The debate over whether artificial intelligence belongs in the corporate boardroom seems to be over – at least for those responsible for generating revenue.
Seven out of ten enterprise revenue leaders now rely on AI to regularly inform their business decisions, according to a comprehensive new study released Thursday by revenue intelligence company Gong. This finding marks a dramatic change from just two years ago, when most organizations treated AI as an experimental technology for pilot programs and personal productivity hacks.
The research, based on an analysis of 7.1 million sales opportunities across more than 3,600 companies and a survey of more than 3,000 global revenue leaders spanning the United States, United Kingdom, Australia and Germany, paints a picture of an industry in rapid change. Organizations that have incorporated AI into their core market strategy are 65 percent more likely to see increased win rates than competitors who are still treating the technology as optional.
"I don’t think people delegate decisions to AI, but they trust AI in the decision-making process," Amit Bendov, co-founder and chief executive of Gong, said in an exclusive interview with VentureBeat. "Humans are making the decisions, but they are assisted extensively."
Difference matters. Rather than replacing human judgment, AI has become what Bendov describes as a "second opinion" – A data-driven investigation into the intuition and guesswork that have traditionally governed sales forecasting and strategy.
Slow growth is forcing sales teams to squeeze more from each rep
The timing of the rise of AI in revenue organizations is no coincidence. The study highlights a sobering reality: After a rebound in 2024, average annual revenue growth among the companies surveyed dropped to 16 percent in 2025, representing a three percent year-on-year decline. During the same period, sales representative quota attainment fell from 52 percent to 46 percent.
According to Gong’s analysis, the culprit isn’t that sellers are performing poorly on individual deals. The win rate and deal duration remained consistent. The problem is that reps are working on fewer opportunities – a finding that suggests operational inefficiencies are wasting sales time.
This helps explain why productivity has risen to the top of executive priorities. For the first time in the study’s history, increasing the productivity of existing teams was ranked as the number one growth strategy for 2026, up from fourth place last year.
"Focus is on increasing sales productivity" Bendov said. "How many dollars of output per dollar of input."
The numbers support the urgency. Teams where salespeople regularly use AI tools generate 77 percent more revenue per rep than teams that don’t — a gap Gong characterizes as a six-figure difference per salesperson per year.
Companies are moving beyond basic AI automation to strategic decision making
The nature of AI adoption in sales has evolved significantly over the past year. In 2024, most revenue teams use AI for basic automation: transcribing calls, drafting emails, updating CRM records. Those use cases continue to grow, but the report says that will change in 2025 "From automation to intelligence."
The number of US companies using AI to forecast and measure strategic initiatives has increased by 50 percent year over year. These more sophisticated applications – predicting deal outcomes, identifying at-risk accounts, measuring which value propositions resonate with different buyer personas – are correlated with dramatically better outcomes.
According to the study, organizations with the 95th percentile of business impact from AI were two to four times more likely to deploy these strategic use cases.
Bendov offered a concrete example of how this applies in practice. "Companies have thousands of deals that they include in their forecasts," He said. "It used to be based entirely on human emotions – believe it or not. This is why a lot of companies forget their numbers: because people say, ‘Oh, he told me he’d buy it,’ or ‘I think I could probably buy it.’"
AI changes that calculation by examining evidence instead of optimism. "Companies now get a second opinion from AI on their forecasts, and this dramatically improves forecast accuracy – 10 [or] 15 percent better accuracy just because it’s evidence-based, not just based on human emotion," 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 producing results. Teams using revenue-specific AI solutions – tools built explicitly for sales workflows, rather than general-purpose platforms like ChatGPT – reported 13 percent greater revenue growth and 85 percent greater business impact than those relying on generic tools.
The report found that these specialized systems were twice as likely to be deployed for forecasting and predictive modeling.
This finding has clear implications for Gong, which sells exactly this type of domain-specific platform. But the data suggests real differences in outcomes. General purpose AI, while more prevalent, often creates exactly what is described in the report "blind spot" For organizations – especially when employees adopt consumer AI tools without company oversight.
