Only 22% companies have scaled AI across business units: Report

Only 22% of organisations have successfully scaled artificial intelligence (AI) across multiple business units or adopted an AI-first approach, even as companies continue to increase spending on the technology, according to a new survey by Gartner.

The survey, conducted between January and April 2026 among 1,303 respondents from organisations with annual enterprise-wide revenue of at least $50 million in fiscal 2025, found that 11% of organisations do not even know how much their function spent on AI in 2025.

Despite limited visibility and challenges in scaling AI, spending on the technology is rising. Around 85% of functional leaders said they plan to increase AI spending in 2026, after allocating an average of 12% of their functional budgets to AI in 2025.


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“This lack of financial visibility heightens risk as spending accelerates,” said Tina Nunno, Distinguished Vice President and Gartner Fellow. She said organisations without disciplined measurement linked directly to business outcomes risk wasting resources and falling short of expectations.

The survey also found a significant gap between organisations that actively measure AI returns and those that do not.

Companies classified as high performers — those that continuously track ROI, manage AI initiatives as a portfolio and regularly reallocate or discontinue underperforming projects — reported positive returns from 81% of their AI initiatives.

In contrast, low-performing organisations said they did not know the rate of return for 29% of their AI initiatives.

Productivity is driving AI spending

For most businesses, AI is currently being deployed more to improve productivity than to drive major transformation or create new revenue streams.

Productivity was a key target outcome for 75% of functional leaders, with around 30% of functional AI spending going towards it on average. This was nearly twice the share allocated to the next-largest objective.

Gartner said leaders that track AI spending against specific outcomes such as productivity, revenue growth, risk mitigation and innovation are better positioned to determine which projects deserve further investment.

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Popular AI use cases don't always deliver the best returns

The survey also highlighted a disconnect between the AI applications organisations pursue most aggressively and those that deliver the strongest returns.

Among IT functions, cybersecurity threat detection and response and IT service desk automation were each cited by 54% of C-suite leaders as frequently pursued AI use cases, while automated code generation and refactoring was cited by 44%.

However, the use cases reporting the highest proportion of positive returns were different. Intelligent IT asset and cost optimisation topped the list at 40%, followed by synthetic data generation at 28% and automated code generation and refactoring at 23%.

“Organisations are seeing the greatest quantifiable value from less common, strategically selected use cases that are closely aligned to their unique business needs,” Nunno said.

The findings suggest that simply increasing AI budgets may not be enough. As companies ramp up spending, identifying the AI projects that produce measurable business value — rather than those that are simply popular — could become increasingly important.