AI and governance issues: 3 keys to bridging a costly gap

Concerns about workforce and data readiness are commonly cited for artificial intelligence (AI) initiatives failing to substantially boost company bottom lines, but a recent survey of nearly 1,000 business leaders revealed an unexpected leading cause — and a lack of response to that cause.

When leaders were asked for Grant Thornton’s 2026 AI Impact Survey to name up to three organizational factors contributing to AI underperformance or failure, a survey-high 46% identified governance or compliance barriers. Insufficient training was next at 31%, followed by insufficient data readiness at 23% (just 8% selected “AI is not underperforming”).

Despite the governance concerns, just 11% identified risk and compliance as the function that needs the most focus in the AI realm.

“Across the organizations we work with, what we consistently see is that AI deployment has outpaced the infrastructure to defend it,” Tom Puthiyamadam, a Grant Thornton Advisors LLC managing partner, said in the report. “Leaders who have invested in governance aren’t moving slower — they’re moving faster because they have the confidence to scale. The ones who haven’t built it yet are one incident away from a much harder conversation.”

Most companies have a long way to go — just 22% said they have a fully developed and implemented enterprise AI strategy — yet companies that have made those strides are reaping the benefits. Those organizations are nearly four times as likely to report revenue growth as a result of AI (58%) than those in the piloting phase (15%).

Across all participating businesses, 78% lacked strong confidence that they could pass an independent AI governance audit within 90 days. But among those that are fully integrated, 74% had strong confidence.

Calling the lack of confidence among most organizations an “AI proof gap,” the Grant Thornton report explored four critical dimensions of the gap — starting with governance but including strategic focus, workforce readiness, and agentic AI risks — before detailing three steps leaders can take to close the gap.

Build governance like a performance system, not a policy document

“This is not a compliance exercise that gets reviewed quarterly,” the report said. “It is an operating system that assigns ownership of AI outcomes to specific leaders, creates measurement standards that make AI provable under scrutiny, and runs continuously alongside the AI it now governs.”

Close the C-suite alignment gap before it closes you

Members of the C-suite, including CFOs, “see the organization differently. Until leadership shares a common definition of AI readiness, accountability, and risk management, workforce investment will underperform, and agentic deployments will scale without the controls to contain them.”

Measure what is working — and exit what is not

“Only 22% of operations leaders are working with a fully developed and implemented AI strategy. The organizations pulling ahead are not scaling more pilots — they are scaling fewer, with better measurement and clearer exit criteria. Depth creates the outcomes that justify the next investment.”

Related AICPA resource: Executive Perceptions of Artificial Intelligence (AI) Opportunities and Risks: A Global Analysis

— To comment on this article or to suggest an idea for another article, contact Bryan Strickland at Bryan.Strickland@aicpa-cima.com.