The State of AI in RevOps: 300 Teams, 6 Patterns, & 1 Big Miss

AI is everywhere in go-to-market teams: in strategy decks, LinkedIn posts, and product demos. But inside most RevOps orgs, the reality looks different: scattered experiments, inconsistent adoption, and ROI that can’t be truly articulated.

So we went straight to the source.

We surveyed the top 300+ RevOps leaders from fast-growing companies, from seed-stage startups to public software companies. We wanted to understand how the people closest to GTM operations are actually using AI today and what they plan to do next.

So we asked them 24+ uncomfortable questions such as:

• How much are you investing into AI - and is it paying off?
• What groundbreaking use cases are the best RevOps leaders exploring?
• What are teams planning to use AI for in the next 6-12 months?
• And a bunch more direct, no-BS questions.

We tried to look for the most concrete examples of how AI is actually contributing to the entire go-to-market department beyond random LLMs usage. And we asked for proof.

We found six clear patterns from high-performing teams  and one common failure point shared across the board:

Let's dive in.

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RevOps AI Adoption

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Everyone says they know AI but a few can prove it.

Most RevOps leaders score high on AI knowledge, but ROI doesn’t follow.

71% of RevOps leaders rate themselves a 7 or higher in AI knowledge. But when asked about their actual implementation results, the cracks start to show. Ad hoc use cases, low ROI, and minimal confidence in outputs were common.

While knowing about AI and the best use cases it’s the first step, most revenue operations leaders surveyed are still trying to implement AI within the major GTM workflows. Respondents who rate themselves highly in AI knowledge are no more likely to have defined ownership or measurable outcomes in their org.

This disconnect suggests that knowing about AI isn’t enough to drive implementation. Teams don’t need more knowledge. They need ownership and accountability.

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Most teams are still experimenting with AI.

30% of teams are experimenting with AI but not seeing the results they want.

Most teams fall into a "low-stakes" category. They're either experimenting with smaller use cases that save a few hours a week or using AI for very niche/small workflows. And both of these groups are not really seeing tangible ROI, which makes sense — it's tough to see big impacts of AI if it's only being embedded into non-critical functions. 25% of surveyed teams are actively integrating across all go-to-market functions, with only 4% categorizing themselves as highly AI-driven orgs.

Among the 34 percent who selected “Experimenting,” a large portion rate AI as important or mission-critical to their 2025 strategy. The idea that non-users are skeptics is simply not true. In reality, they’re often just stuck behind resource, system, or leadership constraints. These teams don’t need convincing. They need enablement.

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AI is stuck enriching leads for reps.

Most teams use AI for account research or lead enrichment.

When asked where AI is currently being applied, the top areas were account research and lead enrichment. These are often repetitive or data-heavy tasks where AI can provide immediate efficiency gains without heavy integration, which you can see by using a standalone tool.

This clearly shows how low the stakes are for operationalizing AI. Researching prospects and accounts seems to be the easier way to get started using AI, but it doesn't provide any unique leverage to any of the go-to-market function. It seems like the best RevOps teams are seeing the highest ROI often rely on AI for CRM data hygiene, forecasting, and pipeline management.

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Going deep beats going wide.

Small-scale, focused use cases show the most significant ROI.

Teams with just 1–2 focused workflows often report stronger time savings per use case than larger adopters. In contrast, teams using AI across 7 or more use cases often report only modest time savings or limited measurable outcomes.

"Less is more" has never been more true than in this new era of AI. Having the strategic discipline to go deep in a few high-impact workflows delivers more value than trying to embed AI broadly across many use cases.

Applying AI to more than lead enrichment workflows puts you in the top 10% of RevOps teams.

With the most common use cases being lead enrichment, these are merely surface-level wins: they save time but don't shift GTM performance in a meaningful way. That's the problem. Teams are gravitating toward the easiest paths to adoption — not the most impactful ones.

To get real leverage, teams must move beyond tactical automations and start embedding AI into revenue-driving workflows like routing, forecasting, and full-cycle opportunity management.

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Where RevOps owns AI, results follow.

The most common owner of AI… is no one.

Ownership of AI adoption is very fragmented. Nearly one in four orgs selected “no clear owner” as their current state. In some companies, AI is driven by IT or data teams, while in others it’s RevOps or even individual contributors experimenting ad hoc. A notable number of respondents reported “no clear owner”, which likely slows coordinated adoption and impact.

