11x to deploy Julian, an AI-powered SDR built to operate like a human phone rep - but at a scale no team could match.

Instead of spinning up generic automations, 11x embedded themselves in Connecteam’s GTM strategy. 

They trained Julian on vertical-specific messaging, aligned the agent with live product updates, and designed workflows around real operational gaps, especially re-engaging closed-lost and low-intent leads their human reps couldn’t prioritize.

Julian didn’t just boost capacity - he completely transformed how Connecteam engaged leads. 

From personalized, intent-driven follow-ups to real-time meeting confirmations, Julian helped cut no-show rates by 73%, reactivated thousands of dormant leads, and doubled call coverage, all without expanding the sales team.

The results include:

• $450,000+ saved annually in SDR salaries.
• 120,000+ monthly phone calls handled autonomously.
• 73% drop in meeting no-shows.
• $30K increase in monthly revenue per SDR.
• 20+ qualified meetings booked weekly—with a 40% conversion rate.

Warmly is proud to partner with 11x to bring this kind of agentic AI firepower into our own platform, helping sales teams run 24/7 outreach, re-engage pipeline, and scale SDR capacity without adding headcount.

2. Equinix uses an AI copilot to eliminate IT queues at global scale

As the world’s largest interconnection and colocation platform, Equinix runs a truly global operation with thousands of employees relying on fast, accurate IT support to stay productive. 

But with a lean IT team of 400 supporting a distributed workforce across the US, UK, and Asia, the service desk was under constant strain.

The challenge? Scaling support without scaling headcount.

Tickets often sat unresolved due to misrouting, especially with hundreds of possible IT assignment groups and a help desk team located halfway across the world from most end users. 

Response delays, resolution backlogs, and time zone mismatches all aggravated the problem.

Equinix needed more than a generic chatbot. 

They needed an intelligent triage system that could understand requests, reason through them, and route them correctly the first time.

Enter Moveworks and E-Bot.

In 2019, Equinix launched E-Bot, a fully autonomous AI agent capable of resolving thousands of common IT issues end-to-end, right inside Microsoft Teams.

More importantly, it acted as a real-time triage co-pilot. 

With 96% routing accuracy, E-Bot instantly assigned unresolved issues to the correct subject matter expert, matching or exceeding the performance of human agents, and doing it in under 30 seconds (versus the 5-hour average for L1 help desk queues).

This shift freed Equinix’s IT agents from tedious ticket triage and allowed them to focus on complex, high-impact work, all while dramatically improving service delivery speed and employee satisfaction.

The results:

• 96% routing accuracy to the correct IT expert group.
• 82% of tickets are now routed autonomously by E-Bot.
• 30-second average triage time (vs. 5 hours manually).
• ~33% reduction in ticket lifecycle time.
• Millions in savings through reduced manual workload and improved resolution velocity.

3. Dutch insurer automates 91% of motor claims with Beam’s AI agent

For insurers, speed, accuracy, and customer satisfaction in claims processing aren’t just operational goals - they’re core to competitiveness. 

But for one major Dutch insurance provider, processing high volumes of motor claims manually had become a bottleneck.

Each claim required adjusters to analyze coverage, liability, and documentation before a payout could be approved, significantly slowing resolution times, consuming costly human hours, and frustrating customers. 

And with rising claim volumes and no easy way to scale personnel, the company needed a smarter solution.

The solution: AI-powered claims decisioning from Beam.

To tackle the problem, the insurer partnered with Beam to deploy a custom-built AI agent directly into their claims management workflow. 

This wasn’t a bolt-on automation - it was a deeply integrated, decision-capable agent that mirrored the process a human claims adjuster would follow.

The agent handled three critical steps autonomously:

1. Intake and classification - The agent analyzed incoming claims to identify which were eligible for automation, based on structured criteria.
2. Automated assessment - It applied business rules and liability logic to determine whether the claim should be approved, denied, or escalated.
3. Decision and action - The agent executed low-risk approvals, rejected invalid claims, or forwarded complex cases to human adjusters, ensuring quality control without creating backlog.

The results:

• 91% of eligible motor claims were processed automatically.
• 46% reduction in average claim processing time.
• 9% improvement in Net Promoter Score (NPS), driven by faster resolutions and consistent outcomes.

Next steps: Implementing agentic AI in your workflows

As the real-world examples in this article show, agentic AI isn’t a future trend - it’s already transforming how modern teams operate. 

From streamlining claims processing and eliminating IT ticket queues to driving pipeline and booking meetings at scale, AI agents are delivering measurable impact across every corner of the business.

The shift is clear: the most effective teams are no longer just automating tasks - they're deploying AI agents that think, adapt, and act autonomously to drive outcomes.

If you're also looking to scale your GTM without adding headcount, Warmly’s agentic AI platform is built for exactly that. 

Our AI SDRs, marketing ops agents, AI Copilots, and demand gen workflows help you:

• Prospect 24/7 based on real-time signals.
• Re-engage leads and warm up pipeline automatically.
• Orchestrate multichannel campaigns without manual lift.
• Book more meetings, generate more pipeline, and move faster without burning out your team.

Ready to scale smarter? Book a demo with Warmly and see agentic AI in action.

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AI-driven agentic workflows are redefining how modern GTM teams operate, replacing one-off automations and manual busywork with self-directed systems that plan, execute, and optimize tasks autonomously.

This is bigger than automation. 

It’s a shift toward orchestrated, intelligent execution across sales, marketing, and revenue operations. 

From booking meetings to launching campaigns and reactivating cold leads, agentic workflows make your entire GTM process smarter - and your team a whole lot more efficient.

In this article, I’ll break down what AI agentic workflows actually are, how they work, real-world use cases you can steal, and which software platforms are leading the way in 2025.

What are agentic workflows?

Agentic workflows are dynamic, goal-driven processes powered by autonomous AI agents that can reason, plan, and act independently. 

Unlike traditional workflows, which are predefined and static, agentic workflows evolve in real-time based on context, inputs, and outcomes.

So, what exactly makes them that special? 

Agentic workflows don't just run tasks - they own them. 

For example, a traditional lead-nurturing workflow might trigger the same email sequence for every MQL. 

But an agentic workflow could analyze each lead’s behavior, adjust the cadence, personalize the content, pause if intent drops, or even alert sales when engagement peaks.

This makes them ideal for modern GTM teams that want to scale without scaling headcount, reduce manual effort, and unlock real-time responsiveness across the funnel.

Essentially, at their core, agentic workflows are:

• Autonomous - Agents don’t need constant human input.
• Context-aware - They adapt based on goals, behavior, and changing signals.
• Self-optimizing - They learn from feedback and improve over time.
• Collaborative - Agents can interact with each other or with humans to execute multi-step processes.

This loop of planning, doing, evaluating, and improving allows agentic workflows to operate more like a smart teammate than a basic script.

What is the difference between automated workflows, AI workflows, and agentic workflows?

This is the first question most new users ask, and for good reason.

These three workflow types are often lumped together, but they are fundamentally different in how they operate and respond to change, and the level of autonomy they bring to the table.

Let’s break them down:

1. Automated workflows (rule-based, non-AI)

This is classic "if-this-then-that" automation. 

You define each step in advance, and the system executes them in a strict linear sequence.

Example: When a lead fills out a form → wait 2 days → send email #1 → wait 3 days → send email #2.

Limitation: If the lead replies or shows high intent midway through, the workflow doesn’t adapt; it just keeps moving forward.

These workflows are efficient for repeatable tasks but brittle in dynamic or unpredictable scenarios.

2. AI workflows (non-agentic)

AI workflows add a layer of intelligence - usually an AI model - to improve the quality of the response, but not the process itself.

Example: A chatbot that uses GPT to generate an answer based on your query.

Limitation: The AI acts once, in a straight line. It doesn’t reason, plan, or reflect - it simply produces an output and stops.

There’s intelligence in the response, but zero autonomy in deciding what to do next.

3. Agentic workflows (goal-oriented, adaptive, reflective)

Agentic workflows take it a step further. 

Here, an AI agent owns the task from start to finish, constantly evaluating the situation, making decisions, and adjusting course as needed.

Let’s look at a workflow example:

1. User query triggers the process.
2. The agent makes a plan based on the goal and available tools.
3. It executes actions and monitors the results in real-time.
4. If the outcome isn’t satisfactory, it reflects, re-plans, and tries a better approach.
5. Only when the goal is achieved does it deliver the final response.

This diagram illustrates the shift from static automation to fully agentic systems. 

While automation moves linearly, agentic workflows form a feedback loop of planning, execution, and reflection, much closer to how humans work.

What is the difference between agentic architectures and agentic workflows?

These terms are often used interchangeably, but they describe different layers of the system:

1. Agentic architectures refer to the underlying design or structure that enables agent behavior. This includes things like how agents are built, how they reason, how they manage memory, tools, goals, and learning.
2. Agentic workflows, on the other hand, are the applied outcome. They describe how those agents function in real-world contexts, such as pipeline generation, outbound sequencing, or ad optimization.

Put simply:

• Agentic architecture is the engine.
• Agentic workflow is the car on the road.

You can have an agentic architecture without a well-designed workflow, but you can’t have a truly agentic workflow without the architecture to support it.

Top 10 AI agentic workflows that you can build in 2025

Now that we’ve unpacked what agentic workflows are (and how they differ from basic automation), let’s get into the good stuff: real workflows you can actually build.

Below are ten high-impact AI agentic workflows that modern GTM teams are already using - or will be soon in 2025. 

From outbound lead generation to dynamic pipeline management, these aren’t theoretical ideas. 

They’re practical, proven, and powered by agents that think, act, and adapt on their own.

1. Autonomous lead prioritization and routing

In most GTM teams, lead management is still a bottleneck. 

New contacts enter the system through marketing forms, website visits, and third-party data sources, but figuring out who’s worth chasing still takes time, coordination, and manual CRM hygiene.

Agentic workflows change that.

With the right AI agent in place, lead prioritization becomes a self-optimizing process. 

Instead of waiting for humans to vet leads or assign ownership, the workflow continuously evaluates every new contact using things like:

• Real-time firmographics.
• Behavior signals (like page visits or email clicks).
• Account data.
• Funnel stage.

Here’s what that looks like in action:

1. Automatic enrichment - As soon as a new lead hits your system, the workflow enriches it with firmographic data (company size, industry, revenue, etc.) and intent signals from third-party platforms.
2. Dynamic scoring - Based on engagement history (e.g., whether they visited a pricing page or opened 3 emails this week), the agent assigns a real-time lead score that updates as the lead continues to interact with your brand.
3. Instant routing - High-fit, high-intent leads are instantly surfaced to the right reps via Slack, email, or CRM alerts with no delay or manual triage.

Warmly’s AI Marketing Ops Agent is purpose-built for precisely this kind of orchestration.

It doesn’t just keep your CRM clean, it proactively flags hot leads and gets them in front of your team while they’re still warm.

It even auto-classifies leads by ICP match and sales-readiness, helping SDRs skip the guesswork and jump straight into action.

This kind of agentic workflow is especially valuable for teams running inbound-heavy strategies or working across multiple segments where lead quality varies widely. 

Automating the prioritization layer ensures that your reps spend time where it counts - on high-fit leads with high intent.

2. Dynamic ad campaign optimization

Running paid campaigns is no longer just about launching and waiting. 

The most effective teams today treat advertising as a living, breathing system - and one that needs to evolve based on performance data, audience behavior, and shifting market signals. 

That’s precisely where agentic workflows shine.

In a dynamic campaign optimization setup, an AI agent monitors performance across ad platforms (Google, LinkedIn, Meta, etc.) and continuously adjusts:

1. Budget allocation - Shifting spend away from underperforming audiences and toward higher-converting segments.
2. Ad creatives - A/B testing copy, images, and formats, then replacing low-performing variants on the fly.
3. Targeting rules - Refining filters like job title, company size, or intent level based on engagement data.
4. Channel mix - Identifying where leads are actually converting and rebalancing investment accordingly.

This level of adaptability is hard to achieve manually, especially when running dozens of variations across multiple platforms. 

But with an agentic workflow, the system doesn’t just analyze performance; it makes decisions, acts on them, and evaluates outcomes in an ongoing loop.

Warmly’s AI Demand Gen Agent helps enable this kind of adaptive campaign orchestration by connecting real-time buyer signals to high-intent execution. 

Using signal-based ad targeting, it tracks both on-site and off-site behavior to automatically create precise lead segments that sync directly with your paid channels, allowing you to run hyper-targeted campaigns without manual audience building.

Beyond targeting, the agent can personalize the next step. 

Based on a lead’s behavior, it routes them into the right follow-up, whether that’s a retargeting ad, a tailored nurture sequence, or even a dynamic “warm offer” shown right on your website. 

It essentially turns your demand generation into a responsive system that adapts in real-time to intent, not just demographics.

3. Multi-channel outbound sequencing

Traditional outbound is often rigid: static sequences, predefined cadences, and cookie-cutter messaging that doesn’t adapt to how prospects actually respond. 

But agentic workflows flip that model. 

Instead of locking reps into fixed playbooks, AI agents can orchestrate multi-channel outreach that adjusts in real time, based on what’s working and what’s not.

In this kind of setup, an agent monitors each prospect's engagement across email, LinkedIn, and other touchpoints, and dynamically decides:

• When to follow up (and through which channel).
• What tone or message to use, depending on past behavior and persona.
• Whether to pause or escalate based on signals like opens, replies, or social activity.
• Which step comes next, for example, switching from a written email to a voice note or video if previous messages were ignored.

Instead of sending the same 6-step cadence to every lead, the workflow evolves per prospect. 

That’s what makes it agentic - it’s not just automated; it’s aware.

Warmly’s AI SDR Agent supports this kind of workflow in three key ways. 

First, it acts as a 24/7 conversational layer on your website, engaging visitors instantly via AI-powered chat that adapts to each person’s level of buying intent. 

Instead of waiting for reps to respond manually, prospects can ask questions, get tailored answers, and even book meetings autonomously.

Second, it handles automated lead nurturing across channels. 

If a prospect isn’t ready to convert, the agent can follow up via email and LinkedIn DMs, keeping them engaged and moving forward without letting anyone slip through the cracks.

Finally, the agent supports always-on outbounding. 

It prospectively targets and engages leads at scale, helping teams cover more accounts without increasing headcount, freeing up your human SDRs to focus on higher-value conversations and warmer opportunities.

4. Real-time meeting preparation and follow-up

Prepping for a sales meeting often means digging through CRM notes, LinkedIn profiles, email threads, and past conversations just to piece together who the prospect is and what matters to them. 

Multiply that across 5–10 meetings a day, and the cognitive load becomes real.

Agentic workflows ease this pressure by automating meeting prep and follow-up. 

AI agents can:

• Pull in relevant background data and summarize recent interactions.
• Generate a suggested agenda or talking points based on the account’s context.
• Identify intent signals, pain points, or objections based on engagement.
• Draft follow-up messages that feel personal, not templated.

