American CIOs and CTOs are carrying a heavier share of the pressure around AI than technology leaders anywhere else, according to new research by AI-centric digital transformation firm GFT Technologies released today.
Based on a survey Wakefield Research conducted in August among 945 CIOs and CTOs across 19 countries, with all participants working at a company with at least $500 million in annual revenue, the study captures the views of people signing off on some of the largest AI budgets in the world.
U.S. tech leaders stood out on several fronts: they’re more worried than anyone that AI spending is getting ahead of what it can deliver, more skeptical about how companies explain AI-related layoffs, and more concerned that one wrong call could cost them their own job.
Meanwhile, respondents everywhere said that outdated infrastructure is quietly killing AI projects before they can even get off the ground.
But pressure isn’t slowing adoption down, as companies keep pushing ahead with AI. The survey, however, suggests many are scaling faster than the systems, governance and talent underneath them can reasonably support.
GFT calls this a widening AI implementation gap, whereby ambition keeps outpacing readiness and the consequences increasingly fall on the executives in charge.
The gap looks different depending on where you sit. For technology leaders in the U.S. particularly, where so much of the world’s AI capital is being concentrated, it shows up as closer scrutiny from boards, sharper doubts about the numbers, and a growing sense that they’ll be the ones held accountable if things go wrong.
More money, more doubt
Globally, 89% of respondents said they’re concerned AI investment may be growing faster than the business value it can realistically produce. In the U.S., that figure rises to 92,1%, the highest of any region, compared with 80.6% in Europe, the Middle East and Africa (EMEA).
According to Stanford’s 2026 AI Index, U.S. private AI investment hit $285.9 billion USD in 2025, representing over 23 times China’s tracked total of $12.4 billion USD. Regardless, spending has only picked up since then, with Statista projecting combined capital expenditure at Alphabet, Amazon, Meta and Microsoft will reach $760 billion USD this year, up from $413 billion in 2025.
When that much capital becomes concentrated in a singular market, the people responsible for turning it into results end up under the most scrutiny; boards want to see returns, but the GFT survey suggests many don’t fully grasp what stands in the way.
Only 20% of respondents said their fellow C-suite executives and board members fully understand the security risks of running AI on legacy systems.
“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 Rishi Chohan, CEO of GFT USA.
The layoff story U.S. leaders aren’t buying
The survey’s workforce findings may be its most pointed. Of American respondents, 93.3% believe some public companies cite AI to justify workforce changes that are mostly about boosting their share price – above the 90.6% global figure and well ahead of EMEA and Asia-Pacific.
The figures arrive at a moment when AI becomes a more common explanation for cuts. Outplacement firm Challenger, Gray & Christmas reported in June that AI was named as the main reason for almost 40% of announced U.S. job cuts in May, and that AI-attribution cuts in the first five months of 2026 reached 87,714, already more than the 54,836 recorded in all of 2025.
Challenger itself acknowledged in August that what counts as an AI-attributed cut can be ambiguous, which is exactly the gap the survey’s respondents seem to be pointing at.
The personal stakes are rising, too. Across the full GFT sample, 89% worry that a wrong workforce decision made while scaling AI could put their own job at risk, with 47% in the U.S. noting they’re very or extremely concerned – compared with 42.9% globally, though overall concern looks similar across all regions.
“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,” said Chohan.
Legacy systems are where AI plans go to die
The most striking global figure has to do with infrastructure. Some 84% of respondents said limitations in their legacy systems have already caused their organization to cancel an AI pilot or project, and 93% believe running AI on old infrastructure without modernizing first will eventually lead to an enterprise-wide security crisis.
Analysts have been warning about this for a while. Last year, Gartner predicted that more than 40% of agentic AI projects would be canceled by the end of 2027, and noted that plugging agents into legacy systems can be technically complex and expensive to adapt.
Geopolitics is adding another complicating layer. Nearly all respondents (99%) said potential government restrictions on AI access make it more important not to depend on a single AI provider, which GFT connects to the U.S. government’s dispute with Anthropic earlier this year over access to its Mythos and Fable models.
In response, 42% are now leaning towards building AI infrastructure in-house rather than buying from outside vendors.
That shift carries its own risks, too. Research from MIT’s NANDA initiative last year suggested companies that build generative AI systems on their own see considerably more failures than those that work with partners. Building in-house can reduce dependence on one provider, but it puts even more weight on the internal foundations the survey says many companies still lack.
For U.S. tech leaders, that pressure isn’t going away. With the largest share of global AI capital flowing through American companies, CIOs and CTOs will likely keep facing the toughest questions about whether that money is paying off.
GFT’s answer is to fix the foundations first, meaning modernizing legacy environments, tightening governance, managing dependence on outside technology, and ensuring AI plans line up with broader business and workforce strategy.
Featured image: Kevin Gonzalez via Unsplash+
Disclosure: This article mentions a client of an Espacio portfolio company.
