Boosting Sales Revenue: How AI-Empowered Sales Teams Drive 77% Higher Revenue per Rep

The debate over whether artificial intelligence belongs in the corporate boardroom appears to be over — at least for the people responsible for generating revenue.
Seven in ten enterprise revenue leaders now trust AI to regularly inform their business decisions, according to a sweeping new 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 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 United States, United Kingdom, 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 percent 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 percent in 2025, marking a three-percentage-point decline year over year. Sales rep quota attainment fell from 52 percent to 46 percent 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 where sellers regularly use AI tools generate 77 percent 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 percent 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 two to four times 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 percent 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 percent higher revenue growth and 85 percent 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 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 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 percent of respondents said they expect it to transform jobs without reducing headcount — the most common response. Only 28 percent anticipate job eliminations, while 21 percent actually foresee AI creating new roles. Just 8 percent predict minimal impact.
Bendov frames the opportunity in terms of 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 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 last ten years, sales organizations have evolved into highly specialized functions, with different individuals handling tasks such as lead qualification, appointment setting, deal closing, and onboarding. This led to customers having to interact with multiple people throughout their buying journey, resulting in a fragmented experience. However, with the advent of AI, companies now have the ability to streamline these processes and provide a more cohesive customer experience.

At Gong, AI has revolutionized the sales process by enabling sellers to generate 80% of their own appointments. This automation of prospecting tasks has significantly increased efficiency and effectiveness in reaching potential customers.

A recent study has highlighted a discrepancy in AI adoption rates between American and European companies. While the United States leads with 87% of companies using AI in their revenue operations, the United Kingdom lags behind by 12 to 18 months. This delay in adoption is not uncommon, as historical trends have shown a similar pattern with new technologies.

Gong believes that its decade-long investment in AI development gives it a competitive edge over industry giants like Salesforce and Microsoft. The company’s sophisticated architecture, which includes a "revenue graph" and intelligence layer powered by proprietary language models, sets it apart in the market. This deep expertise in AI technology creates a significant barrier to entry for potential competitors.

Rather than viewing Salesforce and Microsoft as threats, Gong sees them as potential partners in the evolving AI landscape. The company’s focus on interoperability and flexible pricing models allows customers to leverage AI solutions from multiple vendors, enhancing their overall sales operations.

The rise of AI in sales raises questions about the future of the profession. Will AI expand opportunities for sales professionals, or will it lead to job displacement? Gong’s CEO envisions a scenario where AI simplifies the selling process, potentially creating more job opportunities and diversity in the industry. By making selling more efficient and accessible, AI could lead to a growth in the sales profession rather than a decline.

For Gong, the widespread acceptance of AI represents a long-awaited milestone. The company’s early focus on AI technology, despite initial skepticism, has now become a cornerstone of its success. As more businesses embrace AI as a valuable tool in running their operations, Gong continues to lead the way in leveraging AI for sales excellence.