Warmly’s AI Copilots are purpose-built for this kind of behind-the-scenes enablement. 

They surface timely insights about who the prospect is, why they’re interested, and even suggest what to say, eliminating guesswork and helping reps lead with relevance. 

After the call, the same workflow can trigger personalized follow-ups based on what happened in the meeting.

And it doesn’t stop at email. 

With Warm Chat, reps can be looped into high-intent conversations as they happen, or even shift into a face-to-face video call directly from chat, turning interest into real momentum without delay.

By combining real-time data, smart recommendations, and conversational flexibility, these agentic workflows free up reps to focus on what they do best: having meaningful, human conversations that move deals forward.

5. Automated customer support ticket resolution

Customer support is one of the clearest areas where agentic workflows can create immediate impact, not just by answering FAQs, but by actively resolving complex issues, escalating smartly, and improving over time.

In a traditional setup, support workflows rely on static routing rules or decision trees. 

Tickets are triaged based on keywords or categories, then passed to agents with limited context. 

That might work for basic volume handling, but it doesn’t scale well, and it doesn’t learn.

Agentic workflows, on the other hand, are built to:

• Understand context, arsing ticket history, product data, and user behavior to determine the root cause.
• Take action autonomously, whether that means resetting a password, issuing a refund, or generating a report.
• Escalate smartly, looping in a human only when necessary, with full context attached.
• Reflect and improve, learning from outcomes to improve future ticket handling.

Even more advanced workflows go beyond resolution. 

They can monitor sentiment, flag churn risk based on complaint frequency or NPS scores, and trigger retention workflows when red flags appear.

This level of autonomous service doesn’t just reduce response times.

It gives support teams the bandwidth to focus on complex, high-stakes conversations that require human empathy and creativity.

6. Intelligent content personalization

We’ve all experienced irrelevant content, such as generic nurture emails, static product recommendations, or homepage CTAs that fail to take into account who we are or what we’ve done. 

Agentic workflows are designed to eliminate that disconnect by continuously adapting content to each user’s preferences, behaviors, and context.

At the core of this use case is an AI agent that dynamically assembles content in real time. 

It doesn’t just use broad segments like “CMO” or “small business”.

Instead, it factors in recent activity, historical patterns, and even traffic source to decide what to show and how to frame it.

For example:

• A first-time visitor from LinkedIn might see an intro-focused landing page, while a returning user from a high-intent ABM list sees a personalized call-to-action and case study.
• Product emails adapt based on usage patterns, showing tips or feature highlights for what the user hasn’t tried yet.
• On-site banners offer different assets depending on industry or persona, without requiring hard-coded variations.

This is especially useful for e-commerce brands that can use agentic personalization to adjust homepage layouts and recommend products based on browsing and purchase history, boosting conversion rates and cart value. 

For example, eBay uses AI agents to automate tasks such as code generation and creating marketing content. 

These agents analyze user engagement data to tailor interactions and develop personalized offers.

The result? Better engagement, deeper relevance, and higher conversion, without creating hundreds of manual content variants.

7. Proactive churn prediction and retention strategies

Churn is rarely a surprise; it builds up over time. 

The challenge is catching it early enough to take action. 

That’s where agentic workflows come in.

Instead of waiting for cancellation requests or negative feedback, an AI agent continuously monitors customer signals that indicate declining engagement or satisfaction. 

This might include:

• Decreasing product usage over time.
• Downgrades in plan or feature adoption.
• Negative sentiment in support tickets or NPS responses.
• Missed check-ins, delayed invoice payments, or lack of activity from key users.

Based on patterns and thresholds, the agent can flag accounts at risk of churn and then act, triggering a series of tailored interventions designed to re-engage the customer. 

That might mean:

1. A check-in email from the CSM with personalized context.
2. A special offer or incentive pushed via email or on-site banner.
3. Escalation to a support or success rep with full account history attached

The key difference here is the loop: the agent doesn’t just analyze risk - it responds. 

It learns from which actions lead to retention and adjusts future plays accordingly.

This kind of workflow is particularly valuable in SaaS and subscription businesses, where long-term retention drives profitability. 

By catching red flags early and personalizing the response, teams can reduce preventable churn and maintain healthier customer relationships without needing to manually comb through usage dashboards or CRM data every week.

8. Automated compliance monitoring

In industries such as finance, healthcare, law, and insurance, compliance isn’t just an operational overhead.

It’s a business-critical function with real consequences for failure. 

But the traditional approach to compliance monitoring is often reactive, manual, and siloed: audits run quarterly, alerts get buried, and teams scramble to fix issues after the fact.

Agentic workflows offer a proactive alternative.

In this setup, AI agents operate in the background, continuously scanning workflows, communications, and transactional data for any violations of policy, regulation, or internal standards. 

But unlike basic automation tools, these agents don’t just flag issues. 

They understand context, take action, and reflect on the outcomes to get better over time.

Here’s what that can look like:

• Monitoring outbound email, chat, or documentation for non-compliant language or missing disclosures.
• Auditing transactional or billing workflows in real-time, catching inconsistencies before they reach customers.
• Detecting data privacy risks, such as unsecured access to sensitive information or unusual API activity.
• Auto-generating reports and escalation paths based on the severity or frequency of flagged behavior.

Instead of relying on human oversight for every detail, agentic workflows enable teams to scale their compliance posture across all processes without increasing headcount or slowing things down.

The result is a more resilient system: one that catches issues earlier, minimizes reputational risk, and gives teams peace of mind that someone, or something, is always watching.

9. Supply chain optimization

Supply chains are complex systems - fragile, data-intensive, and highly interdependent. 

Between shifting customer demand, fluctuating inventory, supplier delays, and logistics constraints, even small inefficiencies can ripple into costly bottlenecks. 

Traditional planning methods struggle to keep up with this volatility. Agentic workflows don’t.

By introducing AI agents into the supply chain, companies can move from reactive planning to adaptive, real-time optimization. 

These agents continuously monitor data streams across your ecosystem from procurement and production to distribution and delivery, and take proactive steps to adjust based on current conditions.

Here’s how this plays out in practice:

• Forecasting and demand sensing - AI agents analyze sales trends, seasonality, and external signals (like weather or economic shifts) to improve forecast accuracy and detect surges or drops in demand.
• Supplier risk mitigation - If a supplier delay or quality issue is detected, the agent can initiate a contingency, rerouting orders to a secondary supplier or alerting procurement before a disruption hits fulfilment.
• Logistics rerouting - In case of delays, traffic disruptions, or warehouse constraints, an agent can automatically identify faster routes or alternative delivery partners.

This means that the agent doesn’t just analyze and report - it takes action, learns from outcomes, and evolves its decision-making with every cycle.

The benefits are hard to ignore: fewer stockouts, faster fulfilment, lower transportation costs, and a supply chain that reacts before things go wrong.

10. Employee onboarding and training

A great onboarding experience can set the tone for everything that follows: productivity, retention, and morale. 

But most onboarding workflows today are still rigid and impersonal: a static checklist, a few scattered PDFs, maybe a welcome video. 

That might check the boxes, but it doesn’t meet people where they are or adapt to how they learn.

Agentic workflows offer a more intelligent and dynamic approach.

In this setup, AI agents guide new hires through personalized onboarding journeys tailored to their role, seniority, and pace. 

The agent adapts to the individual, serving the right resources at the right time, tracking progress automatically, and making the experience more human (ironically, by being more adaptive than a human-run process can be at scale).

Here’s how that might work:

• Personalized learning paths - An AE gets different modules and sales collateral than an engineer. A senior hire skips the basics, while a junior hire gets more guidance.
• Progress tracking and nudges - The agent tracks learning progress, flags if someone is falling behind, and gently nudges them with reminders or even escalates to a manager if needed.
• Two-way support - If the new hire asks questions or hits roadblocks, the agent can respond instantly or route them to the appropriate internal contact.

Implementing AI agents in these workflows leads to faster time to productivity, fewer knowledge gaps, and a better employee experience, especially for remote or hybrid teams where onboarding can easily feel fragmented.

What are the benefits of agentic workflows for businesses?

Agentic workflows bring intelligence, adaptability, and strategic value to how teams operate. 

By giving AI agents the ability to reason, act, and self-improve, businesses can unlock benefits across speed, scale, and decision quality. 

Here are some of the key advantages:

1. Increased operational efficiency - Whether it’s lead routing, ticket resolution, or campaign execution, AI agents handle the heavy lifting, freeing up your team to focus on higher-value work.
2. Faster, smarter decision-making - Unlike traditional workflows that follow fixed rules, agentic systems can make real-time decisions based on live data and changing conditions, helping your business respond faster to customer needs, market shifts, or internal bottlenecks.
3. Scalability without scaling headcount - Need to engage thousands of leads, run dozens of campaigns, or triage hundreds of support tickets? With agentic workflows, you can scale these functions without hiring additional team members and burnout.
4. Higher personalization at scale - Because agents can track and reflect on individual behaviors, they can tailor messages, offers, and content to each person, leading to more relevant experiences for your customers and better conversion rates for your business.
5. Continuous learning and improvement - Agentic workflows learn from outcomes. When an action doesn’t produce the desired result, the agent adapts its strategy, making processes more effective over time.
6. Reduced risk and faster issue resolution - In compliance, security, or support settings, agentic workflows can flag anomalies and act immediately, often resolving issues before humans even notice. That means fewer mistakes, faster recoveries, and stronger trust with customers.

What are the top three agentic AI tools on the market to build workflows?

As agentic workflows move from experimental to essential, more platforms are stepping up to help businesses build, launch, and scale them effectively. 

Below are some of the most advanced agentic AI platforms in 2025 for various use cases.

1. Warmly - Advanced GTM agentic workflows for marketing and sales operations

Warmly stands out as one of the most advanced and focused platforms for building agentic workflows tailored to revenue teams. 

Unlike generic AI automation tools, Warmly delivers a full suite of pre-trained, task-specific agents designed for real-world GTM execution, including lead routing, outbound messaging, ad targeting, and real-time enablement.

From marketing ops and demand gen to SDR automation and sales rep support, Warmly’s agents act like autonomous teammates, freeing up human sellers to focus on high-impact conversations while the agents handle everything from first touch to follow-up.

Standout features

• AI Marketing Ops Agent - This agent automates lead scoring and routing by analyzing real-time engagement signals, firmographics, and historical data. It ensures that leads are prioritized and directed to the appropriate sales channels without manual intervention. 
• AI Demand Gen Agent - Warmly's Demand Gen Agent leverages signal-based ad targeting to create highly specific lead segments. It orchestrates personalized follow-up campaigns based on a lead's level of intent, ensuring that prospects receive relevant messaging at the right time. 
• AI SDR Agent - This agent functions as an always-on sales assistant, handling prospecting across numerous accounts. It engages visitors instantly with personalized conversations, nurtures leads through automated email and LinkedIn sequences, and books meetings around the clock. ​
• AI Copilot Agent - Warmly's Copilot Agent integrates seamlessly with tools like LinkedIn, Outreach, and Salesforce, providing AI-driven messaging recommendations and personality profiling. It aids sales representatives by suggesting words, tone, and structure that resonate best with each persona. ​

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:

1. 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.
2. 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.
3. Enterprise: Custom pricing, custom number of visitors, everything in Business, plus custom signals and warm calling.

2. Beam AI - Best for enterprise-grade agentic automation across workflows

Beam AI enables large organizations to deploy agentic workflows across departments from marketing and sales to operations, finance, and customer service. 

Its platform is built around the idea of orchestrating multiple AI agents that can work together autonomously to complete complex tasks, adapt to changing inputs, and improve over time.

Beam AI is especially suited for enterprises looking to move beyond simple automation into truly self-directed systems that reflect, reason, and act across multiple business units.

Standout features

• Multi-agent orchestration - Beam’s platform allows multiple agents to collaborate within a single workflow, handling everything from data analysis to task execution and escalation.
• Contextual reasoning engine - Agents consider historical data, business goals, and user context to make adaptive decisions.
• Enterprise integration layer - Beam connects with CRMs, ERPs, and internal databases, making it easier to embed intelligent workflows directly into enterprise environments without disrupting existing systems.

Pricing

Beam AI has 4 pricing tiers:

1. Agent S: $790/mo, for users with basic needs.
2. Agent M: $2490/mo, designed for more advanced needs and tasks of moderate complexity.
3. Agent L: $3990/mo, most capable option for high complexity workflows.
4. Custom: Custom pricing for bespoke solutions.

Each plan includes unlimited agents.

3. Auxia - Best for hyper-personalized customer journey orchestration

Auxia is an agentic marketing platform designed to help enterprises deliver intelligent, 1:1 customer journeys using AI agents. 

By leveraging first-party data, Auxia enables marketing and product teams to seamlessly orchestrate adaptive, hyper-personalized experiences across various channels, including web, app, email, and SMS. ​

Standout features

• Collects and processes customer data - Analyzes customer data to deliver tailored marketing content across various channels.
• Workflow automation - Streamlines marketing workflows by automating complex tasks, allowing teams to focus on strategic initiatives.
• Continuous optimization - Auxia's agentic copilots create journeys that automatically adapt to real-time user behavior, ensuring that each customer interaction is personalized and optimized for engagement. ​

Pricing

Auxia doesn’t disclose prices.

You’ll have to contact its team for a quote.

What are the current limitations of agentic workflows?

As powerful as agentic workflows are, they’re still a relatively new category, evolving quickly but not without friction. 

While the promise of autonomous, goal-driven systems is real, there are still several limitations businesses should understand before going all in.

1. High setup and integration complexity

Agentic workflows often require stitching together multiple systems, such as CRMs, analytics tools, data warehouses, and communication platforms, and giving agents access to them. 

Without strong internal alignment and solid infrastructure, the initial implementation can be resource-heavy and time-consuming.

2. Limited out-of-the-box flexibility

Unlike traditional automation tools, agentic platforms aren’t always plug-and-play. 

Most require fine-tuning to reflect your goals, edge cases, and operating context. 

This means companies often need to invest in prompt design, feedback loops, and testing cycles to get reliable outputs.

3. Lack of explainability and transparency

When AI agents make autonomous decisions, it’s not always clear why they chose a specific action or what data influenced the outcome. 

This black-box nature makes some teams hesitant to trust agentic systems in customer-facing or regulated environments.

4. Error handling and unpredictability

Because agents adapt based on inputs and outcomes, they can sometimes take unexpected paths, especially if the feedback loop isn’t well defined. 

Without strong boundaries or oversight, this can lead to awkward user experiences or misfired actions (like sending the wrong follow-up to a prospect).

5. Security and data governance concerns

Since agents often require access to sensitive systems and customer data, they introduce new vectors for risk. 

Ensuring compliance with internal security policies, privacy standards (like GDPR), and safe API usage is still a challenge, especially when agents act independently.

