2026 State of Revenue AI

Following a strong rebound in 2024, average annual revenue growth decelerated to 16% in 2025, marking a 3-point decline year-over-year for US organizations.

While increased investments in big AI players drove economic optimism across headlines this year, rising costs, weakening demand, and trade uncertainty have had a substantial impact on revenue performance.

This trend signals a turning point: traditional revenue playbooks and tactics are no longer sufficient in a market shaped by data complexity, evolving buyer expectations, and heightened demands for efficiency.

In 2024, leaders reported that an average of 52% of their reps attained or exceeded their quota¹. This year that number dropped to 46%.

A Gong Labs² analysis of more than 7.1 million opportunities from 3,613 organizations highlights why this performance has leveled off.

This data suggests that the dip in revenue growth and rep participation is due to sellers getting fewer attempts.

While average win rates and deal duration remained consistent, reps worked fewer opportunities in 2025.

To reignite growth in 2025, leaders shift their focus from customer and market expansion to maximizing team output. Productivity and efficiency now top the list of growth levers for 2026.

While productivity has remained an evergreen priority for revenue leaders, this is the first time it has ranked number one.

This is a clear signal that to outperform in 2026 and beyond, revenue teams will need to reengineer their organizations, processes, and roles to maximize the output of their existing resources.

Leaders should treat their productivity initiatives with the same attention, rigor, and measurement as a new product launch or geographic expansion.

While AI adoption is wide, it's ultimately how deeply it's adopted that dictates a return on investment.

For example, organizations with AI as a core part of their strategy in 2025 reported 31% higher revenue growth than those teams currently running limited pilots¹.

Globally, this same group was 65% more likely to increase win rates in 2025.

These same teams also reported 2.6x higher commercial impact scores².

When looking at the global data, Australian teams are further along on their AI journey, reporting the deepest adoption across revenue teams.

As AI adoption increased this past year, so did the number of revenue-specific use cases rolled out.

In 2024, a majority of revenue teams utilized AI to automate tasks such as note-taking, content creation, data entry, and other similar tasks.

While these use cases continue to grow, 2025 was characterized by an uptick in intelligence delivered by AI. For example, the number of organizations using AI to forecast and measure the success of their strategic initiatives both increased by 50% in 2025.

Instead of fueling individual productivity via efficiency, these types of strategic use cases create wider productivity gains by making the entire organization more effective.

Why is this important? Organizations falling within the 95th percentile of commercial impact¹ were two to four times more likely to have these use cases in place.

Teams using revenue-specific solutions¹ were twice as likely to report using AI for forecasting and predictive modeling.

These solutions are also driving the highest impact. Adoption of revenue-specific AI correlates with 13% higher revenue growth and 85% higher commercial impact.

However, data shows that general-purpose AI is much more prevalent across the industry, with enterprise general-purpose AI being the most common, followed by general-purpose tools used by individuals (e.g. a rep using ChatGPT at work).

Interestingly, while only 59% of respondents indicated that their team was using these personal tools, research from MIT² suggests that this number is closer to 90%.

This blind spot poses serious challenges for organizations, including security risks and disparate tech stacks.

Top-performing companies aren't just using AI; they're consulting it.214

69% of all revenue leaders trust the insights delivered to them by AI. As a result, more revenue organizations are beginning to leverage AI insights to make critical business decisions.

In fact, more frequent use of AI correlated with a higher commercial impact. These teams report higher trust in AI outputs and a measurable boost in forecasting accuracy and strategic clarity.

Leaders in the UK reported the lowest amount of trust in AI, with 12% being skeptical, compared with 2.7% of Germans, 3% of Australians and 0% of US. This may correlate with the slower adoption of AI in the UK.

Last year, 92% of respondents reported that AI saved their sellers time, measuring the total number of hours saved by automating tasks and workflows with AI.

This year, leaders looked beyond hours saved and emphasized revenue generated per sales rep, providing a measure of both efficiency and effectiveness.

In our own Gong Labs analysis of 7.1 million opportunities worked in 2025, we examined how seller utilization of AI impacted revenue per rep.

Teams in which sellers were frequent AI users generated 77% more revenue per sales representative than those not using AI at all.341

While this newfound productivity enables revenue teams to do more with less (or the same), leaders still view AI as a catalyst for transformation rather than a threat to jobs.

When asked about the three-year impact AI will have on revenue headcount, 43% of respondents expect it to transform jobs without reducing headcount, making it the most common outlook. Only 28% anticipate job eliminations, while 21% foresee AI creating jobs. Just 8% predict minimal impact.

While Australia reports the highest planned AI adoption in 2026, they also have the most aggressive hiring plans. This signals that AI is not seen being leveraged for cost-savings but rather as a growth engine.