The debate over whether AI belongs in the corporate boardroom appears to be over — at least for those responsible for generating revenue.
Seven in 10 enterprise revenue leaders now trust AI to regularly inform their business decisions, according to a sweeping new study released by revenue intelligence company Gong. The finding marks a dramatic shift from just two years ago, when most organizations treated AI as an experimental technology relegated to pilot programs and individual productivity hacks.
The research, based on an analysis of 7.1 million sales opportunities across more than 3,600 companies and a survey of over 3,000 global revenue leaders spanning the U.S., UK, Australia and Germany, paints a picture of an industry in rapid transformation. Organizations that have embedded AI into their core go-to-market strategies are 65% more likely to increase their win rates than competitors still treating the technology as optional.
"I don't think people delegate decisions to AI, but they do rely on AI in the process of making decisions," Amit Bendov, Gong's co-founder and chief executive, said in an exclusive interview with VentureBeat. "Humans are making the decision, but they're largely assisted."
The distinction matters. Rather than replacing human judgment, AI has become what Bendov describes as a "second opinion" — a data-driven check on the intuition and guesswork that has traditionally governed sales forecasting and strategy.
Slowing growth is forcing sales teams to squeeze more from every rep
The timing of AI's ascendance in revenue organizations is no coincidence. The study reveals a sobering reality: After rebounding in 2024, average annual revenue growth among surveyed companies decelerated to 16% in 2025, marking a three-percentage-point decline year over year. Sales rep quota attainment fell from 52% to 46% over the same period.
The culprit, according to Gong's analysis, isn't that salespeople are performing worse on individual deals. Win rates and deal duration remained consistent. The problem is that representatives are working fewer opportunities — a finding that suggests operational inefficiencies are eating into selling time.
This helps explain why productivity has rocketed to the top of executive priorities. For the first time in the study's history, increasing the productivity of existing teams ranked as the number-one growth strategy for 2026, jumping from fourth place the previous year.
"The focus is on increasing sales productivity," Bendov said. "How much dollar-output per dollar-input?"
The numbers back up the urgency. Teams that regularly use AI tools generate 77% more revenue per representative than those that don't — a gap Gong characterizes as a six-figure difference per salesperson annually.
Companies are moving beyond basic AI automation toward strategic decision-making
The nature of AI adoption in sales has evolved considerably over the past year. In 2024, most revenue teams used AI for basic automation: Transcribing calls, drafting emails, updating CRM records. Those use cases continue to grow, but 2025 marked what the report calls a shift "from automation to intelligence."
The number of U.S. companies using AI for forecasting and measuring strategic initiatives jumped 50% year over year. These more sophisticated applications — predicting deal outcomes, identifying at-risk accounts, measuring which value propositions resonate with different buyer personas — correlate with dramatically better results.
Organizations in the 95th percentile of commercial impact from AI were 2 to 4X more likely to have deployed these strategic use cases, according to the study.
Bendov offered a concrete example of how this plays out in practice. "Companies have thousands of deals that they roll up into their forecast," he said. "It used to be based solely on human sentiment, believe it or not. That's why a lot of companies miss their numbers: Because people say, 'Oh, he told me he'll buy,' or 'I think I can probably get this one.'"
AI changes that calculus by examining evidence rather than optimism. "Companies now get a second opinion from AI on their forecasting, and that improves forecasting accuracy dramatically — 10 [or] 15% better accuracy just because it's evidence-based, not just based on human sentiment," Bendov said.
Revenue-specific AI tools are dramatically outperforming general-purpose alternatives
One of the study's more provocative findings concerns the type of AI that delivers results. Teams using revenue-specific AI solutions — tools built explicitly for sales workflows rather than general-purpose platforms like ChatGPT — reported 13% higher revenue growth and 85% greater commercial impact than those relying on generic tools.
These specialized systems were also twice as likely to be deployed for forecasting and predictive modeling, the report found.
The finding carries obvious implications for Gong, which sells precisely this type of domain-specific platform. But the data suggests a real distinction in outcomes. General-purpose AI, while more prevalent, often creates what the report describes as a "blind spot" for organizations — particularly when employees adopt consumer AI tools without company oversight.
Research from MIT suggests that while only 59% of enterprise teams use personal AI tools like ChatGPT at work, the actual figure is likely closer to 90%. This shadow AI usage poses security risks and creates fragmented technology stacks that undermine the potential for organization-wide intelligence.