Next steps: Putting agentic workflows to work

Instead of stitching together disconnected automations or manually managing every step of a funnel, agentic workflows let you build systems that think, act, and adapt on their own.

The best part is that agents are not here to replace your team - they’re here to improve its efficiency. 

When agents handle the repetitive, reactive, and routine, your team can focus on the strategy, creativity, and conversations that actually drive growth.

If you’re ready to scale smarter and bring AI-powered autonomy into your GTM, Warmly makes it easy to start. 

With a full stack of prebuilt agents for demand gen, outbound, ops, and enablement, you can go from idea to execution without needing a dedicated AI team.

Book a demo and see how Warmly’s agentic workflows can help your marketing and sales teams do more with less.

Read more

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• 8 Examples of AI Marketing Automation in 2025 - Real-world use cases showing how businesses are using AI to automate and scale their marketing in smarter, faster ways.
• Best 10 AI Sales Agents In 2025 [Reviewed] - A deep dive into the top AI sales agents that are reshaping outbound, prospecting, and pipeline management in 2025.
• 30+ Powerful AI Agents Statistics In 2025: Adoption & Insights - Get the latest stats on how AI agents are being used across industries and what that means for your business.
• Are AI Agents Worth It In 2025? Expectations vs. Reality - We separate hype from results in this honest breakdown of what AI agents are really delivering for teams this year.
• 7 Factors To Consider When Buying An AI Sales Agent In 2025 - Before you invest, make sure your AI sales agent checks these critical boxes for ROI, usability, and real impact.
• 10 Best AI Sales Assistants in 2025 [Reviewed] - Meet the AI sales assistants that are helping reps book more meetings, shorten sales cycles, and stay focused on selling.
• 10 Best AI SDR Agents to Win More Deals in 2025 - From email outreach to lead qualification, these AI SDR agents are built to help small teams win bigger, faster.
• 10 Best AI Agents for Digital Marketing in 2025 [Reviewed] - Discover the top AI agents transforming digital marketing in 2025, from campaign automation to content personalization.
• 10 Best AI Agents for Small Businesses in 2025 [Reviewed] - Explore the best AI agents helping small businesses scale operations, boost outreach, and compete smarter in 2025.
• Agentic AI Orchestration: Definitions, Use Cases & Software - A complete guide to agentic AI orchestration: what it is, how it works, and the tools leading the charge in 2025.

Agentic AI for marketing isn’t just another trend. 

It’s a full-on shift in how modern teams operate.

Instead of relying on disconnected tools and one-off automations, agentic AI gives marketers self-directed, always-on assistants that can plan, launch, and optimize campaigns with minimal oversight.

This means we're talking about AI that doesn’t just execute tasks. It thinks, prioritizes, and adapts.

One agent might be analyzing real-time intent data to spin up new campaigns. At the same time, another rewrites underperforming ads on the fly or optimizes email sequences based on open and reply rates.

Exciting, right?

This article breaks down what agentic AI means for marketing teams in 2025, including its use cases and the top software platforms that make it all possible.

If you're a growth-minded team looking to scale smarter and not just harder, keep reading.

How is agentic AI being used in marketing in 2025?

Today, agentic AI has become the engine behind the entire marketing tech stack.

Instead of juggling tools and stitching together workflows, marketers are increasingly relying on autonomous agents to drive outcomes across the entire funnel. 

These agents aren’t waiting for instructions - they’re making decisions, taking action, and learning in real time.

And the shift is massive.

Campaigns that used to take weeks to launch now go live in a day. 

Segmentation and personalization are handled dynamically. 

Performance is optimized continuously - not monthly, not weekly, but minute by minute.

No matter whether you’re running paid media, email nurtures, product-led growth loops, or account-based campaigns, agentic AI is showing up behind the scenes, coordinating execution, testing variations, scoring leads, and triggering follow-ups. 

All without constant hand-holding.

In short, marketing teams are still steering the ship, but agentic AI is now powering the engine room.

What are the benefits of agentic AI for marketing teams?

Agentic AI isn’t just about efficiency.

It’s about unlocking a fundamentally smarter way to market.

Here’s what the best teams are seeing:

1. Massive time savings - With agents owning execution, teams spend less time in the weeds and more time on strategy, positioning, and creative thinking.
2. Faster speed-to-campaign - No more waiting on backlogs or approvals. Agents move from idea to launch in hours, not weeks.
3. More personalized buyer journeys - Agentic AI adapts messages, sequences, and content based on live behavior, so every touchpoint feels tailor-made.
4. Continuous optimization - Agents don’t “set it and forget it.” They’re constantly monitoring performance data and making smart tweaks, so campaigns improve over time.
5. Scalability without increasing headcount - Growth doesn’t have to mean more hires. You can expand efforts, test new markets, or increase output without adding new team members.

This means that agentic AI gives marketing teams the power to do more with fewer manual inputs and way more precision.

Agentic AI vs. Generative AI: What’s the difference for marketers?

Most marketers are already familiar with generative AI, which includes tools like ChatGPT, Jasper, and Copy.ai that help you create content more quickly.

But agentic AI takes things a step further.

Here’s the core difference:

Generative AI is reactive. You prompt it, and it responds. 

It’s a helpful assistant when you need to write a blog post or generate a few ad headlines.

Agentic AI is proactive. It doesn’t wait for instructions - it acts. 

It decides when to launch a campaign, which channels to use, what content to update, and how to optimize based on outcomes.

Think of it this way:

1. Generative AI helps with tasks.
2. Agentic AI owns outcomes, executing and orchestrating workflows from top to bottom.

For marketers, that shift is game-changing. 

Instead of jumping between tools, setting up zaps, or manually tweaking flows, agentic AI handles entire goals - from increasing conversion rates to reducing CAC - while you focus on the bigger picture.

What are the 8 best use cases of agentic AI in marketing?

Agentic AI is primarily about doing smarter marketing, faster.

Top-performing teams today are using agents not just to support their workflows but to run them. 

These aren’t task-takers - they’re autonomous operators, each with a mission, a feedback loop, and a goal they optimize toward.

Let’s walk through 8 of the most powerful use cases where agentic AI is transforming marketing execution, from pipeline generation to performance analytics.

1. Automating full-funnel demand generation

Demand gen has always been a juggling act.

You’ve got to spot the right accounts at the right moment, craft tailored outreach, sync with sales, run paid ads, test offers, and keep your CRM clean without missing a beat. 

But the traditional process is slow, fragmented, and deeply manual.

That’s precisely where agentic AI steps in.

In 2025, top-performing teams are deploying demand gen agents that take full ownership of pipeline creation, doing things like:

• Proactively identifying in-market accounts.
• Launching cross-channel campaigns.
• Running warm offers and outbound.
• Pushing hot leads to sales. 

And Warmly’s AI Demand Gen Agent is built to do just that.

This isn’t a static automation tool - it’s a self-directed agent that actively generates pipeline while your team focuses on strategy. 

Here’s what it does:

1. Watches for in-market buyers constantly - The agent continuously monitors intent signals, account activity, and CRM data to identify high-fit prospects who show buying behavior. 
2. Launches warm outbound sequences automatically - Once it identifies engaged accounts, it kicks off tailored outbound sequences with pre-approved messaging. Each sequence is timed and customized based on persona and stage.
3. Runs targeted ads without human input - The agent sets up and launches paid campaigns to warm up leads, without you needing to log into your ad manager or build custom audiences. It knows who to target and when.
4. Activates warm offers to increase conversion - The agent can launch targeted offers, such as gated content, exclusive webinars, or discounts, based on what it predicts will convert a specific segment or account. 
5. Pushes hot leads to sales automatically - When an account hits a readiness threshold, the agent sends it to the right rep with context (including what they clicked, what offer they engaged with, and what action triggered handoff).

This means no more waiting days (or weeks) for demand gen campaigns to go live. 

The agent monitors, acts, and adapts in real-time, launching outbound paid ads and warm offers across the funnel, and closing the loop with automated sales handoffs.

So instead of your team doing all the heavy lifting to generate pipeline, your AI agent quietly does it for you at scale, 24/7, with zero micromanagement.

2. Lead scoring, routing, and handoff

Getting the right lead to the right rep at the right time sounds simple in theory. 

In reality? It’s one of the biggest sources of lost revenue in B2B marketing.

Lead scoring models become outdated. Routing rules break. Sales gets junk leads or, worse, misses hot ones entirely. 

And when handoffs aren’t timely or contextual, deals die before they ever get a chance to close.

Agentic AI solves this by owning the entire journey from scoring to sales activation.

Instead of relying on static MQL definitions or brittle logic, marketing teams now deploy agents that continuously evaluate leads based on real-time behavior, update their status, and automatically trigger handoffs with full context.

Warmly’s AI Marketing Ops Agent is purpose-built for this kind of intelligent execution. 

Here’s how it works:

1. AI-powered ICP identification - The agent continuously refines and updates your ICP based on real-time data, so you’re always targeting the right buyers, not just the ones you thought were a fit last quarter.
2. Dynamic scoring models - Agentic AI doesn’t rely on a single scoring formula. It continuously adjusts weights based on what converts, assigning more value to meaningful engagement (like pricing page views or high-intent demo requests) and less to vanity metrics (like webinar sign-ups).
3. Real-time signal detection - Agents monitor behavior across every touchpoint, such as website visits, email opens, ad clicks, LinkedIn engagement, webinar attendance, and use that data to detect buying intent faster than humans ever could.
4. Automated lead qualification - Once a lead reaches a defined readiness threshold (based on ICP fit and behavior), the agent automatically qualifies them, saving SDRs from having to comb through cold or misaligned prospects.
5. Intelligent routing to the right rep - The agent uses territory rules, product interest, deal size, and rep availability to assign the lead appropriately, ensuring that follow-up is both fast and personalized.
6. Contextual handoff and notifications - When a lead is handed off, the agent provides sales with all the context: what the prospect viewed, clicked, downloaded, and engaged with, so reps know exactly how to approach.

The result? No more static MQL gates. No more dropped handoffs. Just a seamless flow from interest to impact.

For marketing teams, this means fewer complaints from sales. For sales teams, it means better conversations and more pipeline. And for leadership? Clearer ROI and faster velocity through the funnel.

3. Continuous ad optimization

Paid media has always been high-risk, high-reward, and high-maintenance. 

Between bid management, creative testing, audience segmentation, and pacing budgets, media buyers often end up buried in dashboards, spreadsheets, and A/B tests.

But with agentic AI, paid media can now run itself.

Instead of manually analyzing results or waiting for performance reports, marketing teams are using ad-focused agents that continuously monitor spend, test creative variations, shift budgets across campaigns, and optimize for key outcomes in real-time.

Here are some of the things they can handle:

1. Dynamic bidding and budget allocation - The agent adjusts spend across platforms and campaigns automatically, doubling down on high-performing ads and pulling back on underperformers minute by minute, not week by week.
2. Creative performance testing - Variants are tested in real time across copy, visuals, and CTAs. Once a winner emerges, the agent rolls it out automatically, meaning there’s no need to wait for a human to review the data.
3. Audience segmentation and refinement - Based on conversion data and intent signals, the agent hones in on the most responsive audience segments, building lookalikes and excluding low-intent profiles to improve efficiency.
4. Goal-based optimization - Whether you’re aiming to reduce customer acquisition cost, increase pipeline velocity, or improve lead quality, the agent tunes performance toward that outcome.
5. Cross-channel coordination - Agents can manage spend across Google Ads, LinkedIn, Facebook, and programmatic platforms simultaneously, ensuring consistency and balance across your media mix.

This results in always-on ad campaigns that adapt in real time, without burning out your paid team or relying on lagging metrics.

4. Hyper-personalized email and nurture campaigns

Traditional email marketing often relies on pre-built flows, static segments, and monthly performance reviews. 

Even the most personalized nurtures are usually built with rigid logic: if a contact does X, send Y.

But today, static logic isn’t enough.

With agentic AI, lifecycle campaigns have become dynamic, self-optimizing systems. 

AI agents now manage your nurture programs end-to-end, tailoring sequences on the fly based on real-time engagement, lead behavior, persona, funnel stage, and more.

Here’s how these agents level up email performance:

1. Behavior-driven personalization at scale - Instead of relying solely on firmographics or persona tags, agents dynamically adapt content based on live user behavior (e.g., what pages they’ve visited, emails they’ve opened, forms they’ve filled out, and how recently they’ve engaged).
2. Sequence optimization - If a lead skips a step, opens three emails but doesn’t click, or shows renewed interest after weeks of silence, the agent adjusts the cadence, messaging, or even the channel without any manual input.
3. Dynamic content testing - Agents run A/B (and even A/B/C/D) tests on subject lines, CTAs, layouts, and send times, and then automatically switch to the top performer in each sequence. 
4. Real-time lead prioritization - As a lead moves from cold to warm, or shows signs of urgency, the agent shortens the nurture cycle and surfaces them to sales instantly.
5. Multi-channel escalation - If email alone isn’t working, agents can recommend or trigger next-best actions, such as retargeting ads or a LinkedIn touchpoint, ensuring prospects don’t go cold.

The result is a nurture system that feels more like a living, breathing conversation than a mechanical drip campaign.

5. SEO content optimization and management

SEO has always been a long game, but it’s also a never-ending one.

Keeping content fresh, rankings high, and traffic consistent takes constant upkeep, such as:

• Re-optimizing old pages.
• Monitoring keyword shifts.
• Updating internal links.
• Auditing site structure.
• Watching what competitors are doing.

Most content teams don’t have time to do all that. 

Which is why agentic AI is quietly becoming their most valuable player.

Nowadays, AI agents have gone far beyond just basic blog writing. 

They can act as autonomous SEO managers, auditing your content portfolio, identifying at-risk rankings, suggesting updates, and even automatically rewriting or republishing pages.

Here’s how agentic AI transforms SEO from a reactive grind into a proactive growth engine:

1. Content performance monitoring - Agents track your top-performing and underperforming content in real-time, flagging pages that are dropping in rank, losing traffic, or slipping behind competitors.
2. Smart refresh recommendations - When a page starts to stagnate, the agent identifies what’s missing (such as updated stats, a stronger H1, or new keyword variations) and provides clear next steps or executes them directly.
3. On-page optimization - Agents can automatically adjust meta tags, headings, internal links, and image alt text according to SEO best practices, improving crawlability and user experience without requiring manual edits.
4. Keyword and topic expansion - Using intent data and SERP analysis, the agent surfaces new topic clusters and keyword opportunities aligned with your ICP and maps them to your existing content or suggests new articles.

This way, instead of scrambling to “do SEO” once rankings drop, you’ve got an always-on system watching your site, flagging risks, and fixing them before they cost you traffic.

For lean teams, it’s the difference between maintaining SEO as a side hustle and running it like a growth channel.

6. Marketing analytics and reporting

For all the tools we have, marketing reporting is still painfully manual for most teams.

