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U.S. technology leaders are under higher AI-related strain than their global counterparts.

In corporate America, the push for AI-driven initiatives is not matching the technical reality: legacy code limitations. According to research from GFT Technologies, an AI-centric digital transformation company, 84% of CIOs and CTOs globally say that limitations in their legacy systems have caused their organization to cancel an AI pilot.

The 2026 'The Future of AI, Unfiltered' study, conducted by Wakefield Research for GFT, spanned 945 CIOs and CTOs across 19 countries at companies with at least $500 million in annual revenue.

It confirmed that U.S. technology leaders are under higher AI-related strain than their global counterparts. While 89% of tech leaders globally worry that AI investment is growing faster than the business value it can realistically deliver, that anxiety is most present in the U.S., where 92.1% of executives hold the same reservations, compared to just 80.6% in the EMEA.

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Rishi Chohan, CEO of GFT USA, told the International Business Times over email that this localized pressure stems from concern "over whether AI is delivering real value, workforce trust, and their own personal exposure if something goes wrong."

The data aligns with Chohan's perspective. Aging infrastructure has always been a wall between corporate ambition and technical reality. The rigid design of old codebases, accumulated patches, and overworked architecture all contribute to 84% of global tech leaders canceling an AI project.

Beyond potential deployment failures, 93% of respondents agree that running AI applications on outdated systems introduces security risks. Left unaddressed, these risks will likely escalate into an enterprise-wide security crisis, costing organizations more in the long run.

"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.

Tech leaders are also skeptical about how AI is invoked in boardrooms to justify workforce changes. 93.3% of U.S. respondents believe that some public companies use AI as cover for workforce changes meant primarily to boost their share price.

Analysts have warned of this trend, dubbed 'AI washing', where organizations without vetted AI applications attribute financially motivated layoffs to future AI implementation.

Speaking at the 2026 Semafor World Economy conference, Jason Droege, Scale AI interim CEO, noted that a lot of AI-related cuts are simply "companies using the technology as an excuse to remove headcount." Consequently, this creates an environment where CIOs and CTOs must manage widespread organizational skepticism, while building the very systems that could be used as a smoke screen for future workforce reductions.

The AI-related strain on U.S. tech leaders is compounded by the fear of professional fallout. According to the study, 47.0% of U.S. respondents are very or extremely concerned that a wrong workforce decision made while scaling AI could put their own job at risk, compared to 42.9% globally.

While the overall concern levels are similar across regions, the scale is notable: nearly half of U.S. CIOs and CTOs feel exposed. Operating under constant scrutiny and the threat of termination, these executives know they have very little margin for error.

Despite the security risks of forcing AI applications onto fragile legacy systems, shifting workforce dynamics, and the looming threat of professional fallout, the pressure to deliver ROI on AI initiatives is climbing — especially in a hyper-capitalist market like the U.S.

But closing the gap between AI ambition and infrastructure readiness often circles back to the foundations: architectural modernization, data readiness, enterprise security, and strict governance.

"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," noted Chohan.

"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," he added.