The integration of artificial intelligence (AI) into corporate strategies has reached a pivotal moment, particularly in the realm of sales. A recent study conducted by Gong, a revenue intelligence company, highlights a significant transformation in how sales teams leverage AI to enhance their performance and drive revenue growth. The findings reveal that organizations employing AI tools generate an astonishing 77% more revenue per sales representative compared to those that do not utilize such technologies. This statistic underscores the growing recognition among enterprise revenue leaders of AI’s potential to inform business decisions and optimize sales processes.
Historically, AI was often viewed as an experimental technology, relegated to pilot programs and individual productivity hacks. However, the landscape has shifted dramatically over the past two years. According to Gong’s research, seven out of ten enterprise revenue leaders now trust AI to regularly inform their business decisions. This marks a significant departure from previous attitudes, where AI was seen as a novelty rather than a necessity. The study analyzed over 7.1 million sales opportunities across more than 3,600 companies and surveyed over 3,000 global revenue leaders from countries including the United States, United Kingdom, Australia, and Germany. The results paint a picture of an industry undergoing rapid transformation, with organizations embedding AI into their core go-to-market strategies.
One of the most striking revelations from the study is the correlation between AI adoption and increased win rates. Organizations that have integrated AI into their sales strategies are 65% more likely to improve their win rates compared to competitors who treat AI as an optional tool. Amit Bendov, co-founder and CEO of Gong, emphasizes that while humans ultimately make decisions, they increasingly rely on AI as a “second opinion”—a data-driven check on intuition and guesswork that has traditionally governed sales forecasting and strategy. This shift signifies a move away from reliance solely on human sentiment, which has historically led to inaccuracies in forecasting and decision-making.
The timing of AI’s rise within revenue organizations is not coincidental. The Gong study reveals a sobering reality: after a rebound in 2024, average annual revenue growth among surveyed companies decelerated to 16% in 2025, marking a three-percentage-point decline year over year. Additionally, sales representatives’ quota attainment fell from 52% to 46% during the same period. Interestingly, the analysis indicates that the decline in performance is not due to salespeople performing worse on individual deals; rather, it stems from representatives working on fewer opportunities. This finding suggests that operational inefficiencies are encroaching on valuable selling time, prompting organizations to prioritize productivity as a critical growth strategy.
For the first time in the study’s history, increasing the productivity of existing sales teams emerged as the number-one growth strategy for 2026, rising from fourth place the previous year. Bendov notes that the focus is shifting toward maximizing dollar output per dollar input. The urgency of this shift is underscored by the fact that teams utilizing AI tools generate significantly higher revenue per representative—an annual difference that Gong characterizes as a six-figure gap per salesperson.
As organizations evolve in their use of AI, the nature of its adoption has also changed. In 2024, many revenue teams primarily employed AI for basic automation tasks such as transcribing calls, drafting emails, and updating customer relationship management (CRM) records. While these use cases continue to grow, 2025 marked a notable shift from mere automation to strategic intelligence. The number of U.S. companies using AI for forecasting and measuring strategic initiatives surged by 50% year over year. These advanced applications—predicting deal outcomes, identifying at-risk accounts, and assessing which value propositions resonate with different buyer personas—are correlated with significantly better results.
Organizations that fall within the 95th percentile of commercial impact from AI are two to four times more likely to have deployed these strategic use cases. Bendov provides a concrete example of how this shift plays out in practice. Traditionally, companies relied heavily on human sentiment to roll up thousands of deals into their forecasts. This approach often led to missed targets, as salespeople would express optimism based on personal interactions rather than hard data. AI changes this dynamic by analyzing evidence rather than relying on subjective optimism. Companies can now obtain a second opinion from AI on their forecasting, resulting in improved accuracy—often by 10% to 15%—simply because the forecasts are based on data rather than sentiment.
Another key finding from the Gong study pertains to the type of AI tools that yield the best results. Teams utilizing revenue-specific AI solutions—tools designed explicitly for sales workflows—reported 13% higher revenue growth and 85% greater commercial impact compared to those relying on general-purpose platforms like ChatGPT. These specialized systems are also twice as likely to be deployed for forecasting and predictive modeling. This distinction carries significant implications for Gong, which markets precisely this type of domain-specific platform. The data suggests that while general-purpose AI tools are more prevalent, they often create “blind spots” for organizations, especially when employees adopt consumer AI tools without proper oversight.