You log into half a dozen platforms, pull exports, double-check attribution, chase missing UTMs, and spend hours building slide decks, only to realize you’re already behind on next week’s campaign.

AI marketing agents change that by monitoring campaign performance, surfacing insights, and suggesting actions in real-time.

Here’s how they do it:

1. Live KPI monitoring - Agents track key performance indicators continuously across tools (e.g., site traffic, ad spend, conversions, pipeline contribution, channel performance) and alert teams when something spikes or slips.
2. Anomaly detection - Sudden drop in leads from a paid channel? Massive increase in demo bookings from a new page? The agent flags it immediately, so you can act now, not after the damage is done.
3. Attribution insights and fixes - Agents don’t just pull first-touch and last-touch reports; they spot gaps in attribution data (e.g., missing UTM tags, broken workflows) and either correct them or flag them to be fixed.
4. Automated report generation - Whether it’s a weekly campaign summary or a board-ready MQL → SQL funnel snapshot, the agent builds and updates reports for you, pulling in clean, up-to-date data every time.
5. Multi-source consolidation - Agents connect data across tools, such as CRM, MAP, ad platforms, and web analytics, giving you a holistic view of performance without stitching anything together manually.

This way, instead of reacting to lagging metrics, marketing teams get a live, intelligent pulse on what’s happening, and a trusted AI teammate that translates performance into action at speed.

The result? 

Fewer reporting cycles, faster decision-making, and a team that’s always one step ahead of what the data’s trying to say.

7. Marketing campaign orchestration

Campaign planning is usually a bottleneck. 

You’ve got to choose the right audience, segment messaging, map touchpoints, align stakeholders, and then actually launch - and that’s all before optimization even begins.

With agentic AI, campaign orchestration becomes autonomous, thanks to AI agents that tackle:

1. End-to-end campaign setup - Agents take your inputs (goal, persona, and offer) and turn them into live campaigns across email, ads, and outbound channels, automatically configuring settings like cadence and messaging variations.
2. Channel coordination - Instead of siloed campaigns on email and paid, the agent sequences everything in harmony, ensuring that the right message hits the right prospect on the right platform, in the correct order.
3. Real-time response adjustments - As performance data comes in, the agent dynamically tweaks campaigns, pausing low performers, increasing frequency for engaged segments, or escalating hot leads to sales.

With campaign orchestration handled by an intelligent agent, marketing teams spend less time managing logistics and more time shaping the marketing strategy behind the work.

8. Experimentation and insights

Marketers know testing is key, but in reality, experimentation often gets deprioritized. 

It’s time-consuming to set up, hard to track, and even harder to interpret at scale. 

Most teams end up testing one thing at a time, very slowly.

Today’s agents don’t just execute campaigns - they experiment by default. 

They’re constantly testing subject lines, offers, formats, timing, and audience segments. 

And unlike humans, they never forget to document their results or apply what they've learned.

Here’s what they enable:

1. Multi-variant testing at scale - Instead of testing A vs. B, agents can test multiple variables simultaneously across audiences, then adapt in real time based on what’s working best.
2. Hypothesis generation - Agents don’t just run random tests. They use behavioral and performance data to generate hypotheses about what might improve performance and then test them autonomously.
3. Always-on optimization - Every touchpoint, such as email, ad, landing page, etc., is treated as testable. Agents learn from every interaction and continually refine their approach based on the data.
4. Insight delivery to humans - While agents act independently, they also report back. You get visibility into what’s working, what changed, and why without having to dig through dashboards.

The result is a culture of automated experimentation where testing happens in the background, outcomes improve over time, and your team gets smarter with every interaction.

What are the 3 best agentic AI tools for marketing teams on the market?

The agentic AI space is evolving fast, but not all AI tools are created equal.

Some platforms offer surface-level marketing automation. 

Others give you an AI writing assistant dressed up as “agentic.” 

But the real value lies in tools that deliver autonomous, outcome-focused agents that understand context, act independently, and integrate across your GTM stack.

Here are 3 of the best agentic AI tools marketing teams are using today, starting with the most complete solution on the market.

1. Warmly - Best for full-funnel GTM orchestration

Warmly is a leading agentic AI platform designed to streamline marketing operations and enhance GTM strategies. 

Its suite of AI agents empowers marketing teams to automate complex tasks, personalize outreach, and optimize campaign performance across multiple channels.​

Standout features

• AI Demand Gen Agent - Automatically launches multi-channel campaigns (including paid ads and email) based on real-time buying signals, and serves up personalized offers on your website to convert visitors.
• AI Marketing Ops Agent - Monitors real-time data signals to enrich lead profiles, ensuring accurate ICP identification, and continuously updates the ICP to keep it fresh and relevant. It also manages lead routing and audience segmentation, enabling targeted advertising and efficient follow-up strategies.​
• Tracks intent signals - The platform’s agents identify website visitors and use intent signals to help uncover those most likely to convert right now.
• Live Video Chat - Human reps can hop on a video call with high-value leads straight from Warmly’s dashboard.
• AI Chat - AI-powered chatbot that engages, qualifies, and routes leads, as well as books meetings with them.

By integrating these agents and capabilities, Warmly offers a cohesive solution that enhances marketing efficiency, improves lead quality, and drives revenue growth. 

Its focus on AI marketing automation and personalization makes it an invaluable tool for modern marketing teams aiming to scale their efforts effectively.

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:

1. 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.
2. 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.
3. Enterprise: Custom pricing, custom number of visitors, everything in Business, plus custom signals and warm calling.

2. Omneky - Best for autonomous ad generation

Omneky leverages AI to generate and optimize ad creatives across platforms, enabling marketers to scale personalized advertising efforts.​

As such, Omneky's platform empowers marketing teams to maintain a consistent brand voice while rapidly producing and deploying high-performing ad content.

Standout features

• Creative Generation Pro - Automatically produces a variety of ad creatives tailored to different audiences and platforms.​
• Product Generation Pro - Generates hyper-realistic product images without the need for traditional photo shoots.​
• Smart Ads - Utilizes AI to autonomously generate and launch on-brand ads across multiple channels.​

Pricing

Omneky has three pricing plans:

1. Product Generation Pro: $25/mo, provides access to basic AI features and lets you have one brand.
2. Creative Generation Pro: $99/mo, all Product Generation Pro features, plus AI creative insights and recommendations, brand voice consistency, etc.
3. Enterprise: Custom pricing, everything in Creative Generation Pro, plus more advanced analytics and more refined customization options.

3. Salesforce Agentforce - Best for creating bespoke agents 

Salesforce Agentforce introduces AI agents into the Salesforce ecosystem, enhancing productivity by automating complex tasks within sales, marketing, and customer service domains.​

Agentforce enables large organizations to scale their operations efficiently by incorporating AI agents in their workflows, reducing the need for direct human supervision in routine tasks.

Standout features

• Pre-built agents - It features ready-made agents for marketing campaign orchestration, personalized product recommendations, and prospect engagement and nurturing to help you get started.
• Powered by Atlas Reasoning Engine - This mimics human thought processes, helping agents plan and execute tasks autonomously.​
• Seamless integration - Works within the existing Salesforce environment, allowing for smooth adoption and operation.​

Pricing

Salesforce Agentforce offers a free plan for users of the Salesforce Enterprise plan and above (starting at €165 per user per month) that includes 1,000 Agentforce conversations and leads, as well as 250,000 Data Cloud credits.

Users of other plans, or those who want to create bespoke agents, will pay €2 per conversation.

What are some of the risks that marketers face when trusting agentic AI?

Agentic AI opens up a world of possibilities for marketing teams, such as faster execution, smarter targeting, and unprecedented scale. 

But handing over decision-making power to autonomous agents isn’t without its risks.

Marketers need to be aware of the tradeoffs and potential pitfalls that come with delegating real control to AI, especially in high-stakes, brand-facing environments.

Here are some of the key risks to watch out for:

1. Loss of oversight and unintended outcomes

When agents operate independently, they can take actions that go unnoticed until it’s too late, like launching a campaign with flawed logic, promoting the wrong offer, or targeting an irrelevant segment. 

Without proper controls or guardrails, even a well-meaning agent can derail a strategy.

2. Over-personalization or tone mismatch

Agents may personalize too aggressively or misinterpret tone, especially in outbound messages. 

A subject line that seems clever to the AI could come across as off-brand or even offensive to recipients. 

Maintaining human-level nuance is still a challenge for many systems.

3. Data bias and faulty decision-making

Agentic systems rely heavily on historical data. 

If that data reflects bias (e.g., favoring certain personas, industries, or channels), the agent may reinforce poor targeting or exclusionary practices, leading to missed opportunities or even reputational harm.

4. Feedback loops that over-optimize the wrong metrics

Without human judgment, agents can fall into feedback loops, optimizing for opens when the goal is revenue, or focusing on volume over quality. 

If you don’t explicitly define success, the AI might chase the wrong outcomes.

5. Security and compliance vulnerabilities

Agents that access and act on customer data must be closely monitored and controlled. 

Mishandled data, GDPR violations, or accidental exposure of sensitive information can pose major legal and reputational risks.

6. Dependency without understanding

Over time, teams may grow overly reliant on AI without fully understanding how decisions are being made. 

This "black box" problem can reduce agility and make it hard to pivot strategies when things stop working.

How to mitigate the risks?

Agentic AI is incredibly powerful - but it’s not plug-and-play magic. 

When marketers stay informed, set the right boundaries, and keep a human in the loop, the risks become manageable, and the upside is enormous.

Here’s how to achieve this:

• Set clear goals and constraints for each agent.
• Build in human approval points for sensitive actions.
• Use explainable AI wherever possible to audit decision logic.
• Regularly review outputs and campaign performance.
• Combine agentic execution with human strategy and oversight.

Next steps: Bring agentic AI into your marketing stack

Agentic AI isn’t a futuristic concept anymore - it’s a practical, high-impact tool that’s already reshaping how modern marketing teams operate. 

From full-funnel demand generation to hyper-personalized nurture flows, campaign orchestration, and intelligent ops automation, agents aren’t just assisting marketers.

Today, they’re running the playbook.

But as with any powerful technology, success comes down to choosing the right system.

If you're looking for a platform that doesn't just talk about agentic AI but delivers real, self-directed execution across your entire GTM strategy, Warmly is your answer. 

With agents purpose-built for demand gen, marketing ops, and revenue teams, Warmly lets you automate smarter, respond faster, and scale without sacrificing control or context.

Ready to bring agentic AI into your marketing team’s day-to-day?

Book a demo with Warmly and see how our agents can help you generate more pipeline, personalize at scale, and operate like a modern GTM machine.

Read more

• Agentic AI Orchestration: Definitions, Use Cases & Software - Discover what agentic AI orchestration really means, how it works across GTM functions, and which tools are leading the charge in 2025.
• 10 Best AI Email Agents in 2025 to Drive Pipeline [Reviewed] - Explore the top AI email agents that automate outreach, personalize at scale, and help your team convert more leads faster.
• 30+ Powerful AI Agents Statistics In 2025: Adoption & Insights - A data-driven look at how AI agents are being adopted, used, and delivering value across sales and marketing in 2025.
• Are AI Agents Worth It In 2025? Expectations vs. Reality - We separate hype from real-world results with an honest breakdown of what AI agents actually deliver—and where they still fall short.
• 7 Factors To Consider When Buying An AI Sales Agent In 2025 - Before you commit to an AI sales agent, here are the key features, risks, and differentiators you need to evaluate.
• AI Marketing Agents: Use Cases and Top Tools for 2025 - Get a deep dive into how AI marketing agents work, what they can automate, and which platforms are worth your attention.
• AI GTM: Top Use Cases, Software, & Examples [2025] - Learn how agentic AI is transforming go-to-market strategies across marketing, sales, and customer success in 2025.
• AI Marketing Strategy: Top Use Cases and Tools - Uncover the smartest ways to integrate AI into your marketing strategy with real use cases, tool comparisons, and next steps.
• 10 Best AI Agents for Digital Marketing in 2025 [Reviewed] - From campaign orchestration to creative testing, meet the AI agents redefining how digital marketing gets done.
• 8 Examples of AI Marketing Automation in 2025 - See how leading teams are using AI to automate content, campaigns, and customer journeys with real examples from the field.

AI agents for digital marketing are changing the game in 2025, not by replacing marketers, but by amplifying what’s possible, helping you move faster, think bigger, and scale smarter.

But let’s be honest: the AI agent space is growing fast, and not all tools are created equal. 

Some are just smarter - more adaptable, more collaborative, more in tune with how real marketing teams actually work.

In this guide, I’ll break down the 10 best AI agents for digital marketing in 2025, ensuring you’ll find the right fit for any use case, from ad optimization and content management to better lead qualification and beyond.

Let’s begin!

Factors to Consider When Choosing an AI Agent for Digital Marketing

Before diving headfirst into a new platform, it’s worth zooming out and asking the big question: 

Will this AI agent actually improve how my team works, or just add more noise?

Here are the key factors to consider before choosing your digital sidekick.

1. Strategic Fit with Your Marketing Operations

A great AI agent doesn’t just automate tasks.

It aligns completely with how your team operates. 

Whether you’re running inbound or outbound campaigns or full-funnel demand gen, the right agent should plug into your workflow naturally, not force you to change it.

Ask: Does it support the channels we use? Can it adapt to our current tech stack? Does it feel like a fit for how we market?

2. Level of Autonomy vs Control

Some AI agents handle tasks independently. 

Others act more like copilots, offering suggestions but leaving the final decision to you. 

There’s no right or wrong here, but it depends on your team’s comfort level with delegation.

If you want speed and scale, lean autonomous. And if your brand voice is sacred, opt for one with stronger oversight and editing features.

3. Personalization and Learning Ability

Modern marketing isn’t one-size-fits-all. 

Your AI agent should get smarter over time, learning from past campaigns, adapting to audience signals, and personalizing outreach based on behavior, not just segments.

Look for tools that go beyond templates and improve with use. 

(Bonus points for those that adjust based on individual personas or buying stages.)

4. Integration and Ease of Setup

No one wants another tool that takes three quarters to implement. 

The best AI agents are plug-and-play, integrate cleanly with your CRM and marketing tools, and can start showing value quickly.

Check: Does it integrate with your existing ecosystem (e.g., HubSpot, Salesforce, LinkedIn, ad platforms), and how long will onboarding take?

5. Visibility and Reporting

You can’t improve what you can’t measure. 

Your AI agent should provide transparent metrics so you can see what’s working - and what isn’t. 

Think real-time dashboards, engagement insights, attribution, and ROI breakdowns.

Great reporting isn’t just for bragging, as it helps you prove impact, optimize faster, and build internal buy-in.

6. Scalability and Cost-Effectiveness

An AI agent should grow with your team, not limit it. 

Look at how the pricing scales, whether usage caps apply, and if you’ll need to constantly upgrade plans just to stay effective.

And of course, does the ROI justify the cost? 