GTM leaders say they own AI, but rarely act on it.

Those who selected “GTM leadership” often report fewer measurable improvements. These leaders may support AI in theory, but fail to delegate implementation when ask for tangible ROI.

Where RevOps leads, outcomes & ROI follow.

Companies that assigned AI ownership to RevOps also reported higher workflow counts, better time savings, and stronger confidence in outputs. It’s no surprise - RevOps already owns systems, data flow, and efficiency, so it should also lead and support other go-to-market functions with highly targeted AI implementations.

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AI RevOps Use Cases

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AI is everywhere, but mostly ad hoc.

Most teams currently use AI in ad-hoc or individual ways.

Most teams currently use AI in ad-hoc or individual ways, such as ChatGPT experiments or lightweight outbound personalization, rather than through structured or centralized workflows. About 15% of respondents noted custom or internal workflows built with APIs or LLMs, but these remain the exception, even with a possible higher ROI or impact on business metrics.

Power users either rely on purpose-built AI tools or struggle with native AI features.

The most interesting insights from this question have to be the actual tools used by the power users, which can be categorized into two segments: using AI features from a legacy platform or leveraging an AI-first tool. Both of these users see more impact on GTM efficiency; however, the general theme has to be the fact that most AI features still require a lot of off-platform customization. As of today, you're better off investing in an AI-first platform that has the necessary infrastructure to leverage all of AI's capabilities.

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Efficiency is up, but pipeline isn’t.

Most teams are still measuring AI in terms of effort, not outcomes.

When it comes to impact, the most commonly cited gains were in reducing manual work—especially in areas like account research, enrichment, and outbound personalization. However, relatively few teams reported measurable improvements in pipeline generation, lead scoring accuracy, or call transcription automation.

Overall, teams that move beyond surface-level use cases like lead or account research and apply AI to pipeline management or accurate sales forecasting tend to see stronger results. Teams using AI for lead routing, forecasting, or qualification report stronger business outcomes—not just productivity gains.

If everyone’s using AI on their own, it’s no surprise the team’s results aren’t improving.

On top of that, most AI applications are used ad-hoc or individually which also means that the use cases are spread thin and without any real orchestrate. This is why we believe RevOps should own the implementation of AI across all revenue functions.

If there's one takeaway from this report, it's this: AI can give you an unfair GTM advantage - but only if you let it power your most critical business workflows. As for AI-driven outbound personalization, it's quickly becoming table stakes.

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Most teams bolt AI onto old tools.

Built-in AI features dominate but underdeliver.

Most respondents rely on AI features embedded in CRMs or marketing automation platforms (MAP) to drive adoption or higher GTM efficiency. While accessible, these tend to deliver generic functionality with limited customization to only what this one platform can offer. The reality is that most go-to-market teams need context from multiple sources that can feed into AI models to create business impact.

Best results come from dedicated AI SaaS platforms.

Users of standalone tools report more measurable impact than users of CRM-native AI. These solutions tend to offer sharper focus and clearer workflow logic, especially for revenue forecasting, lead routing, and account scoring.

The common theme from the conversations is a lack of context. CRM, although a “source of truth” for all GTM activity, often lags behind the data needed. Most teams are bolting AI on to existing tools, not building workflows around it.

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AI is used often, but without a strategy.

AI is used daily, but often in an ad-hoc manner and without the necessary GTM context.

In most cases, usage doesn't translate into ROI. Even with the majority of teams or individuals using AI more than a few times a week the usage is still very ad-hoc or based on time-saving tasks. This really goes to show that lack of necessary context and strategic application of AI can only slightly increase productivity rather than really drive impact.

This only backs up the previous point on RevOps becoming an owner of AI implementation for GTM. If RevOps orchestrates the AI applications, teams should see higher usage, more ROI, and better GTM efficiency.

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ROI of AI in RevOps

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AI is helping teams work faster, not grow faster.

Most teams are still measuring AI in terms of effort, not outcomes.

The key difference in how the best RevOps teams use AI lies in their expectations around outcomes. Most teams evaluate AI through the lens of effort saved -less data entry, easier outbound personalization, faster content generation. But the best teams don’t see AI as just a time-saver. They expect real business impact: faster speed-to-lead, higher data accuracy, and improved conversion rates.

Speed-to-lead is the most clear and immediate benefit.

