A new poll of 945 CIOs and CTOs points to aging infrastructure, bubble fears, and government uncertainty as brakes on corporate AI.
Legacy systems and old applications are forcing large companies to abandon AI projects at a striking rate, according to a new survey from GFT Technologies, a firm that specializes in infrastructure modernization.
Eighty-four percent of chief information and technology officers said limits in their older systems have forced them to cancel at least one AI pilot or project, and nearly a quarter of them said it happened more than once.
The finding comes from polling of 945 CIOs and CTOs at companies with at least $500 million in annual revenue across 19 countries, fielded in August by Wakefield Research on behalf of GFT Technologies.
Beyond the legacy-system problem, the survey also found widespread doubt that AI spending is paying off and rising anxiety among the executives tasked with making it work.
Old Systems, New Problems
Virtually every company surveyed (99%) said its AI runs on or connects to legacy systems not built with AI in mind, and 95% said those systems slow their ability to deploy and scale AI.
Most consider the risk serious and believe running AI on unmodernized systems will eventually trigger a company-wide security crisis. Yet fixing it is slow. Only 15% said their modernization is complete or nearly there, 27% have started but feel behind, and 9% haven’t started at all.
“Every enterprise I talk to has an AI roadmap. Fewer have an infrastructure roadmap to match it, and that gap is why so many AI initiatives don’t make it past the pilot stage. Closing it is the real work of AI transformation, and it’s where the value ultimately gets decided,” said Rishi Chohan, CEO, GFT USA.
Is the Money Paying Off?
The infrastructure problem sits inside a bigger question about whether AI spending is delivering results.
Goldman Sachs has projected global AI-related investment will hit roughly $1 trillion this year. But a Gartner survey found only 22% of organizations have scaled AI across multiple business units, and a McKinsey survey found just 37% of companies say AI has meaningfully helped their earnings.
Executives in GFT’s survey share that skepticism from the inside: 89% said they’re concerned that global AI investment is growing faster than the business value it can realistically deliver, and 44% of those called themselves very or extremely concerned.
“An 84% project cancellation rate shows enterprises are finally recognizing that AI can’t simply be layered onto legacy systems, and that’s a healthier starting point than another year of pilot theater or chasing the next model release,” said Chohan.
Rethinking Who to Depend On
Geopolitics is reshaping how companies buy AI. In June, access to some of Anthropic’s most advanced models was interrupted worldwide after the company said it suspended access to comply with U.S. Department of Commerce export controls. (The controls were lifted on June 30, and access was restored the next day).
GFT’s report ties that episode to a near-unanimous response in its survey: 99% of executives said potential government restrictions on AI access make it more important not to depend on a single provider.
That’s already changing behavior. Half of respondents said geopolitical developments have limited where they can deploy AI, 34% cut planned investment, and 28% canceled projects outright.
Forty-two percent now lean toward building AI infrastructure in-house rather than buying it, and 54% said regulatory uncertainty has made them more cautious. Seventy-seven percent said they consider models from China-based providers, usually with added security reviews or only when no other option fits.
Skeptical of the Layoff Narrative
The survey also pushes back on a common assumption about AI and jobs.
Ninety-one percent of executives believe some public companies cite AI to justify workforce changes that are really meant to lift their share price, a view that is even more common in the U.S., at 93.3%.
Their own experience suggests the reality is messier: 51% hired people specifically to review or fix AI-generated work, 45% hired staff for work they had expected AI to handle, and 26% rehired employees they had previously let go.
Thirty-one percent canceled an AI initiative because it performed worse than the humans it was meant to replace.
The Pressure on Tech Leaders
That backdrop is weighing on the people running these programs. Eighty-nine percent worry that a wrong workforce decision made while scaling AI could put their own job at risk, and only 20% said other executives and board members fully understand the security risks of running AI on legacy systems.
The pressure is sharpest in the U.S., where 92% of tech leaders worry AI investment is outpacing its value, the highest of any region GFT surveyed, compared with 80.6% in Europe, the Middle East and Africa. Nearly half of U.S. respondents, 47%, said they’re very or extremely worried about their own jobs, above the 42.9% global average.
“U.S. technology leaders are carrying more pressure than most, over whether AI is delivering real value, workforce trust, and their own personal exposure if something goes wrong. With so much AI investment concentrated in the U.S., the resulting scrutiny makes it all the more important to recognize that the foundation underneath AI, from infrastructure and governance to the right talent, matters as much as the technology itself,” said Chohan.
What to Watch
GFT’s own conclusion is that companies need to modernize their infrastructure before they can scale AI safely, which is also the business GFT is in, so its framing is worth reading as an argument as much as a finding.
The open question is whether the wave of modernization spending now underway pays off before AI bubble fears become something more concrete, or whether more projects quietly join the 84% that already didn’t survive contact with legacy systems.