Compare it to what you’d spend on hiring or outsourcing the same tasks.

What Are the Best 10 AI Agents for Digital Marketing in 2025?

1. Warmly - Your full-funnel GTM copilot, built to turn buyer signals into action across channels, from instant outbound to real-time ad targeting and email nurture.
2. Jasper - AI-powered content creation engine that helps marketers scale high-converting copy, blog posts, and ads faster and on-brand.
3. Smartly.io - Paid media automation agent that optimizes creative, bidding, and placement across Meta, TikTok, and more, in real time.
4. Mutiny - Web personalization agent that dynamically adjusts messaging and layouts on your site based on who’s visiting without needing dev help.
5. Seventh Sense - Email optimization agent that pinpoints the exact time when each contact is most likely to engage, boosting deliverability and open rates.
6. Ocoya - Social media scheduling + copywriting agent that creates, automates, and publishes content across platforms with built-in performance predictions.
7. Drift - Conversational AI agent that qualifies leads and books meetings via chatbot, turning your website into a 24/7 inbound engine.
8. HubSpot Breeze - End-to-end marketing automation suite embedded in HubSpot, combining content creation, lead engagement, customer support, and CRM intelligence into one powerful AI assistant.
9. Lavender - Email copilot that helps you write better cold emails by scoring drafts, improving tone, and suggesting edits that boost reply rates.
10. Opal - An integrated marketing strategy assistant that helps marketers plan, create, personalize, and experiment

1. Warmly

Best for: Automating digital campaign workflows and accelerating GTM execution by identifying and engaging your hottest leads in real-time.

Who is it for: B2B marketing and revenue teams who want to scale outreach, ads, and ops without adding headcount.

Warmly is a GTM AI platform that builds fully autonomous agents to help revenue teams move faster, no matter the channel. 

Its AI Marketing Ops Agent is purpose-built for digital marketing execution, helping teams coordinate across tools, capture buyer intent signals, and turn strategy into live campaigns with zero busywork.

How Does Warmly's AI Agent for Digital Marketing Work?

Warmly’s AI Marketing Ops Agent is like a high-performing digital teammate that automatically turns buyer signals into coordinated marketing actions across your stack in real-time.

At its core, the agent continuously monitors your funnel for activity that matters: from new site visitors and job changes to pipeline shifts and firmographic matches. 

It doesn’t just alert you - it acts for you.

Here’s how it works step-by-step:

• AI-powered ICP identification - Warmly’s agent automatically builds and refines your ICP using live data. It tracks patterns in your pipeline, spotting which industries, roles, and behaviors are most likely to convert.

• Campaign triggers based on live signals - When an account matches your ICP - or a key action is taken (like hitting your pricing page or engaging with an ad) - the agent springs into action. You can define what counts as a trigger (such as intent, engagement, or funnel movement), and the agent builds its play around it.
• Multi-channel campaign launch - The moment a trigger is hit, the agent launches coordinated campaigns across your channels, such as retargeting ads on LinkedIn and Meta, sending personalized nurture emails or LinkedIn DMs and connection requests.

• Smart sequencing with fallback logic - The agent intelligently sequences actions, waiting for engagement, reacting to opens or clicks, and adjusting next steps based on real-time behavior. 
• Lead routing and notifications - Automatically puts a human in the loop at precisely the right time, ensuring that human reps handle the highest-value leads.

• Always-on optimization - The agent learns over time, tracking what’s working across channels, personas, and segments, and refines its actions accordingly. 

Other Key Features

• Identifies website visitors in real-time - Know exactly who’s visiting your website right now.
• Reveals your hottest leads - Warmly leverages intent data to pinpoint which of your website visitors are most likely to convert.
• Multi-channel execution - Launches ads, emails, Slack alerts, CRM updates, and more from a single trigger.
• Signal-based actions - Tracks buyer intent and behavior to personalize and prioritize campaigns.
• No-code setup - Configure goals and workflows with simple prompts, no devs needed.
• Native integrations - Seamlessly plugs into HubSpot, Salesforce, LinkedIn Ads, Outreach, Slack, and more.
• Live Video Chat - Human SDRs can engage hot leads with direct face-to-face meetings from Warmly’s dashboard.
• AI Chat - AI-driven chatbot that automatically engages leads, qualifies them, routes them to SDRs, and books meetings.

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:

1. 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.
2. 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.
3. Enterprise: Custom pricing, custom number of visitors, everything in Business, plus custom signals and warm calling.

Pros & Cons

✅ Automates high-impact marketing tasks across your stack.

✅ Adapts to real-time buyer behavior, not just static segments.

✅ Helps lean teams scale like a bigger one without hiring.

✅ Connects demand gen, ops, and sales in one loop.

✅ No-code, intuitive UI that’s quick to implement.

❌ Access to Warmly’s AI agents is available only on its paid plans.

2. Jasper

Best for: Scaling high-quality marketing content fast without burning out your team.

Who is it for: Content marketers, copywriters, and marketing teams that need to produce SEO-optimized blogs, landing pages, ad copy, and more at scale.

Jasper is an AI writing platform explicitly trained for marketers. 

It helps teams generate on-brand content quickly, using pre-built templates, brand voice training, and campaign-specific prompts to support everything from blog posts to performance ads.

Key Features

• Brand voice memory - Jasper learns your unique tone and style, then mimics it across different content types.
• SEO mode - Integrated Surfer SEO support helps optimize content while writing.
• Multilingual support - Create content in over 30 languages to support global campaigns.

Pricing

Jasper.ai has 3 pricing plans:

1. Creator: $49 month/seat (1 user only)
2. Pro: $69 month/seat (up to 5 users)
3. Business: Custom pricing

Pros & Cons

✅ Customizable voice ensures brand consistency.

✅ Great for scaling copy across paid + organic channels.

❌ Requires human editing to avoid repetition or generic phrasing.

3. Smartly.io

Best for: Automating and optimizing paid media campaigns across social platforms.

Who is it for: Performance marketers and growth teams running multi-platform ad campaigns on Meta, TikTok, Pinterest, Snapchat, and more.

Smartly.io is an AI-powered ad automation platform that helps teams launch, test, and optimize creative at scale. 

It streamlines everything from asset production to bidding strategy, so you can maximize ROAS without having to babysit every campaign.

Key Features

• Creative automation - Build thousands of ad variations with dynamic creative templates.
• Cross-platform campaign management - Launch and manage ads across multiple channels from a single dashboard.
• AI-based budget optimization - Automatically shifts spend toward top-performing campaigns.

Pricing

Smartly.io doesn’t publish its prices.

Contact its team to get more details on its pricing.

Pros & Cons

✅ Built-in testing tools improve creative performance.

✅ Real-time data helps cut waste and optimize budgets.

❌ Better suited for larger ad budgets and more mature teams.

4. Mutiny

Best for: Personalizing website experiences for different personas and industries without dev resources.

Who is it for: B2B marketing teams, growth leaders, and demand gen pros looking to improve on-site conversion rates.

Mutiny uses AI to personalize your website for every visitor based on firmographics, behavior, and funnel stage. 

There’s no coding required, as you just get smarter messaging, better UX, and more pipeline from the traffic you already have.

Key Features

• Dynamic content blocks - Personalizes headlines, CTAs, and visuals based on industry, role, or account.
• AI-powered account research - Collects relevant information on important accounts in seconds, compiling them in comprehensive profiles.
• A/B and multivariate testing - Lets you test and optimize variations to find what resonates best.

Pricing

Mutiny also doesn’t disclose prices.

You’ll have to book a demo or contact its team to get more details.

Pros & Cons

✅ Super intuitive UI with no need for engineering help.

✅ Helps personalize your website down to the individual account level.

❌ Higher price point, especially for early-stage startups.

5. Seventh Sense

Best for: Optimizing email send times for maximum opens, clicks, and engagement.

Who is it for: Email marketers, demand gen teams, and revenue leaders using HubSpot or Marketo who want to get more mileage out of their existing email lists.

Seventh Sense is an AI-powered platform that analyzes individual user behavior to send emails at the exact right time for each contact.

This improves deliverability, boosts open rates, and makes every email count more, making it perfect for anyone who wants to incorporate AI in their email marketing campaigns.

Key Features

• Send time personalization - Analyzes engagement patterns to send emails when they’re most likely to be seen.
• Deliverability optimization - Avoids bulk sends by throttling them, which minimizes bounce and spam risks.
• Engagement analytics - Tracks opens, clicks, and reply patterns to continuously improve timing.

Pricing

Seventh Sense has two separate pricing packages for each of the two platforms it integrates with:

HubSpot:

• Business Plan: Starts at $80/mo for up to 5000 leads, billed annually.
• Enterprise Plan: Custom price, includes all features as Business with the addition of enterprise-grade security capabilities.

Marketo:

• Business: Starts at $450/mo for up to 5000 leads, billed annually.
• Enterprise: Custom price, includes all features as Business with the addition of enterprise-grade security capabilities.

Pros & Cons

✅ Boosts email performance without changing copy or design.

✅ Helps protect domain reputation and reduce churn.

❌ Only supports HubSpot and Marketo (not other tools like Mailchimp or Pardot).

6. Ocoya

Best for: Creating, scheduling, and optimizing social media content with AI-powered assistance.

Who is it for: Social media managers, content marketers, and solopreneurs who want to scale their online presence without spending hours writing and scheduling posts.

Ocoya combines AI-generated copywriting with social media automation, helping you brainstorm, write, schedule, and analyze posts across multiple platforms.

This means you can handle the entire social media marketing process from a single platform instead of needing a separate marketing tool for each part of the process.

Key Features

• Performance predictions - AI suggests the best times to post and which content is likely to perform well.
• Social media scheduler - Plan and schedule posts across Instagram, LinkedIn, Twitter, Facebook, and more.
• Visual editor - Built-in Canva-style design tools to pair posts with branded visuals.

Pricing

Ocoya has four pricing plans:

1. Bronze: $15/month, includes 1 user, 5 social profiles, and 100 AI credits.
2. Silver: $49/month, includes 5 users, 20 social profiles, 500 AI credits, etc.
3. Gold: $99/month, includes 20 users, 50 social profiles, 1,500 AI credits.
4. Diamond: $199/month, includes 50 users, 150 social profiles, unlimited AI credits, advanced analytics, etc.

Pros & Cons

✅ All-in-one tool for social media content creation and scheduling.

✅ Fast, intuitive, and affordable, which is why it’s great for small teams.

❌ Not ideal for large enterprise teams needing advanced analytics.

7. Drift

Best for: Turning website traffic into a qualified pipeline through AI-powered conversations.

Who is it for: B2B marketing and sales teams looking to capture, qualify, and convert inbound interest in real time, especially on high-intent pages like pricing or features.

Drift is a conversational marketing platform that uses AI chatbots and live chat to qualify visitors, route them to the right rep, and book meetings instantly. 

It helps revenue teams engage prospects at the moment of intent, so you’re never missing a hot lead.

Note: Drift was recently acquired by Salesloft.

Key Features

• Trainable AI chatbots - Engage visitors automatically and guide them through custom playbooks.
• Lead qualification & routing – Scores leads and connects them with the right rep or calendar.
• Sales alerts - Notify reps in real time when target accounts visit your site.

Pricing

There’s no pricing information available for Drift.

You’ll have to contact Salesloft’s sales team for more details.

Pros & Cons

✅ Captures and converts inbound interest in real-time.

✅ Automates lead qualification to reduce manual work for reps.

❌ Pricing may be high for startups or SMBs.

8. HubSpot Breeze

Best for: All-in-one AI marketing automation across channels within the HubSpot ecosystem.

Who is it for: Small to mid-sized businesses and startups using HubSpot, seeking to scale GTM operations without increasing headcount​.

Breeze AI is HubSpot’s native suite of AI tools designed to automate and enhance sales, marketing, and customer service workflows. 

It integrates seamlessly with HubSpot's CRM, offering intelligent agents, data enrichment, and a conversational assistant to streamline operations and drive growth. ​

Key Features

• Breeze Copilot - An AI-powered assistant embedded across HubSpot, aiding in content creation, CRM updates, and task management through a conversational interface. ​
• Breeze Intelligence - Enhances CRM records with enriched data, assesses buyer intent, and streamlines forms by pre-filling known information. ​
• Breeze Agents - Specialized AI agents for distinct functions, including content, social media management, sales prospecting, etc.

Pricing

Two HubSpot pricing plans include Breeze:

1. Marketing Hub Professional: Starts at €880/mo, includes 3 seats (additional seats start at €49), 2,000 contacts.
2. Marketing Hub Enterprise: Starts at €3,300/mo, includes 5 seats (additional seats start at €75/mo), 10,000 contacts.

Pros & Cons

✅ Seamless integration with HubSpot's CRM and tools.

✅ Automates a wide range of tasks across departments.

❌ Currently in beta; some functionalities might be limited.

9. Lavender

Best for: Writing cold emails that get responses—with real-time coaching and personalization suggestions.

Who is it for: SDRs, AEs, founders, and anyone doing outbound email who wants to improve reply rates without hiring a copywriter.

Lavender is an AI-powered email assistant that helps you write better cold emails faster. 

It scores your writing in real time, gives instant feedback on tone and clarity, and suggests edits to improve personalization, relevance, and response likelihood.

Key Features

• Email scoring - Analyzes subject lines, body copy, and CTAs with a real-time engagement score.
• Personalization assistant - Suggests ways to tailor your message based on LinkedIn profiles and company info.
• Tone and clarity coaching - Highlights overly formal, vague, or robotic phrasing and recommends improvements.

Pricing

Lavender offers a free forever plan that allows you to analyze and personalize up to 5 emails per month, providing access to its essential features.

If you need more, there are three paid plans to choose from:

1. Starter: $29/mo, includes unlimited email personalization and generation in addition to AI email writer, mobile optimization, etc.
2. Individual Pro: $49/mo, everything in Starter, plus dedicated support and more native integrations.
3. Team Plan: $99/user/mo, includes aggregated email analytics, accelerated dynamic scoring, content studio access, and more.

Pros & Cons

✅ Instant coaching helps reps level up their writing fast.

✅ Improves personalization at scale without losing speed.

❌ Heavily focused on 1:1 emails, meaning it’s less useful for bulk sends like newsletters.

10. Opal

Best for: End-to-end AI support for content creation, personalization, and experimentation within the Optimizely ecosystem​.

Who is it for: Marketing teams and digital experience professionals using Optimizely One, aiming to enhance efficiency, creativity, and performance across campaigns​.

Opal is Optimizely’s integrated AI assistant that streamlines the entire marketing lifecycle from ideation and content creation to personalization and experimentation. 

Embedded within the Optimizely One platform, Opal provides intelligent suggestions, automates tasks, and delivers actionable insights to optimize marketing efforts. 

