A new survey from Grant Thornton revealed a growing “AI proof gap,” a disconnect between AI investment and accountability.
“Companies are making tremendous investments into AI and yet, we’re not seeing that correlate with an increase in AI accountability,” Tom Puthiyamadam, managing partner of advisory services for Grant Thornton Advisors, said. “Our report found that while most organizations have implemented AI solutions, many teams cannot measure its impact or respond effectively when initiatives fail. That is a critical gap: in our view, the companies that win tomorrow will leverage AI effectively and adapt in real-time to ever-evolving business trends.”
The AI Impact Survey, based on responses from nearly 1,000 senior business leaders across multiple industries in the U.S., collected in early 2026, found that more than three-quarters (78%) lack full confidence that their organization could pass an independent AI governance audit within 90 days. Half (50%) of operations leaders said they need a formal AI strategy or governance plan in place within the next six months to improve performance.
“AI deployment is simply outpacing the infrastructure that supports it,” Puthiyamadam said. “We see this pattern repeatedly with new technology: guardrails come after an incident occurs — not before — and by then there may be significant organizational and operational consequences.”
Don’t Blame the Technology
The survey makes clear that what’s holding AI back isn’t the technology — it’s governance. Nearly half of leaders (46%) said AI underperforms because controls and compliance aren’t working.
Even more concerning, governance is rarely a top priority; just 11% of respondents said organizations should be most focused on risk and compliance to enable AI success.
The survey indicates that if companies scale AI before being certain it’s safe or effective, they aren’t being innovative; they’re increasing exposure to avoidable risk.
Investment Without Ownership or Strategy
AI is often advancing without clear accountability at the top of the organization. While three in four boards have approved major AI investments, only 52% have set clear AI governance expectations. Similarly, just 54% have integrated AI risk and opportunity into ongoing board or committee oversight.
“Most governance models weren’t designed for AI,” Puthiyamadam said. “Centralized review bodies become overwhelmed, creating bottlenecks that slow execution without reducing risk. The fix is to set policy and risk criteria centrally, then delegate assessments to trained reviewers at the division or regional level — aligning the depth of review to the level of risk.”
The survey also highlights a strategy gap that is limiting returns. More than half of executives (51%) said strategy is the biggest driver of AI return on investment, yet only 22% of operations leaders reported having a fully developed and implemented AI strategy.
“Organizations are expanding AI across more pilots, use cases and functions, but without consistent measurement, feedback loops or clarity on where value is created,” Sumeet Mahajan, lead partner, AI and data for advisory services at Grant Thornton Advisors, said. “You have to apply discipline — set measurement targets, build governance infrastructure and curtail initiatives that do not deliver results.”
AI Adoption Outpacing Workforce Readiness
Workforce readiness is also lagging AI adoption, creating a gap between technical deployment and day-to-day execution.
The size of this gap depends on whom you ask. Chief information officers (CIOs) and chief technology officers (CTOs) are five times more likely than chief operations officers (COOs) to say their workforce is fully ready to adopt AI (39% versus 7%).
Overall, only 12% of leaders said their workforce is truly ready to adopt AI. Most acknowledge training gaps, with 81% describing their workforce as only “fairly” or “mostly” ready.
Data and systems constraints compound the problem. More than half (55%) of CIOs and CTOs said the majority of their core applications are not AI-ready.
“Companies are betting on AI, but are not investing properly in the people and systems required to use it,” Mahajan said.
Scaling Autonomy Without Safeguards
As organizations grant AI systems more autonomy, many are doing so without the safeguards needed to manage failure. Nearly three in four organizations are piloting, scaling or running autonomous AI, yet only one in five has tested a response plan for AI failures.
While most organizations (95%) don’t permit agents to make fully autonomous, high-stakes decisions without human review, exposure remains significant at moderate risk levels. More than 4 in 10 (43%) list regulatory and compliance uncertainty as one of their top concerns about implementing agentic AI.
The report emphasizes that as AI gains autonomy, the real risk is being unprepared when it fails. For example, companies usually have incident playbooks, but they haven’t adapted them for AI, and that gap slows response times and makes failures harder to explain.
Strong Governance Drives Stronger Results
The survey shows a clear divide between organizations experimenting with AI and those capturing real value. Companies with fully integrated AI are nearly four times more likely to report AI-driven revenue growth than those still piloting AI (58% versus 15%).
“The organizations pulling ahead in AI are the ones with governance in place,” Puthiyamadam said. “They train their people, measure results and focus on scaling what works. Governance isn’t slowing AI leaders down — it’s correlated with stronger and more sustainable AI outcomes.”
