Gartner AI survey finds only 22% organisations scale AI successfully across businesses

AI investment is accelerating, but many organisations are still struggling to turn that spending into business-wide results. The latest Gartner AI survey found that only 22% of organisations have successfully scaled AI across multiple business units or adopted an AI-first approach. At the same time, 85% of functional leaders plan to increase AI spending in 2026.

The survey, conducted from January to April 2026 among 1,303 respondents from organisations with at least $50 million in annual revenue, points to a growing gap between AI adoption and measurable business value.

AI spending is rising, but visibility remains weak

Organisations dedicated an average of 12% of their functional budgets to AI in 2025. Yet roughly 11% of organisations were entirely unaware of what their function spent on AI during the year.

That lack of visibility becomes more important as investment grows. Gartner argues that without measurement tied directly to business outcomes, organisations risk wasting resources and missing expectations. For IT leaders and channel partners supporting Enterprise AI, the takeaway is straightforward: scaling investment without tracking outcomes can make AI programs harder to defend.

High performers provide a sharper contrast. Organisations that consistently track AI ROI, treat AI as a portfolio of value and reallocate resources when projects underperform reported positive returns in 81% of their AI initiatives. Low performers, meanwhile, did not know the return rate for 29% of their AI initiatives.

Productivity is winning over transformation

The survey also shows where organisations are putting their AI money. Productivity was the main target for 75% of functional leaders and accounted for around 30% of functional AI spending on average.

That focus suggests many businesses are using AI first to improve existing work rather than pursue larger transformation or new revenue opportunities. While productivity can provide measurable gains, the survey indicates that simply adopting popular AI applications does not guarantee strong returns.

The most popular AI use cases are not always winners

IT provides perhaps the clearest example. Cybersecurity threat detection and response, IT service desk automation and automated code generation and refactoring were among the most frequently pursued AI use cases.

However, the use cases reporting the highest proportion of positive returns were different. Intelligent IT asset and cost optimisation, synthetic data generation, and automated code generation and refactoring ranked among the leading areas for positive returns.

The difference points to one of the bigger AI scaling challenges: organisations may be choosing AI applications because they are widely discussed rather than because they fit a specific business problem.

AI adoption needs a stronger value test

The Gartner AI survey ultimately shifts the conversation from how much organisations are spending on Artificial Intelligence to what that spending delivers.

For businesses scaling AI, tracking expenditure by outcome, measuring returns and being willing to stop underperforming projects could matter as much as expanding deployment. The strongest AI strategy may not be the one with the most use cases, but the one that can clearly show where AI is creating measurable value.

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