Key Features

• Campaign ideation - Generates campaign briefs by analyzing past performance data, helping marketers initiate creative campaigns swiftly. 
• Content and asset generation - Creates campaign-specific assets, including images and social posts, using context from briefs, accelerating content creation workflows. ​
• Personalization - Applies user-level attributes and real-time behavior to dynamically personalize experiences for every visitor. ​

Pricing

Optimizely doesn’t disclose prices for its AI agent.

You’ll have to contact its sales for a custom quote.

Pros & Cons

✅ Enhances personalization and customer engagement​.

✅ Accelerates content creation and experimentation processes​.

❌ Primarily beneficial for users already within the Optimizely ecosystem​.

Next Steps: Meet Your New Digital Teammate

AI agents for digital marketing are already reshaping how modern teams operate. 

However, the best ones don’t just plug into your tools. Instead, they plug into your strategy. 

They understand timing, buyer intent, and your team’s actual motion. 

They don’t just automate tasks - they help your team work smarter, move faster, and focus on what actually drives growth.

That’s where Warmly stands out.

It’s not another point solution or tool you have to supervise constantly. 

It’s a full-funnel copilot designed to identify, engage, and convert your best leads automatically across ads, email, chat, and more. 

And it plays really well with the rest of your stack.

Curious to see how it works in action?

Book a demo with Warmly and meet the AI agent that’s already helping B2B teams scale smarter in 2025.

Read More

• AI Marketing Agents: Use Cases and Top Tools for 2025 - Explore how AI marketing agents are being used across content, ads, and lead gen, plus the top tools leading the charge.
• AI Marketing Strategy: Top Use Cases and Tools - A practical guide to building a winning AI-powered marketing strategy with real-world use cases and tools to match.
• 30+ Powerful AI Agents Statistics In 2025: Adoption & Insights - Get the latest data on AI agent adoption, performance impact, and how top teams are putting them to work.
• Are AI Agents Worth It In 2025? Expectations vs. Reality - We cut through the hype and break down what AI agents are actually delivering for modern sales and marketing teams.
• 7 Factors To Consider When Buying An AI Sales Agent In 2025 - Before you buy, here are the seven things you must evaluate to choose the right AI sales agent for your strategy.
• 10 Best AI Sales Assistants in 2025 [Reviewed] - Our expert breakdown of the most effective AI sales assistants this year, including what they do, who they’re for, and why they work.
• 10 Best AI SDR Agents to Win More Deals in 2025 - Meet the AI SDRs helping reps book more meetings, qualify leads faster, and stay ahead in the modern sales game.

In 2025, AI agents for small businesses are no longer a luxury.

Today, they’re a game-changer for teams looking to grow faster, work smarter, and stay lean. 

If you’re drowning in customer requests, chasing leads manually, or burning hours on routine tasks, AI agents can step in to lighten the load, all while keeping that human, high-touch experience your customers expect.

But here’s the catch: not all AI agents are built with small teams in mind. 

Some are overkill. Others are too rigid, too expensive, or simply not made for how your team works.

That’s why I put together this guide. 

I’m spotlighting the 10 best AI agents for small businesses, featuring tools that are useful, cost-effective, and ready to plug into your day-to-day operations. 

Let’s dive into the tools that punch way above their weight and can give your business a serious edge in 2025.

Factors to Consider When Choosing an AI Agent for Your Small Business

Not all AI agents are created equal, especially when it comes to small businesses. 

The right one can save you hours, boost productivity, and unlock new levels of growth. 

But the wrong one can easily turn into yet another tool to manage, patch, or eventually abandon.

Before you commit, it’s worth taking a step back to ask: Will this actually help my team work better?

Here are the key factors to keep in mind when choosing an AI agent that fits your business, your budget, and your way of working.

1. Alignment with Your Workflows

The best AI agents don’t reinvent how you work - they simply enhance it. 

Look for tools that integrate seamlessly with your existing systems (like your CRM, email, or help desk), support your current workflows, and don’t require a total overhaul to get started. 

Bonus points if the tool adapts to you, rather than the other way around.

2. Ease of Use (and Setup)

Small teams don’t have time for complex setups or steep learning curves. 

Prioritize AI agents that are plug-and-play or require minimal onboarding. 

A great UI, clear documentation, and helpful onboarding can be the difference between adoption and abandonment.

3. Pricing that Scales with You

Some AI tools look affordable enough until you scale. 

Choose AI agents with transparent, SMB-friendly pricing that grows with your business, not against it. 

Avoid platforms that lock core features behind enterprise paywalls or charge extra for every little add-on.

4. Real Results, Not Just Hype

AI is a buzzword magnet. 

Cut through the noise by focusing on outcomes. 

Look for case studies, testimonials, or at least strong use-case examples that show how the agent saves time, increases conversions, or boosts efficiency in real-world small business settings.

5. Customization and Control

No two small businesses are alike. 

Choose AI agents that let you tailor responses, workflows, or logic to fit your audience and voice. 

Whether it’s tone of voice in marketing emails or the flow of a customer support conversation, the best tools give you options without needing to code.

6. Human Handoff and Fail-Safes

Even the smartest AI agent won’t get it right 100% of the time. 

Make sure the tool you choose has smooth fallback systems, such as human handoffs, alerts, or review flows, so you stay in control of customer interactions and critical decisions.

What Are the 10 Best AI Agents for Small Businesses in 2025?

1. Warmly - Your full-funnel sales and marketing copilot, built to turn buyer signals into action across channels, from instant outbound to real-time ad targeting and email nurture.
2. Tidio - AI-powered live chat and helpdesk agent that automates support, captures leads, and engages customers instantly across web and messaging platforms.
3. Jasper - AI content creation engine that helps marketers and small teams generate on-brand copy for blogs, emails, and ads — fast.
4. Pictory - Video creation agent that transforms long-form content like blogs or webinars into short, engaging video clips for social media.
5. Otto AI - Bookkeeping and invoicing assistant that automates finances, tracks expenses, and provides real-time insights, making it ideal for solo founders and small teams.
6. OpenAI Operator - Autonomous AI agent that performs web-based tasks like form filling, scheduling, and online research, streamlining routine operations for small businesses.
7. Omneky - Autonomous ad creative agent that generates, tests, and optimizes personalized multichannel ads using performance data and AI.
8. Levity - No-code AI automation agent that classifies emails, tags data, and routes tasks based on custom logic without engineering support.
9. Presentations.ai - AI-powered presentation assistant that turns your ideas into clean, professional slide decks without the need for design skills.
10. Otter.ai - Meeting transcription and note-taking agent that records, transcribes, and summarizes your conversations so nothing gets lost.

1. Warmly

Best for: Driving more pipeline with less effort by unifying outbound, email, ads, and CRM automation in one AI-powered platform.

Who is it for: Founders, marketers, and sales teams at small B2B companies that need to grow their pipeline without increasing headcount.

Warmly is your AI-powered GTM copilot built to help small businesses turn buyer signals into booked meetings, nurtured leads, and closed deals across multiple channels. 

Whether you're running lean or just need a smarter way to manage sales and marketing touchpoints, Warmly’s agentic AI keeps things moving even when you're away from your desk.

How Does Warmly's AI Agent for Small Businesses Work?

Warmly actually includes four powerful AI agents, each designed to handle a critical piece of the go-to-market puzzle.

Here’s a quick breakdown of each:

1. AI Marketing Ops Agent - This agent focuses on optimizing your marketing operations by:

• ICP identification: Utilizing AI to conduct deep research and define the actual characteristics of your ideal customer profile (ICP).
• Real-time data & signal monitoring: Combining data from multiple enrichment providers to stay engaged with leads showing the right signals.
• Lead routing and notifications: Ensuring timely human intervention by routing leads and sending notifications based on engagement levels.​

2. AI Demand Gen Agent - Designed to scale your demand generation efforts, this agent features:

• Signal-based ad targeting: Tracks onsite and offsite signals to create specific lead segments for hyper-targeted ad campaigns.
• AI-powered lead workflows: Orchestrates leads to appropriate follow-up campaigns based on their intent level.
• Warm Offers: Displays personalized offers to leads on your site, tailored to their traffic source, persona, and buying intent.​

3. AI SDR Agent - Aimed at enhancing your sales development efforts, this agent includes:

• AI Chat: Engages website visitors instantly with personalized conversations and books meetings 24/7.
• Automated lead nurturing: Re-engages leads through follow-up emails and LinkedIn DM sequences.
• AI-powered outbound: Acts as an always-on sales assistant, handling prospecting across numerous accounts and leads.​

4. AI Co-Pilots - These agents assist in facilitating more effective human-to-human connections by:

• Engaging right when it matters: Looping you into live chatbot sessions with high-intent leads for timely conversations.
• Instant video connections: Tells you when to transition from text chat to face-to-face video calls to create more meaningful interactions.
• Personalized outreach: Providing insights to personalize outreach without extensive manual research.​

Together, these agents work as a tightly connected unit, turning cold leads into warm ones, engaging known buyers, and keeping your pipeline flowing.

Other Key Features

• Real-time visitor insights - See exactly who’s on your site, what they’re interested in, and how they’re engaging as it happens.
• Instant lead scoring - Warmly uses intent signals and firmographic data to highlight the leads most ready to buy.
• Orchestrated outreach across channels - From Slack alerts and CRM updates to ad triggers and emails, Warmly coordinates your GTM strategy in one place.
• Behavior-driven automation - Tracks digital body language to personalize every touchpoint based on buyer intent and activity.
• Simple, no-code configuration - Set up campaigns, lead routing, and personalization workflows without needing technical support.
• Built-in integrations - Connect Warmly with your existing stack in just a few clicks.

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:

1. 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.
2. 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.
3. Enterprise: Custom pricing, custom number of visitors, everything in Business, plus custom signals and warm calling.

Pros & Cons

✅ Truly plug-and-play for small teams.

✅ Handles top and mid-funnel work across channels.

✅ Eliminates manual lead research and follow-up.

✅ Continuously learns and improves outreach performance.:

✅ Has more than one AI agent, allowing you to optimize various parts of your sales process.

❌ Warmly’s AI agents are available only on its paid plans.

2. Tidio

Best for: Automating customer support and lead capture through live chat and AI chatbots.

Who is it for: Small businesses, ecommerce stores, and service providers that need to stay responsive without hiring a whole support team.

Tidio combines live chat, chatbots, and helpdesk functionality into one intuitive platform, allowing small businesses to engage with customers in real-time, resolve issues quickly, and automatically qualify leads.

It’s easy to set up, budget-friendly, and integrates with platforms like Shopify, WordPress, and Messenger out of the box.

Key Features

• Lyro AI Agent - Handles simpler queries and requests autonomously.
• AI-powered chatbot builder - You can design and deploy automated conversations using simple workflows, with no coding required.
• Multichannel support - Chat with customers via your website, Facebook Messenger, Instagram, and more within a single platform. 

Pricing

Tidio has a free plan that includes up to 50 handled conversations.

However, if you want access to more features and its AI agent, you’ll have to upgrade to one of the following plans:

1. Starter: $29/mo, includes 50 one-time Lyro AI conversations.
2. Growth: Starts at $59/mo, includes 50 one-time Lyro AI conversations.
3. Plus: Starts at $749/mo, includes up to 5,000 Lyro AI conversations.
4. Premium: Starts at $2999/mo, includes up to 10,000 Lyro AI conversations.

The exact price will depend on the number of conversations you need handled and whether you want to use Lyro.

Namely, if you want access to Lyro AI on the lower-tier plans, you can purchase it as an add-on.

The same applies to Tidio’s workflow automation capabilities.

Pros & Cons

✅ Fast, no-code setup.

✅ Affordable pricing plans with a free tier.

❌ Bot customization is limited when it comes to highly specific workflows.

3. Jasper

Best for: Creating high-converting marketing content quickly and at scale.

Who is it for: Solo marketers, founders, and small teams that need to generate consistent, on-brand copy without a full-time writer.

Jasper is an AI-powered content creation platform built to help small businesses write better content faster. 

From social media posts and product descriptions to email campaigns and blog articles, Jasper adapts to your brand’s tone and audience, delivering high-quality copy with just a few clicks.

Key Features

• Content templates for every use case - Choose from 50+ templates for blogs, emails, ads, social media, and more.
• Brand voice memory - Train Jasper to write in your unique tone, style, and messaging preferences.
• AI art generation - Create on-brand visuals alongside your written content.

Pricing

Jasper.ai has three pricing plans:

1. Creator: $49/month/seat (1 user only)
2. Pro: $69/month/seat (up to 5 users)
3. Business: Custom pricing

Pros & Cons

✅ Speeds up content production dramatically.

✅ Customizes tone and output for your brand.

❌ Can over-generate without clear input, so it cannot be used without human editing.

4. Pictory

Best for: Turning blogs, scripts, or long-form content into engaging short-form videos.

Who is it for: Small business owners and marketers who want to produce video content without editing skills or expensive tools.

Pictory uses AI to help small businesses transform written content into professional-quality videos perfect for social media, YouTube, landing pages, or email campaigns. 

No video editing experience is required. Just upload a script or a link to a blog post, and Pictory takes care of the rest.

Key Features

• Text-to-video automation - Convert any written content into a polished video with scenes, voiceover, and visuals.
• AI voiceover & subtitles - Add natural-sounding narration and auto-captioning for accessibility and engagement.
• Stock media library - Access a built-in collection of royalty-free images, video clips, and music.

Pricing

Pictory has four pricing plans:

1. Starter: $25/mo, includes 1 user, 200 video minutes, 1 brand kit.
2. Professional: $49/mo, includes 1 user, 600 video minutes, and 5 brand kits.
3. Teams: $119/mo, includes 3+ users, 1800 video minutes, 10 brand kits, and a shared working space.
4. Enterprise: Custom pricing, custom minutes, unlimited brand kits.

Pictory also has a 14-day free trial.

Pros & Cons

✅ Makes video creation accessible for non-creators.

✅ Great for content repurposing (e.g., turning blogs into videos).

❌ Limited creative control compared to pro editing tools.

5. Otto AI

Best for: Automating invoicing, bookkeeping, and payments for small businesses.

Who is it for: Freelancers, solopreneurs, and small business owners who want to streamline financial tasks without hiring a full-time accountant.

Otto AI is an AI-powered accounting platform designed to simplify financial management for small businesses. 

It automates routine tasks like invoicing, expense tracking, and payment processing, allowing business owners to focus more on growth and less on administrative work.

Key Features

• Automated invoicing - Create and send professional invoices with automatic payment reminders.
• Financial insights - Gain real-time visibility into your financial health with intuitive dashboards.
• Tax preparation - Simplify tax season with organized records and reports.

Pricing

Otto AI doesn’t publish prices for its products.

You can book a demo and request a custom quote once you’ve seen it in action.

Pros & Cons

✅ User-friendly interface suitable for non-accountants.

✅ Saves time by automating repetitive financial tasks.

❌ Limited customization options for complex financial scenarios.

6. OpenAI Operator

Best for: Automating browser-based workflows like research, form submissions, scheduling, and data extraction.

