Key Takeaways
Why is RevOps ahead of sales on AI adoption? RevOps and sales ops professionals report the highest weekly AI usage of any go-to-market discipline, at 55%, because their core work — data enrichment, forecasting, reporting — is the first layer AI tools were built to automate.
Does AI productivity automatically mean better business outcomes? No. Survey data shows 46% productivity gains and 12 hours reclaimed per week, but RevOps leaders themselves say the next milestone is proving those gains translate into revenue outcomes, not just faster workflows.
What’s the biggest blocker to scaling AI in RevOps? System integration. Most RevOps teams run planning, forecasting, territory, and compensation as disconnected systems, which limits how far AI-enriched data can actually improve decision-making.
What closes the RevOps skills gap faster than training alone? Tools that encode RevOps judgment — territory logic, quota fairness, comp accuracy — directly into the workflow, so teams don’t need every generalist to become an AI specialist to trust the output.
CEOs Are Rethinking GTM Planning (And Building This Instead)
I read a lot of survey data. Most of it tells me what I already suspected. Every once in a while, one lands that puts precise numbers on something I’ve been saying in boardrooms for two years. ZoomInfo’s new State of AI 2025 Report, covered by their Content Director Curt Woodward, is one of those.
RevOps and sales ops professionals are now the power users of AI inside go-to-market organizations. Fifty-five percent use it weekly — ahead of sales. Seven in ten are satisfied with what AI is doing for forecast accuracy. Teams report being 46% more productive and are reclaiming about 12 hours a week that used to vanish into manual data work.
Why 70% of GTM Strategies Fail (And 7 Ways To Prevent It)
Tessa Whittaker, VP of RevOps at ZoomInfo, said something in the report that I totally agree with: “You need to show that you’re driving better business outcomes, not just an increase in productivity.”
That’s the sentence I want every revenue leader to tape to their monitor.
Productivity Was Never the Hard Part
I’ve spent a lot of time tracking the GTM execution gap, to evaluate the distance between what your tech stack can technically do and what it actually delivers, because your planning, forecasting, territory design, and compensation systems don’t talk to each other. This survey is the clearest external validation I’ve seen of that thesis.
Look at what Woodward’s reporting names as the real constraints on RevOps: system integration, skills gaps, and shaky trust in the underlying data. Notice none of those are “we don’t have enough AI tools.”
Keep in mind, every team in this survey already has AI. What’s holding them back is architecture — the fragmented mess of CRMs, marketing platforms, and analytics tools that were never built to operate as one system.
That’s a design problem. And design problems don’t get solved by adding another point solution.
Where I’d Push the Conversation Further
The report is right that data enrichment is RevOps’ go-to AI application right now — it’s the foundation for segmentation, pipeline management, and scoring. But clean data sitting in a well-enriched CRM doesn’t automatically mean your quota logic reflects reality, or that your territory model updates when the market shifts, or that your comp plan pays people fairly for the deals AI just helped them close.
The Rise of the Revenue Command Center: Why GTM Teams Are Unifying Plan, Perform, and Pay
Forecasting satisfaction is climbing, and that’s genuinely good news, but a forecast is only as honest as the plan it’s measured against. If your planning still lives in a spreadsheet that’s disconnected from the CRM feeding your AI forecast model, you haven’t closed the execution gap — you’ve just made the gap faster to observe.
This is exactly why Fullcast’s plan-to-pay suite — Plan, Perform, Pulse, Propel, and Pay — functions as one architecture instead of five modules that happen to share a login.
The Skills Gap Isn’t Solved by More Training Alone
I’ll push back gently on one thing in the report. It frames the skills gap as a training problem — get more RevOps people hands-on with AI, and the gap closes. I think that’s necessary but insufficient. The teams I see winning aren’t the ones who trained harder. They’re the ones whose tools already encode good RevOps judgment — territory fairness, quota logic, comp accuracy — into the workflow itself, so a generalist doesn’t have to become an AI specialist just to trust the output.
The Real Story
RevOps stepping forward as a strategic architect of growth is the headline underneath the headline. And the teams pulling ahead of that curve have unified the plan-to-pay motion enough that AI has something coherent to work with in the first place.
