Gartner Survey: Only 28% of AI Infrastructure Projects Deliver Full ROI

Only 28 percent of AI infrastructure projects fully succeed and deliver return on investment (ROI), according to new research from Gartner that surveyed 782 IT infrastructure and operations (I&O) managers. The findings paint a sobering picture for organizations investing heavily in AI technologies to improve efficiency and reduce costs.

High Failure Rates in AI Infrastructure

The survey, conducted in November and December 2025, found that one in five AI projects in IT infrastructure and operations fail outright. More concerning, 57 percent of I&O leaders reported experiencing at least one failure when applying AI to their operations.

Melanie Freeze, research director at Gartner, attributes these failures primarily to unrealistic expectations. "They assumed AI would immediately automate complex tasks, cut costs, or fix long-standing operational issues," Freeze explained. "When expectations are not realistically set and the results don't appear quickly, confidence drops and projects stall."

Common Points of Failure

Gartner identified specific areas where AI initiatives most frequently stumble:

• Auto-remediation systems that fail to handle complex scenarios
• Self-healing infrastructure that cannot adapt to novel situations
• Agent-led workflow management between systems that breaks down under pressure

Beyond technical challenges, the research highlighted persistent organizational barriers. Among I&O leaders who faced setbacks, 38 percent cited ongoing skill gaps as a major impediment to AI success. An equal proportion pointed to poor data quality or limited data availability as direct causes of project failure.

Where AI Shows Promise

Despite the overall disappointing results, the survey found that tech managers achieve better outcomes in areas where AI technology is more mature. IT service management (ITSM) and cloud operations emerged as the most successful applications, with 53 percent of I&O leaders reporting success in these domains.

The contrast between mature and emerging AI applications underscores the importance of realistic project scoping and choosing appropriate use cases for initial AI deployments.

Funding Challenges and ROI Pressure

Organizations face mounting pressure to justify AI investments. The research revealed that many AI initiatives are still funded by individual business units rather than through coordinated enterprise strategies. "However, as AI infrastructure spending continues to rise, CEOs and CFOs need to play a more active role in setting funding criteria and approving major investments," Freeze noted.

This funding challenge comes amid broader industry struggles to demonstrate AI's value. A separate survey of nearly 6,000 corporate executives across the US, UK, Germany, and Australia found that more than 80 percent detect no discernible impact from AI on either employment or productivity, despite 69 percent of businesses currently using some form of AI.

Growing Pressure for Results

Additional research from Harris Poll, commissioned by Dataiku, indicates that tech leaders face increasing pressure to show returns on AI investment in 2026. The survey found that 98 percent of CIOs report growing pressure from boards to demonstrate ROI, while 71 percent believe their AI budget could face cuts or freezes if targets aren't met by mid-2026.

These findings suggest that organizations must become more strategic and disciplined in their approach to AI infrastructure projects, focusing on realistic goals, mature technologies, and clear success metrics to avoid becoming part of the 72 percent that fail to achieve full ROI.

The research highlights a critical inflection point for AI adoption in IT infrastructure, where the gap between expectations and reality is forcing organizations to reassess their strategies and investment approaches.