Research from MIT shows that while only 59 percent of survey respondents said their teams use personal AI tools like ChatGPT at work, the actual figure is likely closer to 90 percent. This shadow AI use creates security risks and creates fragmented technology stacks that weaken organization-wide intelligence capabilities.
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. Gong’s research paints a more nuanced picture than the apocalyptic predictions that often make headlines.
When asked about the three-year impact of AI on revenue headcount, 43 percent of respondents said they expect it to replace jobs without reducing headcount – the most common response. Only 28 percent anticipate jobs being lost, while 21 percent actually expect AI to create new roles. Only 8 percent predicted minimal impact.
Bendov framed the opportunity in terms of reclaiming lost time. He cited Forrester research indicating that 77 percent of sales reps’ time is spent on activities that do not involve customers – administrative tasks, meeting preparation, researching accounts, updating forecasts and internal briefings.
"AI could, ideally, eliminate all 77 percent – all the hard 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 into a lot of revenue."
The change in role consolidation is already visible. Over the past decade, sales organizations fragmented into hyper-specialized functions: One person qualifies leads, another schedules appointments, a third closes deals, a fourth handles onboarding. The result was that customers interacted with five or six different people during their shopping journey.
"Which is not a good buyer experience, because every time I meet someone new they may not have full context, and it’s very inefficient for companies," Bendov said. "Now with AI, you can have one person do all or much of this."
At Gong itself, sellers now do 80 percent of their recruiting themselves as AI handles prospecting tasks, Bendov said.
US companies are adopting AI 18 months faster than their European counterparts
The study reveals a notable divide in AI adoption between the United States and Europe. While 87 percent of US companies now use AI in their revenue functions, another 9 percent of companies plan to adopt it within a year, with the United Kingdom lagging behind by 12 to 18 months. Only 70 percent of UK companies currently use AI, with 22 percent planning to adopt it in the near term – these figures mirror US data for 2024.
Bendov said this pattern reflects a broader historical trend of late crossing the Atlantic for enterprise technology trends. "It’s always like this," He said. "Even as the Internet was taking off in America, Europe was a step behind."
This gap is not permanent, he said, and Europe is sometimes ahead in technology adoption – mobile payment and messaging apps like WhatsApp gained popularity there before the US – but for AI in particular, the US market remains ahead.
Gong says a decade of AI development gives it an edge over Salesforce and Microsoft
The findings come as Gong enters 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 huge barrier to entry. Company architecture consists of three layers: a "revenue graph" which collects customer data from CRM systems, emails, calls, videos and web signals; An intelligence layer combining large language models with approximately 40 proprietary smaller language models; And workflow applications built on top.
"Anyone who wants to build something like this—this is no small feature, it took 10 years to develop—the first thing they have to do is create a revenue graph," Bendov said.
Rather than seeing Salesforce and Microsoft as threats, Bendov portrayed them as partners, pointing to the two 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 implications of the report go beyond sales departments. If AI can transform revenue operations – long considered a relationship-driven, human-centric function – then it raises questions about what business processes might be next.
Bendov sees potential for expansion rather than contraction. Comparing digital photography, he said that while camera manufacturers suffered losses, the total number of photos taken increased after smartphones made photography easier.
"I can see a world if AI makes sales easier—I don’t know exactly what it looks like yet—but why not?" Bendov said. "We probably have ten times more jobs than we have now. Today this is expensive and ineffective, but if it became as easy as taking a photo, the industry could really grow and create opportunities for people with 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 had waited a decade to see. At the time, mentioning AI to sales executives seemed like science fiction. The company struggled to raise funding as the underlying technology barely existed.
"When we started the company, we were born as an AI company, but we almost had to hide AI," Bendov recalled. "It was scary."
Now, seven in ten of those same executives say they rely on AI to help run their businesses. Technology that once had to be hidden has become something that no one can ignore.
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