For two years, sales leaders have braced for AI to shrink their teams. A new analysis of 33.5 million B2B deals, run by Gong across 3,398 companies since February 2024, says the opposite is happening at the organizations moving fastest on AI: they are hiring more aggressively than their slower-moving peers, not less.
What the Deal Conversations Actually Show
Gong’s research team, led by Dan Morgese, Director of Content Strategy and Research, mined aggregated, anonymized topic signals from live sales conversations rather than survey responses, which is what makes the pattern hard to dismiss as spin. Since 2024, mentions of AI inside B2B sales conversations are up 85%. Talk of AI agents specifically has climbed 1,217%, roughly 13 times where it stood two years ago. Buyers are also changing how they shop: AI-assisted vendor discovery is up 250%, AI-assisted vendor evaluation is up 209%, and mentions of using AI as a buying advisor are up 280%.
Against that backdrop, the number sales leaders would expect to spike, hiring conversations tied to AI-driven headcount cuts, barely moved: up just 1%. Anxiety about AI replacing human work in these conversations did rise 237%, but Gong’s team found that spike concentrated in buyer concerns and seller pitches, not in actual staffing decisions.
The Layoffs Attributed to AI Don’t Add Up
Gong’s report puts a number on the gap between the narrative and the record: of roughly 1.2 million tracked layoffs in 2025, fewer than 5% were primarily attributable to AI-driven efficiency gains. The report calls the broader trend “AI-washing,” where companies cite an AI strategy publicly while the underlying driver is a correction for prior overhiring, not automation replacing workers at scale.
That reading lines up with data Gong cites from outside its own dataset. Deloitte’s 2026 State of AI research found only 25% of companies have moved 40% or more of their AI pilots into production, meaning most organizations are still experimenting rather than automating at the scale layoff headlines imply. PwC’s 2025 Global AI Jobs Barometer found revenue growth in AI-positioned industries has nearly quadrupled since 2022, with wages in AI-exposed roles rising twice as fast as elsewhere. Separate Federal Reserve research turned up no evidence that AI adoption is reducing hiring: firms with higher AI uptake are not posting fewer job openings than firms with lower uptake.
The Companies Deploying AI Fastest Are Hiring the Most
The report’s central finding inverts the assumption most sales leaders are operating under. Among the 3,048 revenue leaders Gong surveyed on hiring plans, the organizations with the most mature AI deployments reported the most aggressive hiring plans of any cohort, not the most cautious. Gong’s framing is that AI is redirecting effort rather than eliminating it: reps at AI-mature companies are spending less time on drafting, summarizing, logging notes, and researching accounts, and more time on judgment calls, relationships, and navigating complex, multi-stakeholder deals.
That shift shows up elsewhere in the conversation data too. Budget pressure discussions are up 41%, build-versus-buy debates are up 45%, and license or seat-reduction conversations are up 44%. Those are the marks of buyers scrutinizing every tool purchase harder, a rational response to a tighter market, not evidence that AI itself is the reason headcount is shrinking. It also tracks with what Gong itself has been building toward with Revenue Harness, a platform aimed at letting RevOps leaders configure their own AI agents rather than wait on engineering, which only makes sense if the buyer base plans to keep growing its revenue operations function, not shrink it.
Where the Optimism Has Limits
None of this means the AI-and-headcount question is settled. Gong’s own supporting data shows most companies are still early: Deloitte’s research found only a quarter of organizations have pushed 40% or more of their AI pilots into production, which means the “AI-mature hires more” pattern is currently describing a leading edge, not the average sales organization. Morgese’s report is explicit that it is separating two different things: AI genuinely changing the nature of work for reps who spend less time on drafting and logging and more on judgment calls, versus a macro narrative that overstates how much of that change has actually reached production systems. A sales leader reading the top-line numbers should not assume their own org is automatically in the hiring-up cohort just because it bought AI tools; the report’s finding is tied to deployment maturity, not deployment intent.
There is also a selection-bias question worth naming directly: companies confident enough in their AI rollout to keep hiring aggressively may simply be the companies that were already growing fastest for other reasons, with mature AI adoption as a symptom of strength rather than its cause. Gong’s dataset cannot fully separate those two explanations from conversation signals alone, which is a reason to treat the finding as directional rather than a guarantee that adopting AI tools will itself produce a hiring tailwind.
What This Means for the Sales Leader
The practical takeaway is not that AI anxiety is baseless, it is that the anxiety is aimed at the wrong lever. Sales leaders evaluating headcount plans against AI adoption should treat deployment maturity as a leading indicator of growth, not contraction, and communicate that distinction explicitly to reps who are absorbing “AI is coming for your job” messaging from outside the building. The same discipline applies to how teams talk to buyers: with vendor discovery and evaluation increasingly AI-assisted on the buyer’s side, discovery calls are shifting from educating a prospect to validating what an AI-informed buyer already believes, which changes what a rep needs to prep before that first conversation. That same AI-assisted evaluation is also showing up inside the sales process itself, echoing what conversation intelligence platforms have been building toward as they move from recording calls to actively guiding them.
The action item is straightforward: before finalizing a headcount plan around AI ROI, pull the same comparison Gong ran internally, hiring intentions against AI deployment maturity, rather than assuming the two move in opposite directions by default. The data says they do not.
Source: Gong