The leverage from reducing manual work is limited. At best, you save a few hours per rep per day, and while that’s helpful, it doesn’t get you praise in the boardroom the way growing revenue does.

Less than 10% of teams see positive impact of AI on pipeline.

In terms of measure improvements, less than 9 percent say AI has helped generate more pipeline, and only 7 percent have seen improved conversion or pipeline indirectly accelerated by the use of AI.

11 percent reported faster lead routing and follow-up, more than those who reported revenue-related improvements. This indicates that AI is most effective today when it’s embedded in real-time workflows, such as lead routing, or scoring - where speed and depth of output matters the most.

Teams that don’t use AI within workflows tend to see lower ROI.

13 percent said they’ve seen no measurable improvement - a notable level of honesty in an AI-hyped landscape. This likely reflects either premature adoption, poor implementation, or lack of tracking frameworks. Teams reporting minimal or no impact tend to lack workflow coordination or clear ownership.

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AI saves a few hours a week for teams.

Most respondents save under 5 hours per week.

While 35 percent report some weekly time savings, the numbers remain modest. AI is helping, but it hasn’t dramatically changed the shape of the workweek for most GTM operators. These are likely teams using AI for content generation, enrichment, or summaries, which are relatively simple, high-frequency tasks.

The same goes for the time saved per rep thanks to RevOps-built automation. The majority of GTM operations teams say that they managed to save a few hours per week per rep. While this is an amazing accomplishment that can get a few Slack shoutouts, it doesn't necessarily drive the tangible, irreplaceable ROI.

Workflow count doesn’t correlate with time saved.

Some teams with 7 or more workflows still report low time savings. More automation doesn't necessarily yield better ROI, as teams that focus on key business use cases often see higher improvements compared to manual processes. Picking a few key workflows such as lead routing, scheduling, or lead to account matching and embedding AI in multiple places in these workflows seems to provide much higher value than small (but compounding) time improvements.

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AI isn’t taking jobs (for now).

AI is not going to take your job. Someone with high AI leverage will.

Despite some automation benefits, very few respondents reported reducing headcount, and only a handful mentioned reallocating roles. This reinforces the idea that AI is currently acting as a productivity enhancer, not a workforce disruptor, which should come as no surprise. It’s almost impossible to hire fewer people if you’re only using AI to save a few hours per day per rep.

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Challenges of AI

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RevOps needs to take the wheel.

You can’t expect great AI output without the right combination of inputs.

The biggest blockers to AI adoption in GTM workflows are unclean data and a lack of context for the AI. Fewer and fewer people fully trust enrichment vendors, CRMs still need to be updated, and social platforms continue to block scraping. On top of that, respondents say that budgets are not keeping up with the needs for specific AI-first tooling or that those AI tools are too hard to fully implement and see real business impact.

That tells us a lot about the current state of AI in GTM: leaders may know the tools, but they lack the frameworks, playbooks, and internal enablement to turn them into outcomes. Until there’s ownership, education, and tooling that builds trust, AI will stay stuck in pilot mode. Many respondents also cited issues such as security and privacy concerns, as well as internal resistance, meaning that both technical and organizational challenges are slowing the adoption of AI for go-to-market.

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Teams don’t trust AI.

Lack of GTM context kills trust in AI outputs.

Confidence in AI-driven outputs is generally neutral to low with only a small subset of respondents express high confidence in AI outputs. This skepticism is likely based on the lack of context and poor integration into GTM systems. You can’t trust what you don’t understand. And you won’t scale what you don’t trust.

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AI needs ownership, education, & enablement.

Without ownership, education, and better integrations, AI adoption will not accelerate.

When we asked GTM and RevOps leaders to name their #1 barrier to adopting AI, the responses were clear and surprisingly human: 19% cited poor data quality as the biggest blocker & 18% pointed to lack of internal knowledge on how to implement AI. Others mentioned lack of prioritization, integration complexity, and security concerns which means: It’s not that the tools aren’t good enough - it’s that the foundations aren’t in place.

Many teams are playing with AI, but few know how to operationalize it.

Even more telling: these blockers directly contradict other responses. While most leaders rate their AI knowledge as “high,” nearly 1 in 5 say they don’t know how to implement it. That disconnect is the core issue. Many teams are playing with AI — but few know how to operationalize it. No strategy. No ownership. No playbook.