Most sales leaders believe AI will reshape their jobs rather than eliminate them
Perhaps the most closely-watched question in any AI study concerns employment. The Gong research offers a more nuanced picture than the apocalyptic predictions that often dominate headlines.
When asked about AI's three-year impact on revenue headcount, 43% of respondents said they expect it to transform jobs without reducing headcount — the most common response. Only 28% anticipate job eliminations, while 21% actually foresee AI creating new roles. Just 8% predict minimal impact.
Bendov frames the opportunity as reclaiming lost time. He cited Forrester research indicating that 77% of a sales representative's time is spent on activities that don't involve customers — administrative work, meeting preparation, researching accounts, updating forecasts and internal briefings.
"AI can eliminate, ideally, 77% of the drudgery work that they're doing," Bendov said. "I don't think it necessarily eliminates jobs. People are half productive right now. Let's make them fully productive, and whatever you're paying them will translate to much higher revenue."
The transformation is already visible in role consolidation. Over the past decade, sales organizations splintered into hyper-specialized functions: One person qualifies leads, another sets appointments, a third closes deals, a fourth handles onboarding. The result was customers interacting with five or six different people across their buying journey.
"Which is not a great buyer experience, because every time I meet a new person that might not have the full context, and it's very inefficient for companies," Bendov said. "Now with AI, you can have one person do all this, or much of this."
At Gong itself, sellers now generate 80% of their own appointments because AI handles the prospecting legwork, Bendov said.
American companies are adopting AI 18 months faster than their European counterparts
The study reveals a notable divide in AI adoption between the U.S. and Europe. While 87% of U.S. companies now use AI in their revenue operations, with another 9% planning adoption within a year, the UK trails by 12 to 18 months. Just 70% of UK companies currently use AI, with 22% percent planning near-term adoption — figures that mirror U.S. data from 2024.
Bendov said the pattern reflects a broader historical tendency for enterprise technology trends to cross the Atlantic with a delay. "It's always like that," he said. "Even when the internet was taking off in the U.S., Europe was a step behind."
The gap isn't permanent, he noted, and Europe sometimes leads on technology adoption — mobile payments and messaging apps like WhatsApp gained traction there before the U.S. — but for AI specifically, the American market remains ahead.
Gong says a decade of AI development gives it an edge over Salesforce and Microsoft
The findings arrive as Gong navigates an increasingly crowded market. The company, which recently surpassed $300 million in annual recurring revenue, faces potential competition from enterprise software giants like Salesforce and Microsoft, both of which are embedding AI capabilities into their platforms.
Bendov argues that Gong's decade of AI development creates a substantial barrier to entry. The company's architecture comprises three layers: a "revenue graph" that aggregates customer data from CRM systems, emails, calls, videos and web signals; an intelligence layer combining large language models (LLMs) with approximately 40 proprietary small language models; and workflow applications built on top.
"Anybody that would want to build something like that — it's not a small feature, it's 10 years in development—would need first to build the revenue graph," Bendov said.
Rather than viewing Salesforce and Microsoft as threats, Bendov characterized them as partners, pointing to both companies' participation in Gong's recent user conference to discuss agent interoperability. The rise of MCP (Model Context Protocol) support and consumption-based pricing models means customers can mix AI agents from multiple vendors rather than committing to a single platform.
The real question is whether AI will expand the sales profession or hollow it out
The report's implications extend beyond sales departments. If AI can transform revenue operations — long considered a relationship-driven, human-centric function — it raises questions about which other business processes might be next.
Bendov sees the potential for expansion rather than contraction. Drawing an analogy to digital photography, he noted that while camera manufacturers suffered, the total number of photos taken exploded once smartphones made photography effortless.
"If AI makes selling simple, I could see a world [with] maybe ten times more jobs than we have now," said Bendov." It's expensive and inefficient today, but if it becomes as easy as taking a photo, the industry could actually grow and create opportunities for people of different abilities, from different locations."
For Bendov, who co-founded Gong in 2015 when AI was still a hard sell to non-technical business users, the current moment represents something he waited a decade to see. Back then, mentioning AI to sales executives sounded like science fiction. The company struggled to raise money because the underlying technology barely existed.
"When we started the company, we were born as an AI company, but we had to almost hide AI," Bendov recalled. "It was intimidating."
Now, seven out of 10of those same executives say they trust AI to help run their business. The technology that once had to be disguised has become the one thing nobody can afford to ignore.