Research from MIT indicates that while only 59% of survey respondents claim their teams use personal AI tools like ChatGPT at work, the actual figure may be closer to 90%. This phenomenon, referred to as “shadow AI,” poses security risks and creates fragmented technology stacks that undermine the potential for organization-wide intelligence. As organizations grapple with the implications of shadow AI, the need for robust governance and oversight becomes increasingly apparent.
The question of employment in the age of AI remains a focal point of concern. The Gong research offers a nuanced perspective that contrasts with the apocalyptic predictions often associated with AI’s rise. When asked about AI’s potential impact on revenue headcount over the next three years, 43% of respondents indicated they expect AI to transform jobs without reducing headcount—the most common response. Only 28% anticipate job eliminations, while 21% foresee the creation of new roles. Just 8% predict minimal impact. Bendov frames the opportunity in terms of reclaiming lost time, citing research indicating that 77% of a sales representative’s time is spent on non-customer-facing activities—administrative work, meeting preparation, account research, updating forecasts, and internal briefings.
Bendov argues that AI has the potential to eliminate much of this drudgery, allowing sales representatives to focus on high-value activities. He believes that rather than eliminating jobs, AI can enhance productivity, enabling sales professionals to operate at full capacity and translate their efforts into significantly higher revenue. The transformation is already evident in the consolidation of roles within sales organizations. Over the past decade, sales functions have splintered into hyper-specialized roles, resulting in customers interacting with multiple representatives throughout their buying journey. This fragmentation often leads to inefficiencies and a suboptimal buyer experience, as each new interaction lacks the full context of previous discussions.
With AI, organizations can streamline these processes, allowing one representative to handle multiple aspects of the sales cycle. At Gong, for instance, sellers now generate 80% of their own appointments because AI manages the prospecting legwork. This shift not only enhances efficiency but also improves the overall customer experience by providing continuity and context throughout the sales process.
Geographically, the study reveals a notable divide in AI adoption between the United States and Europe. While 87% of U.S. companies currently utilize AI in their revenue operations, with an additional 9% planning to adopt AI within the next year, the United Kingdom lags behind by 12 to 18 months. Only 70% of UK companies report using AI, with 22% planning near-term adoption—figures that mirror U.S. data from 2024. Bendov attributes this pattern to a historical tendency for enterprise technology trends to cross the Atlantic with a delay. While Europe occasionally leads in technology adoption—such as mobile payments and messaging apps—the American market currently maintains a lead in AI implementation.
As Gong navigates an increasingly crowded market, the company faces competition from enterprise software giants like Salesforce and Microsoft, both of which are embedding AI capabilities into their platforms. However, Bendov argues that Gong’s decade-long investment in AI development provides a substantial barrier to entry for competitors. The company’s architecture comprises three layers: a “revenue graph” that aggregates customer data from various sources, an intelligence layer that combines large language models with approximately 40 proprietary small language models, and workflow applications built on top of this foundation.
Rather than viewing Salesforce and Microsoft as threats, Bendov characterizes them as partners, highlighting their participation in Gong’s recent user conference to discuss agent interoperability. The emergence of Model Context Protocol (MCP) support and consumption-based pricing models allows customers to mix AI agents from multiple vendors, reducing the need to commit to a single platform.
The overarching question remains: will AI expand the sales profession or hollow it out? Bendov envisions a future where AI simplifies selling, potentially leading to a tenfold increase in job opportunities within the industry. He draws an analogy to digital photography, noting that while camera manufacturers faced challenges, the total number of photos taken skyrocketed once smartphones made photography accessible to the masses. If AI can streamline the sales process, it could open doors for individuals with diverse abilities and backgrounds, creating opportunities for a broader range of talent.
For Bendov, who co-founded Gong in 2015 when AI was still a hard sell to non-technical business users, the current moment represents a culmination of a decade-long journey. In the early days, discussing AI with sales executives felt like venturing into science fiction territory. The company struggled to secure funding as the underlying technology was still in its infancy. Today, however, seven out of ten executives express trust in AI’s ability to assist in running their businesses. The technology that once had to be concealed has become indispensable, and organizations can no longer afford to ignore its potential.
In conclusion, the Gong study serves as a clarion call for organizations to embrace AI as a fundamental component of their sales strategies. The evidence is clear: sales teams leveraging AI tools not only outperform their peers but also position themselves for sustained growth in an increasingly competitive landscape. As AI continues to evolve, its