Gartner has found that just 22% of organizations have successfully scaled AI across multiple business units or adopted an AI-first approach, according to its latest survey. Even so, 85% of functional leaders said they planned to increase AI spending in 2026.
The survey covered 1,303 respondents at organizations with at least $50 million in annual revenue in fiscal 2025. Functional leaders said they devoted an average of 12% of their budgets to AI last year. Gartner also found roughly 11% of organizations lacked visibility into functional AI spending.
High performers manage AI as a portfolio
High performers in Gartner’s survey continuously tracked returns, managed AI initiatives as a portfolio and regularly reallocated or stopped underperforming projects.
They reported positive returns in 81% of AI initiatives. Low performers, by contrast, did not know the rate of return for 29% of their initiatives.
Productivity targets and use case returns
Productivity was the most common objective. Gartner said 75% of functional leaders targeted productivity, with about 30% of functional AI spending going toward that goal.
The most-pursued IT use cases did not fully align with those reporting the highest returns. Cybersecurity threat detection and response and IT service desk automation were each pursued by 54% of C-suite respondents, while intelligent IT asset and cost optimization had the largest share reporting positive returns, at 40%. Automated code generation and refactoring was the only use case to appear in both top-three lists.
Methodological limits and undefined thresholds
Gartner’s public release does not define the minimum threshold for a positive return or say how much functional leaders expect AI spending to increase in 2026. It also does not establish that the management practices associated with high performers caused the positive returns those respondents reported.
Cross-industry benchmarks show varying metrics
A separate KPMG survey found a different kind of cost-visibility gap among larger organizations. Based on responses from 204 U.S. C-suite and business leaders at organizations with at least $1 billion in annual revenue, KPMG found only 26% of organizations had full, real-time visibility into AI operating costs. KPMG also found 66% had monitoring dashboards and 61% had approval processes.
Gartner’s 22% is not directly comparable with other estimates of enterprise AI scaling. McKinsey’s “The state of AI in 2026: On the road to ROI” survey of 1,719 respondents across 97 countries found 44% said AI was scaling across their enterprise, rising to 54% among respondents at organizations with at least $1 billion in annual revenue. The surveys use different populations and definitions of scaling.
Financial impact was less widespread in McKinsey’s survey. While 44% reported enterprise-scale AI adoption, 37% said AI had made a positive contribution to their organizations’ earnings before interest and taxes, a share little changed from a year earlier. About 6% met its definition of an AI high performer.
PwC measured AI returns differently. Its analysis of 1,217 organizations across 25 sectors found the top 20% captured 74% of AI-driven returns, based on reported revenue and efficiency gains adjusted against sector medians.