Until RevOps or GTM leadership steps in to own implementation, clean up core data systems, and embed AI in workflows with real business context, adoption will continue to stall.

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Future of AI in RevOps

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45% say they’ll do more with AI.

Nearly 45 percent of teams plan to expand AI usage over the next year.

The intent is loud and clear: nearly 45% of teams plan to expand their use of AI across GTM and RevOps workflows in the coming year. But there’s a gap between aspiration and execution.

While nearly half of respondents say they’re planning to deepen AI adoption, most are still stuck in tactical use cases like enrichment or content generation - with minimal ROI. Without ownership, orchestration, and clean systems, those plans often stay on the roadmap… and never hit production.

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AI is on everyone’s roadmap.

AI is mission-critical for most RevOps teams for 2025.

Still, there’s clear intent to grow: nearly all respondents plan to increase AI usage over the next 12 months. In fact, several rated AI as “important” or even “mission-critical” to their 2025 strategy, signaling growing buy-in at the leadership level. Even if current adoption is still shallow, almost everyone agrees that AI is going to revolutionize go-to-market to some degree.

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Teams want to do more with AI Agents.

20% of teams want to use AI agents in their revenue department.

Most RevOps leaders say they plan to increase their use of AI in the next 6–12 months — and many specifically mention AI agents as their next big investment. However most usage today is still rooted in AI workflows which are linear, rule-based automations designed for predictable tasks like enrichment, scoring, or routing.

Workflows follow instructions. Agents interpret objectives.

AI agents are dynamic, goal-driven systems that adapt their behavior, choose tools, and sequence their own actions to reach outcomes - like researching an account, crafting a personalized email, or qualifying a lead autonomously. Right now, very few teams actually run AI agents in production. And that shift will require not just better tools, but better orchestration, better context, and a new internal role: someone to manage and govern these agents - not just configure them.

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AI targets are operational, not flashy.

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AI is still seen as helping tactically but not strategically.

When asked to describe AI’s current role, most answers pointed to it being a “time saver,” “idea starter,” or “not yet fully implemented.” Many responses suggest AI is viewed as supportive, not central, helping with tactical work but not yet influencing strategy or decision-making.

1 in every 3 teams want to use AI for lead and account scoring.

It seems like the biggest opportunity for RevOps teams is to embed AI within the scoring workflows that go beyond a simple +10,-10 logic for select numbers of criteria. Imagine a great lead comes in (on paper) but you go to their website - only to find out that it's not showing promise. Data vendors are only getting you so far. Same with lead scoring logic. The best teams want to apply lead scoring agents to improve sales team's conversion rates and morale.

RevOps teams aim to deepen, not drastically shift, their AI use.

As for what’s next, the most desired areas for AI application are forecasting, data hygiene reporting, and outreach personalization. This aligns closely with the areas already being explored, indicating that many teams are looking to deepen rather than radically shift their AI investments in the near term.

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Takeaways

AI adoption in GTM is no longer a question of if, but a matter of how well. Most teams already use AI in some form, and nearly half plan to deepen adoption in the coming year. But while confidence in AI fundamentals is high, execution remains fragmented, and outcomes are underwhelming. The reality: AI is being used, but rarely orchestrated.

The reality: AI is being used, but rarely orchestrated.

The most successful teams don't just experiment with AI. They operationalize it. They assign ownership (most often to RevOps), define a few high-leverage workflows, and integrate AI into real-time systems like lead routing, enrichment, and SDR personalization. These teams see the highest ROI and time savings, even with fewer but more deeper workflows.

The biggest blockers? Dirty data, tool sprawl, and lack of ownership. Until organizations address these basics and invest in scalable, purpose-built tools like Default, AI will remain stuck in pilot mode. Leadership alignment, trust in outputs, and embedded usage are the next frontiers.

The teams seeing real ROI treat AI like infrastructure. They assign ownership (often to RevOps), go deep on a few core workflows, and embed AI where it drives revenue—not just saves time.

To move forward:

• RevOps must own AI orchestration
• Fewer, deeper workflows > many shallow ones
• Focus on trust, not just tools

For RevOps teams, the mandate is clear: Own it, focus it, and operationalize it.

If 2024 was the year of AI hype, 2025 is the year of AI accountability. It's time to stop tinkering and start operationalizing.

Schedule a demo of Default how other teams are operationalizing AI for routing, scheduling and enrichment workflows.

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