More than half of insurance executives say artificial intelligence is driving revenue growth at their organizations, but a significant share cannot demonstrate that their AI systems are adequately governed, according to Grant Thornton’s 2026 AI Impact Survey Report.
The survey of 950 business leaders, including a 100-person insurance-specific subgroup, found that 52% of insurance executives report AI-enabled revenue growth, 15 percentage points above the cross-industry average. Another 62% say AI has improved decision-making, and half report cost reductions. At the same time, 44% say governance or compliance challenges have contributed to AI project failure or underperformance.
The confidence gap is notable. While 62% of insurance executives describe their organizations as scaling AI across multiple functions, only 24% say they are very confident they could pass an independent review of AI governance and controls within 90 days. That means 76% are running AI in workflows where they cannot demonstrate, on demand, that those systems are adequately governed.
Most insurers have policies in place. Sixty-one percent of insurance leaders say their boards have set governance policies. However, 68% say their AI controls exist, but evidence is fragmented across teams and tools. Insurers that are managing AI-related risk more effectively define, assess, and classify AI use cases, then prioritize risk based on potential impact and complexity.
Regulatory and compliance uncertainty is also slowing progress. Fifty-six percent of insurance executives name it as a top barrier to scaling AI. Thirty-eight percent say customer expectations are the greatest external pressure driving AI adoption, 12 percentage points above the cross-industry average.
On the workforce side, only 7% of insurance executives believe their employees are fully ready to adopt AI. Thirty-nine percent say frontline employees need the most support, and 29% cite talent or upskilling gaps as a top barrier to scaling. Grant Thornton’s report notes that workforce adoption challenges often reflect operating model issues rather than training gaps alone, pointing to the need for role redesign across underwriting, claims, and service functions.
Grant Thornton recommends three actions for insurers looking to strengthen AI governance. First, organizations should evaluate and modernize governance structures, including defining criteria for high-return AI use cases and creating standards to track performance and detect model drift. Second, insurers should assess their operating models to support greater AI adoption, including updating workflows and redefining roles, reporting structures, and escalation paths. Third, firms should build a governance, risk, and compliance framework aligned with standards such as the NIST AI RMF, ISO/IEC 42001, and the EU AI Act, and embed it directly into AI workflows.
The report includes case study examples illustrating both challenges and results. One large insurer reduced the average review-closure time after completing reviews for more than 200 AI use cases, following a redesign of its intake and risk-review process. A mutual insurer conducting an independent audit of a generative AI call-summary tool identified gaps in output consistency, weaknesses in human review, and undocumented data-handling steps before those issues became regulatory or reputational problems.
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