The controversy over whether artificial intelligence belongs in the company boardroom appears to be over — a minimum of for the people accountable for generating revenue.
Seven in ten enterprise revenue leaders now trust AI to recurrently inform their business decisions, in keeping with a sweeping recent study released Thursday by Gong, the revenue intelligence company. 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 evaluation of seven.1 million sales opportunities across greater than 3,600 firms and a survey of over 3,000 global revenue leaders spanning the US, United Kingdom, Australia, and Germany, paints an image of an industry in rapid transformation. Organizations which have embedded AI into their core go-to-market strategies are 65 percent more prone 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 depend on AI within the strategy of making decisions," Amit Bendov, Gong's co-founder and chief executive, said in an exclusive interview with VentureBeat. "Humans are making the choice, but they're largely assisted."
The excellence matters. Slightly than replacing human judgment, AI has grow to be 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 not any coincidence. The study reveals a sobering reality: after rebounding in 2024, average annual revenue growth amongst surveyed firms decelerated to 16 percent in 2025, marking a three-percentage-point decline 12 months over 12 months. Sales rep quota attainment fell from 52 percent to 46 percent over the identical period.
The perpetrator, in keeping with Gong's evaluation, isn't that salespeople are performing worse on individual deals. Win rates and deal duration remained consistent. The issue is that representatives are working fewer opportunities—a finding that implies operational inefficiencies are eating into selling time.
This helps explain why productivity has rocketed to the highest of executive priorities. For the primary time within the study's history, increasing the productivity of existing teams ranked because the number-one growth strategy for 2026, jumping from fourth place the previous 12 months.
"The main focus is on increasing sales productivity," Bendov said. "How much dollar-output per dollar-input."
The numbers back up the urgency. Teams where sellers recurrently use AI tools generate 77 percent more revenue per representative than those who don't — a niche Gong characterizes as a six-figure difference per salesperson annually.
Corporations are moving beyond basic AI automation toward strategic decision-making
The character of AI adoption in sales has evolved considerably over the past 12 months. In 2024, most revenue teams used AI for basic automation: transcribing calls, drafting emails, updating CRM records. Those use cases proceed to grow, but 2025 marked what the report calls a shift "from automation to intelligence."
The variety of U.S. firms using AI for forecasting and measuring strategic initiatives jumped 50 percent 12 months over 12 months. These more sophisticated applications — predicting deal outcomes, identifying at-risk accounts, measuring which value propositions resonate with different buyer personas — correlate with dramatically higher results.
Organizations within the ninety fifth percentile of business impact from AI were two to 4 times more prone to have deployed these strategic use cases, in keeping with the study.
Bendov offered a concrete example of how this plays out in practice. "Corporations have 1000’s of deals that they roll up into their forecast," he said. "It was once based solely on human sentiment—imagine it or not. That's why a number of firms miss their numbers: because people say, 'Oh, he told me he'll buy,' or 'I feel I can probably get this one.'"
AI changes that calculus by examining evidence moderately than optimism. "Corporations now get a second opinion from AI on their forecasting, and that improves forecasting accuracy dramatically — 10 [or] 15 percent higher accuracy simply because it's evidence-based, not only based on human sentiment," Bendov said.
Revenue-specific AI tools are dramatically outperforming general-purpose alternatives
Certainly one of the study's more provocative findings concerns the sort of AI that delivers results. Teams using revenue-specific AI solutions — tools built explicitly for sales workflows moderately than general-purpose platforms like ChatGPT — reported 13 percent higher revenue growth and 85 percent greater business impact than those counting on generic tools.
These specialized systems were also twice as prone to be deployed for forecasting and predictive modeling, the report found.
The finding carries obvious implications for Gong, which sells precisely this kind of domain-specific platform. But the info suggests an actual 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 percent of survey respondents said their teams use personal AI tools like ChatGPT at work, the actual figure is probably going closer to 90 percent. This shadow AI usage poses security risks and creates fragmented technology stacks that undermine the potential for organization-wide intelligence.
Most sales leaders imagine AI will reshape their jobs moderately than eliminate them
Perhaps essentially the most closely watched query in any AI study concerns employment. The Gong research offers a more nuanced picture than the apocalyptic predictions that usually dominate headlines.
When asked about AI's three-year impact on revenue headcount, 43 percent of respondents said they expect it to rework jobs without reducing headcount — essentially the most common response. Only 28 percent anticipate job eliminations, while 21 percent actually foresee AI creating recent roles. Just 8 percent predict minimal impact.
Bendov frames the chance when it comes to reclaiming lost time. He cited Forrester research indicating that 77 percent 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, all 77 percent—all of the drudgery work that they're doing," Bendov said. "I don't think it necessarily eliminates jobs. Persons are half productive at once. 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, one other sets appointments, a 3rd closes deals, a fourth handles onboarding. The result was customers interacting with 5 – 6 different people across their buying journey.
"Which will not be an awesome buyer experience, because each time I meet a brand new person who won’t have the complete context, and it's very inefficient for firms," Bendov said. "Now with AI, you possibly can have one person do all this, or much of this."
At Gong itself, sellers now generate 80 percent of their very own appointments because AI handles the prospecting legwork, Bendov said.
American firms are adopting AI 18 months faster than their European counterparts
The study reveals a notable divide in AI adoption between the US and Europe. While 87 percent of U.S. firms now use AI of their revenue operations, with one other 9 percent planning adoption inside a 12 months, the UK trails by 12 to 18 months. Just 70 percent of UK firms 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 at all times like that," he said. "Even when the web was taking off within the US, Europe was a step behind."
The gap isn't everlasting, 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 stays 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 corporate, which recently surpassed $300 million in annual recurring revenue, faces potential competition from enterprise software giants like Salesforce and Microsoft, each of that are embedding AI capabilities into their platforms.
Bendov argues that Gong's decade of AI development creates a considerable barrier to entry. The corporate'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 with roughly 40 proprietary small language models; and workflow applications built on top.
"Anybody that will wish to construct something like that—it's not a small feature, it's 10 years in development—would want first to construct the revenue graph," Bendov said.
Slightly than viewing Salesforce and Microsoft as threats, Bendov characterised them as partners, pointing to each firms' participation in Gong's recent user conference to debate agent interoperability. The rise of MCP (Model Context Protocol) support and consumption-based pricing models means customers can mix AI agents from multiple vendors moderately than committing to a single platform.
The actual query is whether or not AI will expand the sales career or hole 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 on which other business processes is likely to be next.
Bendov sees the potential for expansion moderately than contraction. Drawing an analogy to digital photography, he noted that while camera manufacturers suffered, the full variety of photos taken exploded once smartphones made photography effortless.
"If AI makes selling easy, I could see a world—I don't know exactly what it looks like yet—but why not?" Bendov said. "Possibly ten times more jobs than we have now now. It's expensive and inefficient today, but when it becomes as easy as taking a photograph, the industry could actually grow and create opportunities for people of various abilities, from different locations."
For Bendov, who co-founded Gong in 2015 when AI was still a tough sell to non-technical business users, the present moment represents something he waited a decade to see. Back then, mentioning AI to sales executives appeared like science fiction. The corporate struggled to lift money since the underlying technology barely existed.
"After we began the corporate, we were born as an AI company, but we needed to almost hide AI," Bendov recalled. "It was intimidating."
Now, seven out of ten of those self same executives say they trust AI to assist run their business. The technology that after needed to be disguised has grow to be the one thing no one can afford to disregard.
