Key Takeaways
- AI discussions in deals jumped 85% over two years while hiring conversations held steady, signaling that top organizations treat AI as a capacity multiplier rather than a headcount cut.
- With buyers using AI to research and shortlist vendors (up 280%), first sales calls have shifted from education to validation, forcing reps to rethink discovery and proof points.
Something surprising is happening inside your deals. AI shows up in nearly every conversation now, but the headcount panic everyone predicted hasn’t followed. Gong Labs data shows AI discussions in deals jumped 85% over the past two years, while hiring conversations held steady. That gap tells a bigger story. The smartest B2B revenue organizations aren’t treating AI as a way to shrink teams. They’re treating it as a way to raise the ceiling on what those teams can do.
Dig into the numbers and the urgency sharpens. Conversations explicitly naming AI replacing human work climbed 237%. Buyers disclosing that they use AI to inform vendor decisions rose 280%. And talk of AI agents, the systems that reason, act and participate in workflows, exploded 13x. Your buyers aren’t waiting for permission to change how they evaluate you. They’re already doing it, often before your first call. The sellers who adapt their discovery, messaging and proof points will win the deals. The ones who run the old playbook will watch those deals close somewhere else.
To make sense of what these trends mean for your revenue team, we sat down with Shane Evans, Chief Revenue Architect at Gong. Drawing on the “Gong Labs Trends June 2026: AI vs. Jobs” report, Evans breaks down three subjects every seller needs to master right now: how to respond when buyers raise concerns about AI replacing headcount, how discovery and positioning must evolve as buyers lean on AI to research vendors, and how to stand out when prospects increasingly discover vendors through AI tools. His answers are direct, specific and built for practitioners who’d rather act than theorize.
Demand Gen Report (DGR): What’s the biggest takeaway from this report for B2B professionals?
Shane Evans: The biggest takeaway is that AI adoption and hiring are not moving in opposite directions. AI discussions in deals increased 85% over the past two years, while hiring conversations remained relatively stable. That matters because it suggests organizations are not simply looking at AI as a workforce reduction strategy. They are starting to view AI as a capacity multiplier. The companies that will win with AI are not just trying to do the same work with fewer people. They are using AI to increase productivity, expand capacity, and raise the ceiling on what their teams can accomplish.
DGR: How should sellers respond when buyers raise concerns about AI replacing headcount?
Evans: Sellers should not sidestep the concern. Gong Labs found a 237% increase in conversations where AI replacing human work was explicitly mentioned. That tells us this is not a theoretical concern. It is showing up directly in customer conversations. The right response is to separate task displacement from role replacement. AI can take on routine work like drafting, summarizing, logging, routing, researching, and preparing. But that does not eliminate the need for human judgment, customer empathy, creativity, negotiation, or complex decision-making. The best sellers help buyers understand where AI creates leverage. It gives teams more capacity to focus on customers, build stronger relationships, navigate complex decisions, and drive better outcomes. AI should make great people more valuable, not less.
DGR: If AI discussions in deals are up sharply, how should reps adjust their discovery questions and positioning?
Evans: Discovery has to evolve because the buyer has evolved. Buyers are increasingly using AI to research vendors, build shortlists, compare options, and form a point of view before they ever speak with a sales rep. That means the first sales conversation is often no longer an education call. It is a validation call. Reps need to understand what the buyer already knows, what assumptions they have formed, which sources have shaped their thinking, and where they are still seeking confidence. The best discovery questions are shifting from “what problem are you trying to solve?” to “what have you already learned, what are you trying to validate, and what would need to be true for you to move forward?”
DGR: What does the rise in conversations about AI agents mean for sales messaging and product demos?
Evans: Conversations about AI agents have increased 13x over the past two years. That is a clear signal that buyers are moving beyond basic AI assistance and starting to think about systems that can reason, act, and participate in workflows. But the market is still early. Many companies are excited about agents, but they are still trying to understand where agents fit, where humans stay in the loop, and how to measure business impact. For sellers, that means product demos need to move beyond “look what the AI can generate.” Buyers need to see how an agent fits into the actual workflow, what action it can take, where the handoff happens, what judgment remains human-led, and how the outcome is measured. The winning message is not “AI can do this task.” The winning message is “AI can help this team run a better revenue process.”
DGR: How can sales teams address “build vs. buy” objections when prospects think they can create internal AI solutions?
