Why do over 70% of enterprise AI projects fail to deliver ROI?
Gartner finds only 28% of AI projects deliver ROI. What separates that 28% is not a better model, a bigger budget, or a more ambitious use case. Among leaders who reported failure, the dominant root cause was misaligned expectations: leadership assumed AI would immediately automate complex tasks or produce cost reductions on a timeline the technology was never going to meet. Among those who reported success, the leading factor was integrating AI into existing workflows rather than bolting it on as a parallel process.
What actually separates the 28%
Gartner's research points to something less exciting and far more fixable than model quality or spend.
Among the infrastructure and operations leaders who reported failure, the dominant root cause was misaligned expectations. Leadership assumed AI would immediately automate complex tasks or produce cost reductions on a timeline the technology was never going to meet. Among those who reported success, the top factor was integrating AI into existing workflows rather than bolting it on as a parallel process.
Expectations set without evidence are the failure mode. A timeline nobody measured against is a forecast that cannot be corrected while there is still time to correct it.
Source: Gartner: Only 28% of AI Projects Deliver ROI. Here's Why the Rest Don't.
The same shape shows up in a second dataset
PwC's 29th Global CEO Survey, published in January 2026, surveyed 4,454 CEOs across 95 countries and landed on a strikingly similar shape of problem. Fifty-six percent of CEOs report zero revenue or cost benefit from their AI investments to date. Only 12% report benefiting on both fronts, revenue and cost, at once.
Two different research firms, two different survey populations, and the same structural story: a small minority of enterprises can point to AI value with confidence, and a majority cannot, despite comparable or larger spend.
Source: Gartner: Only 28% of AI Projects Deliver ROI. Here's Why the Rest Don't.
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