Insurance insights: 2026 AI Impact Survey Report

Why insurers need provable governance to sustain AI-driven growth

AI is fueling insurer growth. Without governance, it could erode.

For an industry built to manage risk, it’s notable that insurers are taking unnecessary risks as they adopt AI.

Grant Thornton’s 2026 AI Impact Survey Report of 950 executives finds that insurance leaders are seeing real returns from AI: 52% report AI-enabled revenue growth, 62% say they’re seeing improved decision-making insights and half report cost reduction. But 44% say governance or compliance challenges have contributed to AI project failure or underperformance.

Insurers are already seeing commercial upside from AI, so the question is no longer whether AI can create value, but whether firms can scale it safely and defensibly.

Without clear policies and tested controls, insurers are leaving their organizations open to risk with regulators and customers, fueling financial pressure that could ultimately erode product profitability. Tested and provable governance gives insurers confidence to deploy AI across higher-value workflows, leading to improved ROI and revenue growth. 

Insurance produces AI-enabled results it cannot yet govern consistently

Insurers rate their companies highly on the AI maturity scale — 62% of insurance executives say they’re scaling AI across multiple functions, 13% above the full survey sample. But only 24% say they are very confident they could pass an independent review of AI governance and controls with centralized evidence in the next 90 days. That means the remaining 76% are running AI in workflows where they cannot demonstrate, on demand, that those systems are governed adequately.

That creates risk because AI-supported decisions that cannot be traced with explainability create regulatory, conduct and financial risk, which limits performance potential across underwriting, claims, pricing and customer experience use cases where AI can drive efficiency and value creation.

Most insurers already have AI governance policies in place, but they haven’t built the operational infrastructure to prove and test them. Sixty-one percent of insurance leaders say their boards have set governance policies, but even when controls exist, evidence is fragmented across teams and tools, which often stems from poor guardrails and decision rights. Insurers that are successfully managing their AI-related risk define, assess and classify AI use cases and prioritize risk based on their potential impact and complexity.

Insurers have an operating model problem, not a workforce problem

Many insurance firms are scaling AI across front-office customer experience functions as well as middle-office claims and policy management roles. But only 7% believe their workforce is fully ready to adopt AI.

Thirty-nine percent of insurance respondents say frontline employees need the most support to adopt AI-enabled ways of working, and 29% say talent or upskilling gaps is one of the top barriers preventing their organization from scaling AI. To address these challenges, insurers need to go beyond training and change management: they must address their operating model and role redesign. That requires clarifying how underwriters, adjusters, service representatives and compliance teams work differently with AI.

When AI tools are deployed on top of existing workflows without guardrails, rules or decision-making rights, teams may not have the guidance or clarity to deploy the tools with confidence, leading to poor adoption. Insurers that are enabling effective AI adoption across functions have established role-based AI operating models for underwriters, claims adjusters and back-office functions with strong governance embedded throughout.

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As AI-enabled revenue grows, so do the risks

Insurance leaders are more likely than other industry leaders to say their customers are driving their company’s AI adoption. Thirty-eight percent say customer expectations are the greatest external pressure to the company adopting AI, 12 points above the cross-industry average. And 52% of insurance executives report revenue growth from those AI investments — 15 points above the cross-industry average.

Yet 56% name regulatory or compliance uncertainty as a top barrier to scaling AI. The scale and revenue uptick insurance executives are reporting will stall unless firms operationalize governance into live workflows, rather than treat it as policy documentation. Without it, they risk fines, reputational damage and margin pressure on their products. 

As insurers use AI across front-, middle- and back-office functions, it’s essential that they’re operating under a defensible, AI-specific governance, risk and compliance (GRC) framework. This framework should include roles and responsibilities, escalation paths, tested controls for model inventory, use-case classification, human-in-the-loop, third-party AI oversight, decision traceability and ongoing monitoring to maintain regulatory compliance and customer trust.

Three actions to strengthen AI governance that enables revenue

Many insurance firms are scaling AI, but those that gain competitive advantage will have defensible governance controls covering back- to front-office AI use cases that protect the firm while generating revenue.

In today’s competitive insurance landscape, insurers need to provide measurable value of their AI investments — quickly. But governance is what makes AI revenue scalable, defensible and sustainable. 

Grant Thornton works with insurance companies across the P&C, life and retirement, health and insurtech sectors, along with brokers and reinsurers, to identify AI use cases that drive revenue and build operating models to deploy them effectively while maintaining strong governance and controls. Discover how our AI solutions help insurers build responsible AI programs that enable growth.

Methodology

Between Feb. 23 and March 18, 2026, Grant Thornton surveyed 950 business leaders, a group restricted to CFOs, CIOs/CITOs, COOs, and VPs, department heads, and directors who report directly to the C-suite. The insurance-specific subgroup comprises 100 respondents. Role-specific findings within the insurance subset of data are directional only.