Evans: Build-versus-buy is going to become a much more common conversation. Gong Labs found a 45% increase in discussions about building internal AI solutions. That makes sense. AI has made it easier to create prototypes, and that naturally causes more companies to ask whether they can build internally. The question sellers need to help buyers answer is not, “Can you build a demo?” It is, “Can you operate this at scale?” There is a big difference between a working prototype and a production-grade capability that is secure, integrated, governed, maintained, adopted by the field, and tied to measurable business outcomes. Sales teams can add value by helping buyers evaluate the full investment required: data quality, workflow integration, security, compliance, scalability, change management, maintenance, and time-to-value. In AI, getting something to work once is not the hard part. Making it work reliably across the business is the hard part.
DGR: What should reps do when buyers say they’re using AI to evaluate vendors or shortlist solutions?
Evans: Reps should assume AI is already part of the buying committee. Gong Labs found a 280% increase in buyers disclosing that they are using AI to inform decisions and a 209% increase in AI-assisted vendor evaluation. The actual influence is likely even higher, because many buyers may not explicitly disclose how AI is shaping their research and decision-making. That changes the job of the seller. It is not enough to show up and explain the product. Sellers need to understand what the buyer’s AI-assisted research has surfaced, where the company is being positioned correctly or incorrectly, and what proof points matter most. Customer outcomes, third-party validation, clear differentiation, and a crisp point of view become even more important. If AI is helping buyers narrow the field, sellers need to make sure the buyer has the right evidence to confidently choose them.
DGR: How can sellers differentiate when prospects increasingly discover vendors through AI tools?
Evans: With AI-driven vendor discovery up 250%, differentiation now starts well before the first sales conversation. Buyers are increasingly narrowing their options before they ever engage with a rep. That means generic messaging will get compressed. Clear differentiation will get amplified.
The companies that stand out will be the ones with a distinct market position, strong customer outcomes, credible third-party validation, and a consistent story about the business problems they solve. Sellers then have to reinforce that narrative and make it tangible. The core questions become simple: Why this problem? Why now? Why us? And why will this create measurable business impact?
DGR: What can revenue teams learn from AI-mature organizations that are hiring more aggressively, not less?
Evans: The lesson is that AI maturity is not just about efficiency. It is about ambition. Organizations with the deepest AI adoption are also reporting the strongest hiring plans. That suggests AI is not reducing the need for talent in the highest-performing organizations. It is increasing what those organizations believe they can accomplish. The best companies are using AI to raise the ceiling. They are removing low-value work and redeploying that capacity toward customers, pipeline, expansion, retention, and growth. Revenue leaders should be asking a different question. Not just “How do we reduce cost?” but “What could this team accomplish if we gave our people significantly more leverage?”
DGR: How are budget pressure, seat reduction, and discounting trends changing the way sellers should negotiate AI-related deals?
Evans: AI may create interest, but ROI will determine whether deals close. Gong Labs found increases in discussions about budget pressure, up 41%, discounts, up 41%, and license or seat reductions, up 44%. That tells us buyers may be excited about AI, but they are also scrutinizing spend, challenging scope, and looking for proof. Sellers need to connect AI investments directly to business outcomes. Productivity, retention, pipeline creation, forecast accuracy, rep effectiveness, customer expansion, and time saved all matter. The conversation has to move from features to financial impact. In this market, the winning sellers will not be the ones who create the most AI excitement. They will be the ones who can prove the clearest path from AI adoption to measurable business performance.
DGR: How should sales leaders coach reps to talk about AI as a growth driver rather than just a cost-cutting tool?
Evans: Sales leaders need to coach reps to elevate the AI conversation from efficiency to performance. The AI value narrative has expanded beyond cost reduction. The most AI-mature organizations are also among the most aggressive hirers, suggesting they view AI as a way to expand capacity, accelerate execution, and increase what their teams can accomplish. Cost savings matter, but they are only part of the story. The larger opportunity is helping teams do more of the work that drives growth: better discovery, stronger deal execution, faster follow-up, more consistent coaching, improved customer retention, and more effective expansion. AI should be positioned as a way to increase the capacity and impact of the revenue organization. The best companies are not just asking how AI can help them save time. They are asking how AI can help them compete differently, execute faster, and pursue a larger opportunity. That is the real shift. AI is not just a productivity tool. It is becoming part of the revenue operating system.
