A new study of over 300 respondents across multiple industries reveals a significant gap between how insurers assess their AI maturity and the reality of their integration efforts. The third annual EXL U.S. Enterprise AI Study shows a major shift from experimentation to enterprise-wide AI scaling, with 96% of insurers now ranking AI scaling as a high priority, up from 86% in 2025.
Agentic AI is advancing fastest in risk management, actuarial, underwriting, and customer experience, orchestrating complex workflow across multiple systems. Insurers are moving beyond point solutions to redesign entire workflows, connecting intake, conversational, and claims steps at first notice of loss in property and casualty lines, while unifying data foundations across underwriting and onboarding in life and annuity.
Maturity gap persists
Though 76% of insurers believe they are ahead of the competition, actual AI adoption rates are similar across the industry. Only 6% qualify as leaders, and insurance has the highest share of companies in the middle follower category - 72% - of any industry surveyed. Leaders were two times more likely to improve operational efficiency with AI than laggards.
Insurers do move more AI pilots to production than any other industry, with 62% reaching full deployment. Pilots that advance share three traits: a clear business owner, a defined outcome, and agreement on what "production ready" means.
Where AI is delivering
AI maturity in insurance is highest in customer-facing and operational areas. Leading applications include fraud detection and customer servicing at 54% each, followed by financial crime compliance at 44%, risk management at 44%, and claims at 42%. In agentic AI specifically, risk management leads at 54%, followed by actuarial, underwriting, and pricing at 46%, and customer experience at 45%.
"Leaders were nearly three times more likely to adapt to market changes with agentic AI than laggards, and two times more likely to redefine the customer experience with agentic AI," the study found. Of all insurers surveyed, 98% believe agentic AI has improved customer experience, and 45% of insurance agentic AI initiatives have reached success.
Adoption remains less mature in underwriting, actuarial, and risk decision-making, signaling substantial room for deeper transformation. Nearly half of insurers (46%) have fully deployed AI in actuarial and underwriting, and leaders generated 40% more revenue growth and 37% more cost reduction than laggards in cases where AI is applied.
Data is the top barrier
Nearly 92% of insurers say their data is a challenge to AI success, and insurance cites data silos as the top barrier more than any other industry. Confidence in data quality nearly doubled since 2025, yet more than half of insurers now report problems with data efficiency, raising the cost of running AI.
Only 24% consider themselves mature in data management, and just 38% completely agree they have sufficient governance for ethical and responsible AI use. Closing these gaps is central to a durable AI strategy, said EXL, noting that insurers wanting progress in stalled projects must move pilots to production, improve data, embed AI into workflows, and strengthen governance.
For professionals working on AI for Insurance initiatives, the study offers a clear benchmark: most insurers are clustered in the middle, and the gap between perceived and actual maturity means organizations that close data and governance gaps will have a genuine competitive edge. For senior leadership, resources like AI for Executives & Strategy can help bridge the gap between ambition and execution.
Why this matters for insurance professionals
If you're in insurance AI, your organization likely thinks it's ahead - but the data says you're probably in the middle of the pack, surrounded by peers with similar adoption levels. The real differentiator isn't pilot volume; it's data readiness, governance maturity, and moving pilots to production with clear business ownership and defined outcomes. Leaders in this space are already seeing measurable revenue and cost advantages, especially in underwriting, actuarial, and risk decision-making. Focus on data quality and workflow integration, not just technology procurement.
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