Who is it for: Small business owners and lean teams looking to delegate repetitive online tasks to an AI agent.

OpenAI Operator is an autonomous AI agent that can navigate the web like a human by clicking buttons, filling out forms, searching, summarizing, and even scheduling. 

Instead of hiring a VA or spending hours on routine tasks, small businesses can hand off browser-based work to an always-on AI assistant that learns and adapts over time.

Key Features

• Autonomous task execution - Performs multistep actions online (e.g., booking appointments, researching vendors, pulling data) with minimal input.
• Natural language instructions - Just describe what you need in plain English; there’s no prompt engineering required.
• Contextual reasoning - Understands goals, adapts mid-task, and handles unexpected changes like a real assistant would.

Pricing 

The OpenAI Operator is available only to OpenAI users on the Pro plan.

This means you need to pay $200/month to access this agent.

Pros & Cons

✅ Adapts to a wide range of use cases (from booking to research).

✅ Learns and improves over time with feedback.

❌ Still evolving - the current Operator is just a research preview, so it needs oversight for complex or sensitive workflows.

7. Omneky

Best for: Automating the creation and optimization of personalized ad creatives across multiple platforms.

Who is it for: Small business owners and marketers seeking to scale their advertising efforts without a large creative team​.

Omneky is an AI-powered advertising platform that leverages machine learning to generate and test various ad creatives. 

It helps businesses optimize their campaigns across different channels by analyzing performance data, ensuring that the right message reaches the right audience effectively.​

Key Features

• Agentic ad automation - Omneky’s AI agent turns your creative brief and insights into optimized, on-brand ads, complete with tailored headlines, CTAs, and visuals. Edit, scale, and launch variations in minutes.
• Performance analysis - Continuously monitors ad performance to identify top-performing creatives and optimize campaigns accordingly.
• Brand consistency - Ensures all ad creatives align with your brand's voice and visual identity.

Pricing

Omneky has three pricing plans:

1. Product Generation Pro: $25/mo, provides access to basic AI features and lets you have one brand.
2. Creative Generation Pro: $99/mo, all Product Generation Pro features, plus AI creative insights and recommendations, brand voice consistency, etc.
3. Enterprise: Custom pricing, everything in Creative Generation Pro, plus more advanced analytics and more refined customization options.

Pros & Cons

✅ Enhances ad performance through continuous optimization.

✅ Supports a wide range of advertising platforms.

❌ Most advanced features are available only on the enterprise-level plan.

8. Levity

Best for: Extracting insights from emails and attachments, as well as automating the workflows that follow.

Who is it for: Small business teams aiming to streamline operations like classifying emails and attachments, extracting relevant data from their inboxes, and replying.

Levity is a no-code AI automation platform that empowers small businesses to create custom AI models for tasks like extracting insights from emails and automating follow-up actions.

By automating these processes, teams can focus more on strategic activities and less on manual, time-consuming tasks.​

Key Features

• Data extraction - Extracts essential information from emails and attachments and enables seamless data transfer to other systems.
• Workflow automation - Automates complex processes across various tools, enhancing efficiency and reducing manual work.
• Email classification - Automatically labels and categorizes incoming emails based on custom criteria, enabling efficient sorting and prioritization.

Pricing

Levity doesn’t disclose prices for its product.

You can book a demo to learn more.

Pros & Cons

✅ Automates tasks involving unstructured data.

✅ Easy to use even for non-technical users.

❌ On the more expensive end of the range.

9. Presentations.AI

Best for: Quickly generating professional, on-brand presentations from simple prompts or documents.

Who is it for: Small business owners, marketers, and sales teams seeking to streamline presentation creation​.

Presentations.AI is an AI-powered platform that transforms your ideas into polished presentations in minutes. 

Whether you're starting from a prompt, uploading a document, or enhancing an existing deck, Presentations.AI simplifies the process, ensuring your presentations are both engaging and brand-consistent.​

Key Features

• Prompt-to-presentation generation - Create entire presentations by simply inputting a topic or idea in natural language.
• Document import - Convert PDFs, Word documents, or URLs into structured slide decks effortlessly.
• Brand sync - Automatically applies your brand's colors, fonts, and logos to maintain consistency.

Pricing

Presentations.AI has three pricing plans:

1. Starter: $120/mo.
2. Pro: $600/mo.
3. Enterprise: Custom pricing.

You can also get discounts for a limited time.

Pros & Cons

✅ Accelerates presentation creation, saving valuable time.

✅ User-friendly interface suitable for non-designers

❌ Limited customization compared to traditional design tools​.

10. Otter.ai

Best for: Automatically capturing and summarizing meetings, interviews, and conversations.

Who is it for: Small business owners, remote teams, and anyone who needs accurate meeting notes without a dedicated notetaker.

Otter.ai is an AI-powered transcription and note-taking tool that listens, records, and summarizes conversations in real time. 

Whether you're on a Zoom call, recording a live meeting, or debriefing after a sales call, Otter keeps your team aligned by instantly turning spoken words into searchable, shareable notes.

Key Features

• Live transcription - Provides real-time captions during meetings, calls, and webinars.
• Automatic summaries - Otter highlights key points, action items, and decisions.
• Zoom, Google Meet, and MS Teams integrations - Joins your virtual meetings automatically and start transcribing.

Pricing

Otter.ai has four tiers:

1. 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.
2. Pro: $16.99/user/mo, more advanced features and more minutes.
3. Business: $30/user/mo, everything in Pro, plus more minutes and admin features.
4. Enterprise: Custom price, everything in Business, plus more advanced security features.

Pros & Cons

✅ Great for documenting interviews, calls, and brainstorms.

✅ Easy to search and share across your team.

❌ Accuracy can vary with heavy accents or background noise.

Next Steps: Choose the Right AI Agent for Your Small Business

AI agents for small businesses in 2025 aren’t just about saving time.

They’re about unlocking growth without scaling costs. 

So, regardless of whether you're looking to streamline sales, automate content, personalize outreach, or cut down on back-office tasks, there’s now an agent built to do just that.

But if you're looking for a solution that doesn’t just handle one task but connects the dots across your entire go-to-market motion - from outbound to email to ads - that’s where Warmly stands out. 

With a full suite of AI agents designed to think, act, and execute like your best teammate, Warmly gives small businesses the leverage to punch way above their weight.

Book a demo with Warmly and see how AI agents can drive real pipeline, not just busywork.

Read More

• AI Marketing Agents: Use Cases and Top Tools for 2025 - Explore how AI marketing agents are transforming strategy, content, and customer engagement, plus which tools lead the way.
• 8 Examples of AI Marketing Automation in 2025 - Real-world use cases showing how businesses are using AI to automate and scale their marketing in smarter, faster ways.
• Best 10 AI Sales Agents In 2025 [Reviewed] - A deep dive into the top AI sales agents that are reshaping outbound, prospecting, and pipeline management in 2025.
• 30+ Powerful AI Agents Statistics In 2025: Adoption & Insights - Get the latest stats on how AI agents are being used across industries and what that means for your business.
• Are AI Agents Worth It In 2025? Expectations vs. Reality - We separate hype from results in this honest breakdown of what AI agents are really delivering for teams this year.
• 7 Factors To Consider When Buying An AI Sales Agent In 2025 - Before you invest, make sure your AI sales agent checks these critical boxes for ROI, usability, and real impact.
• 10 Best AI Sales Assistants in 2025 [Reviewed] - Meet the AI sales assistants that are helping reps book more meetings, shorten sales cycles, and stay focused on selling.
• 10 Best AI SDR Agents to Win More Deals in 2025 - From email outreach to lead qualification, these AI SDR agents are built to help small teams win bigger, faster.

Agentic AI orchestration is quickly becoming the backbone of modern go-to-market teams - and for good reason. 

Instead of relying on siloed automation tools or one-off scripts, companies are now deploying intelligent agents that collaborate, coordinate, and adapt in real time.

Think of it like this: you’re no longer managing dozens of disconnected tasks across your sales and marketing stack. 

With agentic AI, your tools communicate with each other, make decisions autonomously, and execute goals without constant human intervention. 

It’s not just automation - it’s orchestration with intent.

In this article, I’ll break down what agentic AI orchestration means, why it matters for modern teams, how companies are already using it to generate pipeline faster, and which platforms are leading the charge.

Let’s dive in.

What is an agentic AI orchestration?

Agentic AI orchestration refers to the coordination of multiple AI agents working together toward shared goals, often across different tools, workflows, or departments. 

These agents aren’t just automating isolated tasks.

They’re acting semi-autonomously, making context-aware decisions, and adapting as they go.

So, instead of traditional automation where every step is predefined, agentic orchestration enables dynamic collaboration between agents. 

Think AI SDRs scheduling meetings while syncing with demand-gen agents optimizing ad spend, and both adjusting in real-time based on campaign results. 

It’s the difference between managing tasks and managing outcomes.

How is AI agent orchestration transforming businesses in 2025?

In 2025, the biggest gains aren’t coming from single-use AI tools. 

They’re coming from orchestrated agent networks that operate across the go-to-market engine, including sales, marketing, RevOps, and customer success.

The real transformation starts with coordination. 

Instead of siloed tools or departments working independently, agentic orchestration connects everything. 

Imagine this: 

1. A marketing agent identifies high-intent leads from a campaign and instantly hands them off to a sales agent.
2. That agent then crafts personalized outreach, 
3. While a RevOps agent updates the CRM, 
4. And a success agent starts pre-onboarding.

And all this happens in real-time, without anyone needing to manually orchestrate the handoffs.

The best part is that it’s not just more efficient. 

It’s smarter. 

These agents learn from each interaction and feed that data back into the system, so each cycle gets better, letting:

• Sales teams focus only on hot leads.
• Marketers double down on what’s converting.
• Operations reduce errors and eliminate bottlenecks.
• Leadership get clearer visibility into what’s working without waiting for the end-of-quarter report.

Therefore, it’s fair to say that agentic AI orchestration is transforming businesses from reactive to responsive. 

It’s giving lean teams the superpowers of a fully scaled operation minus the overhead.

What are the different types of agentic orchestration?

Agentic AI orchestration isn’t one-size-fits-all. 

Depending on your team’s structure, goals, and GTM strategy, orchestration can take different forms. 

Understanding these types helps you design a system that actually fits how your business works, not the other way around.

Here are the core types of agentic orchestration you’ll see in 2025:

1. Horizontal orchestration

This is where AI agents operate across departments, connecting siloed teams into one cohesive flow. 

For example, a sales agent collaborates with a marketing agent to prioritize leads based on campaign data, while a customer success agent prepares onboarding materials for newly signed clients.

And it’s all based on the same shared data stream.

Use case: End-to-end deal acceleration, from lead gen to onboarding.

2. Vertical orchestration

Here, orchestration occurs within a single function, such as sales or RevOps, but spans multiple layers of responsibility. 

You might have one agent qualifying inbound leads, another scheduling meetings, and a third updating CRM records and scoring based on intent data.

Use case: Supercharging individual departments with AI teammates that multitask.

3. Goal-based orchestration

In this setup, agents are aligned around specific objectives, regardless of department or channel. 

For example, a unified goal like “book 100 demos this month” might be shared among marketing, SDR, and data agents, each playing their part in reaching the target.

Use case: Cross-functional alignment around revenue outcomes.

4. Context-aware orchestration

This is where orchestration gets really intelligent. 

Agents adapt to changes in real time by shifting priorities, rerouting workflows, or triggering new actions based on live data. 

A spike in website visits? The content agent alerts the SDR agent to follow up. 

A no-show in a demo? The CS agent triggers a re-engagement flow.

Use case: Agile operations that react to signals, not static plans.

These orchestration models can be layered and combined, depending on the complexity of your business. 

The power lies not in choosing one, but in architecting a system where the right types of orchestration serve the right outcomes seamlessly, intelligently, and at scale.

What are the benefits of agentic AI orchestration?

Agentic AI orchestration is about unlocking an entirely new level of business performance. 

By letting intelligent agents collaborate across workflows and functions, companies aren’t just saving time - they’re also creating numerous advantages across every stage of the go-to-market journey.

Here are some of the things that make agentic orchestration such a game-changer in 2025:

1. True end-to-end automation without the rigidity

Unlike legacy automation systems that require predefined logic and rigid workflows, agentic AI adapts on the fly. 

Agents can adjust to changing data, customer behavior, or internal priorities, executing complex workflows without the need for constant reprogramming or manual oversight.

Why it matters: Your processes stay flexible, even as your business scales.

2. Fewer handoffs, fewer headaches

Manual coordination between tools and teams is one of the biggest sources of friction. 

With agentic orchestration, AI agents handle the handoffs by passing context, data, and actions seamlessly between systems.

Why it matters: You reduce delays, eliminate dropped balls, and give your team time back.

3. Personalization at scale

Because agents can work contextually and in parallel, you can deliver hyper-relevant experiences to thousands of leads or customers.

And all this without relying on cookie-cutter templates or burning out your team.

Why it matters: Your outreach doesn’t just reach more people—it resonates with them.

4. Faster time-to-outcome

Agentic orchestration compresses timelines by letting multiple agents act simultaneously across a workflow. 

While a human might complete a task in hours, orchestrated agents can complete dozens in seconds.

Why it matters: You accelerate everything from lead follow-up to revenue recognition.

5. Strategic resource allocation

With AI handling repetitive, operationally heavy tasks, your team can focus on higher-impact activities, such as:

• Refining strategy.
• Closing deals.
• Crafting better campaigns.

Why it matters: Your best people stop wasting time on low-leverage tasks.

The 8 best use cases of agentic AI orchestration

Agentic AI orchestration is not just theoretical, as it’s already driving measurable impact across GTM teams. 

From automating repetitive workflows to coordinating full-funnel campaigns, orchestrated agents are reshaping how companies operate in 2025.

Here are 9 of the best (and most valuable) use cases, starting with how Warmly customers are already putting this into action.

1. Outbound orchestration

Outbound sale today is still plagued by inefficiency. 

Reps juggle CRMs, inboxes, and LinkedIn tabs, manually researching leads, drafting outreach, following up, and trying not to drop the ball. 

The result? Missed signals, delayed responses, and a whole lot of busywork that doesn’t move your pipeline.

That’s where Warmly’s AI SDR comes in.

It’s not just another automation tool - it’s a fully orchestrated outbound system. 

One that runs 24/7, reacts to real-time intent, personalizes outreach across channels, and books qualified meetings without human babysitting.

Here’s how it works:

• Real-time signal detection - The agent listens for buying intent from multiple sources (e.g., site visits, social engagement, and third-party research signals), flagging when prospects are actually in market.
• Multi-channel outbound at scale - Based on the signal and ICP match, the AI SDR launches personalized outreach across email and LinkedIn, with messages tailored to context.
• Persistent nurturing - No response? No problem. The agent runs intelligent, multi-touch sequences designed to stay relevant over time, adjusting tone and timing based on behavior.
• Website chat, supercharged - Warmly’s generative AI chatbot picks up the thread on your site, qualifying leads in real time and handling objections or meeting booking instantly.
• Automated handoffs - When a prospect is ready, the agent routes them to the right human rep, syncs calendars, and logs everything to your CRM.

Why it matters:

With Warmly’s AI SDR orchestrating outbound, you’re not just scaling activity - you’re scaling results. 

Your team gets more meetings with less effort, while your prospects get timely, relevant outreach that actually feels human.

2. Meeting orchestration

In B2B sales, timing is everything.

Prospects often drop off due to delays in follow-ups or cumbersome scheduling processes.

Namely, traditional methods involve back-and-forth emails, missed signals, and manual calendar coordination, leading to lost opportunities.​

Enter Warmly's AI Copilot - a solution designed to streamline the meeting scheduling process by leveraging real-time intent signals and automating engagement.​

How it works:

• Real-time engagement - When a high-intent lead visits your website, the AI Copilot initiates a conversation through chat, providing immediate responses to queries and guiding the prospect through the qualification process.​
• Live video transition - For prospects requiring a more personalized touch, the AI Copilot can transition the conversation from chat to a live video call, driving deeper engagement and building trust.​
• Contextual handoffs - All interactions are logged and shared with the assigned sales representative, ensuring they have full context before the meeting, which leads to more productive conversations.​

Why it matters:

By automating the initial engagement and scheduling process, Warmly's AI Copilot ensures that no high-intent lead slips through the cracks. 

It reduces the time from interest to interaction, increases meeting show-up rates, and allows sales teams to focus on what they do best - closing deals.​

3. Marketing operations orchestration

In the fast-paced world of B2B marketing, the backend operations often become bottlenecks. 

Manual processes, such as lead scoring, routing, and data enrichment, can slow down campaigns and lead to missed opportunities.​

Warmly's AI Marketing Ops Agent - a solution designed to streamline and automate these critical operations - can help with this, ensuring that high-intent leads are promptly identified and acted upon.​

How it works:

• AI-powered ICP identification -  Warmly's agent goes beyond basic firmographics to define your Ideal Customer Profile (ICP). It uses AI to analyze deep customer data, uncovering patterns and characteristics of your best customers. This allows for the identification of new prospects that closely match these profiles. ​
• Real-time signal monitoring - The agent continuously monitors a multitude of high-quality lead signals and de-anonymization data from over 10 data enrichment providers. This real-time monitoring ensures that your team stays engaged with the right leads at the right time, eliminating delays and guesswork. ​
• Lead routing - With preset rules, the agent intelligently routes leads and sends real-time hot lead alerts directly to your sales team via Slack. This ensures that a human is looped in precisely when needed, enhancing the efficiency of your sales process. ​

Why it matters: 

Warmly's AI Marketing Ops Agent enables your team to focus on strategic initiatives instead of manual tasks by automating the backend of your marketing operations. 

It ensures that high-intent leads are promptly identified, enriched, and routed to the appropriate sales reps, significantly improving your marketing strategy's efficiency and effectiveness.​

4. Demand generation orchestration

Great demand gen doesn’t start with ads.

It starts with intent. 

But most teams are still running generic campaigns, blasting cold audiences and hoping something sticks. 

That’s not just inefficient - it’s expensive.

Warmly’s AI Demand Gen Agent flips the script by orchestrating fully agentic campaigns where every action, ad, and follow-up is triggered by real buyer behavior, not guesswork.

Here’s how it works:

• Signal-based audience building - Warmly tracks a mix of on-site, off-site, and research intent signals to identify who is in-market right now. These insights are used to automatically build dynamic lead segments that sync directly with your ad platforms, so your ads only reach people who are showing intent.
• Hyper-targeted ad campaigns - Instead of static, spray-and-pray ads, the Demand Gen Agent runs personalized campaigns across your channels built around persona, behavior, and traffic source. Leads see messages that reflect exactly where they are in their journey.
• AI-powered follow-up workflows - Once a lead engages, the agent routes them into the right sequence: it might launch a multi-step nurture series, serve retargeting ads, or even surface personalized offers on your site in real-time.
• Warm Offers on-site - Based on traffic source and persona, the Demand Gen Agent can dynamically display personalized offers or content directly on your site, increasing conversion chances while the lead is hot.

Why it matters:

With Warmly’s AI Demand Gen Agent, you’re not spending money on cold clicks. 

You’re engaging real buyers, in real time, with messages that meet them exactly where they are. 

The result? Higher conversion rates, lower acquisition costs, and a demand gen engine that finally feels intelligent.

5. Lead lifecycle orchestration

The buyer’s journey isn’t a straight line, but most GTM systems still treat it like one. 

Marketing hands off leads to sales. Sales closes them. Customer success tries to retain them. 

But in between, you get gaps, delays, and lost context.

Agentic AI orchestration changes that by connecting every stage of the customer journey with intelligent, real-time coordination. 

Instead of handing leads off from team to team, orchestrated agents guide them seamlessly from first touch to long-term value.

Here’s how it works:

• Unified lead tracking from first touch - The journey begins with a marketing agent that detects early-stage engagement (like content downloads, ad clicks, or website visits), and enriches that lead profile with real-time data.
• Real-time qualification and handoff - As soon as a lead meets your ICP and intent criteria, the marketing agent signals a sales agent. Outreach begins instantly, with no lag, no manual routing, and no dropped handoffs.
• Sales engagement and deal progression - A sales agent nurtures the lead with personalized messaging across channels and adjusts approach and updates the CRM based on behavior.
• CS activation on deal close - Once the deal is signed, a customer success agent takes over automatically. It can kick off onboarding sequences, send welcome messages, and prepare resources based on the deal context it inherited from sales.
• Ongoing lifecycle orchestration - After onboarding, the same CS agent can flag expansion opportunities, surface churn risks, and loop back in sales or marketing agents to re-engage based on usage signals.

Why it matters:

Lead lifecycle orchestration breaks down silos. 

You get one connected system where every agent (marketing, sales, success) operates in sync. 

That means less friction, faster velocity, and a customer experience that feels cohesive.

6. Reactivation of ghosted or stale pipeline deals

Every sales team has them: leads that looked promising, showed early interest, maybe even took a call… and then vanished. 

In most orgs, these prospects fall into the black hole of the forgotten pipeline. 

But in 2025, top teams don’t let ghosted deals die quietly.

Agentic AI orchestration gives you a second shot. 

It continuously monitors behavior, watches for re-engagement signals, and knows exactly when (and how) to jump back in.

Here’s how it works:

• Always-on deal monitoring - AI agents keep tabs on ghosted and stale deals in your CRM by tracking LinkedIn activity, website visits, company news, funding announcements, and buying intent.
• Behavioral and contextual triggers - When something shifts, like a stakeholder posting about a new initiative, a prospect revisiting your site, or an account hiring for a relevant role, the agent knows. It flags the opportunity and prepares tailored outreach.
• Contextual re-engagement campaigns - Instead of generic “just checking in” messages, the system crafts personalized reactivation sequences that reference the deal history, latest activity, and persona-specific pain points.
• Smart rep alerts and handoff - If human outreach is more appropriate, the agent alerts the right rep with complete context, including what changed, why this moment matters, and what message to lead with.

Why it matters:

Reactivation isn’t about luck - it’s about timing. 

With agentic orchestration, your team shows up exactly when interest resurfaces. 

That means more resurrected deals, shorter sales cycles, and fewer missed second chances.

7. Customer onboarding and expansion orchestration

Closing the deal isn’t the finish line.

It’s actually the starting point. 

But too often, the post-sale experience is disjointed. 

Handoffs to customer success are manual. Onboarding is delayed. Expansion signals go unnoticed. 

That’s not just bad CX: it’s lost revenue.

Agentic AI orchestration changes that by connecting sales, success, and product touchpoints into one seamless customer journey from “yes” to lifelong value.

Here’s how it works:

• Instant post-sale activation - As soon as a deal closes, an onboarding agent gets to work, pulling in deal context from the CRM and launching personalized onboarding flows without waiting for CS to catch up.
• Automated welcome and enablement - The agent can send welcome emails, schedule kickoff calls, and surface product resources based on plan type, persona, or use case. 
• Milestone-based engagement - As the customer progresses, completing onboarding steps, adopting features, or expanding usage, the system celebrates wins, surfaces tips, and routes any issues to the right team.
• Expansion opportunity detection - AI agents monitor for upsell triggers, such as usage surges, new team members, or interest in new products. When a signal hits, the agent can either initiate expansion outreach directly or notify an AE with full context and suggested next steps.
• Churn risk intervention - If engagement drops or usage patterns change, the agent flags risks early, prompting proactive check-ins, re-engagement flows, or support interventions before it’s too late.

Why it matters:

With agentic orchestration, your customers never feel forgotten. 

Every milestone is supported, every signal is acted on, and every growth opportunity is surfaced. 

That means faster time-to-value, higher NRR, and a customer experience that drives loyalty by design, not luck.

8. Revenue reporting and pipeline intelligence

Revenue teams shouldn’t have to wait until the end of the quarter to figure out what’s working and what’s not. 

Yet most orgs still rely on backward-looking dashboards, scattered reports, and time-consuming manual analysis that arrives too late to act on.

Agentic AI orchestration flips that script by embedding intelligence directly into your pipeline. 

Agents don’t just report - they observe, analyze, and alert in real time, giving your team visibility and action at every step of the funnel.

Here’s how it works:

• Automated pipeline monitoring - Orchestration agents are always watching your pipeline, pulling data from CRM, marketing platforms, customer success tools, and product analytics. No need for manual exports or weekly scrambles to update dashboards.
• Signal-driven alerts - If something shifts, e.g., conversion rates drop, win rates spike, or lead velocity slows, agents flag the anomaly immediately. 
• Real-time performance insights - Agents don’t just surface what happened, they show why. Was it campaign performance? Rep activity? Funnel friction? The system connects the dots and identifies root causes across tools and teams.
• Predictive forecasting and scenario planning - Based on historical trends and live signals, agents can forecast revenue trajectories, flag pipeline gaps early, and even simulate “what-if” scenarios to help you plan better.
• Actionable intelligence, not just reports - When insights are uncovered, agents can automatically trigger responses, such as rerouting leads, notifying reps, adjusting campaign budgets, or even shifting targeting, all without human intervention.

Why it matters:

With agentic revenue intelligence, you do more than just measure results.

You shape them. 

Your team makes smarter decisions faster, reacts in real time, and drives outcomes proactively. 

No more “we’ll fix it next quarter.” You fix it now.

Top 3 agentic AI orchestration software in 2025

As more companies shift from siloed automation to fully orchestrated AI systems, the demand for platforms that support agentic orchestration is exploding. 

Below are three of the top players in the space, starting with the one redefining outbound and GTM orchestration across the board.

1. Warmly - Best for outbound and GTM orchestration

Warmly is the go-to choice for teams looking to turn outbound chaos into intelligent, scalable systems. 

It offers a full suite of AI agents purpose-built for GTM teams from SDR and Demand Gen to Marketing Ops and CS. 

These agents don’t just automate - they collaborate, making real-time decisions, syncing across channels, and moving deals forward without human micromanagement.

Standout features

• AI SDR for warm, signal-driven outreach across email and LinkedIn.
• AI Copilot for providing real-time suggestions to human reps and looping them into warm conversations.
• Marketing Ops Agent for lead routing, ICP enrichment, and monitoring for intent signals.
• Demand Gen Agent for intent-based ad campaigns and personalized site offers.

So, if you're looking for a platform that brings agentic AI into every stage of your revenue engine, Warmly is hard to beat.

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:

1. 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.
2. 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.
3. Enterprise: Custom pricing, custom number of visitors, everything in Business, plus custom signals and warm calling.

2. Humantic AI - Best for persona-driven outreach orchestration

Humantic AI uses behavioral science and personality insights to power AI-driven personalization at scale. 

It’s particularly strong in orchestration for sales outreach, helping reps tailor their communication based on the recipient’s DISC profile.

This is why it’s best for sales teams that want to boost response rates with psychologically-informed messaging

Standout features

• Personality AI profiling - Instantly analyzes a prospect’s digital footprint to generate DISC-based personality profiles.
• AI-driven messaging recommendations - Suggests words, tone, and structure that best resonate with each persona.
• CRM and sales engagement integration - Embeds insights directly into tools like LinkedIn, Outreach, and Salesforce for seamless rep workflows.

Pricing

Humantic AI has two different sets of pricing plans for individuals and organizations.

If you’re a solo user, you can pick from three plans:

1. Pro: $40/month, includes 500 prospect profiles/month, Chrome Extension, personality overview, qualifying and prospecting tips, etc. (this plan is annual only).
2. Expert: $50/month, includes unlimited prospects, everything in Pro, plus 1-click personalization, sales dashboard, etc.
3. Owner: $75/month, everything in Expert, plus 200 profile enrichments/month, account level information, etc.

When it comes to plans for organizations, there are also three plans:

1. Startup: $275/month, includes 5-20 users, DISC & Big Five profiles, 1-click personalization, etc.
2. Growth: $1050/month, minimum 10 users, everything in Startup, plus personalized cadences and advanced CRM enrichments.
3. Enterprise: Custom price, minimum 20 users, everything in Growth, plus API access, enhanced lead scoring, etc.

3. Zapier Agents (Beta) - Best for building custom AI bots across your stack

Although this isn’t exactly an agent per se, it can be very helpful for teams seeking to build custom AI bots that integrate with a wide range of applications.

Namely, Zapier Agents is an experimental AI workspace where you can build AI bots and teach them to handle tasks in your favorite apps. 

It allows users to create custom AI agents that can automate tasks across over 8,000 apps, enabling seamless integration and workflow automation.

Standout features

• Custom AI bots - Lets you design AI bots tailored to specific workflows and tasks.
• Extensive app integration - Allows you to connect with over 8,000 apps to automate complex workflows.
• User-friendly interface - Enables building and managing bots without the need for extensive coding knowledge.

Pricing

Zapier Agents has a free forever plan that lets you run 400 activities per month, in addition to some basic features.

To access more actions, subscribe to one of two paid plans:

1. Pro: $50/mo, everything in Free but lets you run 1,500 activities per month.
2. Advanced: Custom pricing, custom number of activities.

Final thoughts: The future belongs to orchestrated teams

Agentic AI orchestration isn’t just the next evolution of automation.

It’s a shift in how modern GTM teams operate, making workflows smarter, faster, and more connected.

And while several tools are entering the space, Warmly stands out for one key reason: it’s purpose-built for the entire GTM process. 

With AI SDRs, Marketing Ops agents, Demand Gen bots, and meeting copilots all working in sync, Warmly doesn’t just automate tasks - it drives pipeline.

Ready to start orchestrating smarter growth with Warmly’s AI agents?

Book a demo and see how agentic AI can transform your sales process end-to-end.

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