EXL: 76% of Insurers Think They Lead AI, Only 6% Do

Seventy-six percent of insurers rate themselves ahead of their competitors on artificial intelligence. Six percent actually qualify as leaders, and 72% land in the middle follower tier, the highest follower concentration EXL's third annual Enterprise AI Study found in any industry it surveyed (Carrier Management, August 12, 2026). The gap is widest exactly where pricing and reserving decisions get made.

Key Takeaways

• A seventy-point spread between self-perception and measured capability. Insurance posted the widest gap of the five industries EXL surveyed, and its 6% leader rate sits four points below the cross-industry average while its self-belief matches everyone else's.
• 46% report agentic AI advancing fastest in actuarial, underwriting and pricing, behind risk management at 54% and roughly level with customer experience at 45%. The functions closest to statutory reserves are not where AI has been pushed hardest.
• 92% cite data as an obstacle, the highest share attributing the barrier to data silos of any industry surveyed, and only 24% rate their own data management as mature.
• 96% now rank scaling AI a high priority, up from 86% in 2025, which is the pressure that pushes a follower toward buying capability rather than building and validating it.

A Wider Gap Than the Cross-Industry Average

EXL surveyed 322 executives across five industries and classified them into leaders, followers and laggards on measured capability rather than self-report (EXL, 2026 Enterprise AI Study). Across all five, 76% of executives believe they are ahead of competitors while only 10% qualify as leaders, a bar that requires capability across six to eight core functions with enterprise-wide data access and agentic AI embedded in workflow redesign.

Insurance's leader rate came in at 6%, four points below that average, while its self-belief held at the same 76% every other industry reported. Run the two together and insurance is not simply overconfident in line with everyone else. It is both further behind the leader threshold and equally convinced it is ahead, which is worse than either fact alone. A sector that knew it was lagging could prioritize catching up; a sector that believes it already leads has no internal signal telling it to change course.

Rup Goswami, EXL's Insurance Growth Office Lead, framed where attention has shifted: "The industry has stopped asking whether AI works and started asking how fast they can scale it" (Digital Insurance, August 2026). Scaling intent and scaling capability are not the same claim, and the tiering is built to catch the difference.

Where the Gap Concentrates

The functional breakdown is where the maturity gap stops being a survey artifact and becomes a pricing and reserving problem. 46% of insurers report agentic AI advancing fastest in actuarial, underwriting and pricing, putting that cluster behind risk management at 54% and roughly level with customer experience at 45%. Fraud detection and customer servicing both lead at 54%, with claims at 42%.

Metric	Insurance	Cross-industry average
Believe they are AI leaders	76%	76%
Actually classified as leaders	6%	10%
Classified as followers	72% (highest of any industry)	–
Agentic AI most advanced in actuarial/underwriting/pricing	46%	–
Cite data as an obstacle to AI success	92%	–
Rate own data management as mature	24%	–

The functions with the most direct line to statutory reserves and filed rates are not where insurers pushed hardest. They run level with or behind functions where a model error produces an inconvenient customer interaction rather than a mispriced book.

Data readiness explains part of it. 92% cite data as an obstacle, and only 24% rate their own data management as mature. Actuarial and underwriting models draw on the deepest, most fragmented data estate a carrier holds: historical loss triangles, policy administration records, claims notes and third-party scoring feeds spread across systems that predate the AI build-out by decades. Fraud and servicing tools bolt onto a narrower, cleaner slice. The functions with the least clean data are the ones where the highest-stakes decisions get made, which is a structural reason the actuarial gap does not close on its own timeline.

The practical use of that finding is as a discount rate on disclosure. If three in four insurers believe they lead and one in twelve clears the bar, a carrier's own characterization of its AI maturity in a proxy statement or an earnings call carries a documented industry-wide bias toward overstatement. It is not necessarily deliberate: 91% of leaders rate themselves ahead on data maturity against 61% of laggards making the identical claim, so companies that are behind are using an internal reference frame calibrated to their own history rather than to the market's actual leaders.

The Governance Debt the Followers Are Carrying

The follower tier is where a second finding turns disclosure skepticism into a balance-sheet question. Leaders achieve 40% more revenue growth and 37% more cost reduction than laggards in the use cases where AI is actually applied, a gap wide enough that boards have pushed hard: 96% now rank scaling a high priority against 86% a year earlier.

That pressure lands on the 72% follower cohort as an incentive to buy capability quickly rather than build and validate it, and buying quickly is where governance debt accumulates. The CAS's AI Primer frames the obligation directly: responsible deployment requires validation, human-in-the-loop review, and documentation showing AI outputs are "appropriate, explainable, and aligned with business and regulatory expectations," regardless of whether the model was built in-house or licensed (Casualty Actuarial Society, April 2026).

A follower that licenses a vendor's pricing or triage model to close the gap fast inherits the NAIC bulletin's third-party oversight obligation on a compressed timeline, with less internal capability to interrogate what it purchased than a carrier that built the equivalent over several validation cycles. Twenty-five states had adopted the AI model bulletin as of mid-2026, requiring a written AI Systems Program with board-level accountability and documented validation, and a twelve-state pilot of the companion evaluation tool gives examiners a structured way to test self-reported maturity against actual documentation.

There is a tension inside EXL's own numbers that sharpens the point rather than softening it. Insurance reported a 62% pilot-to-production rate, the highest of any industry studied, against a 6% leader classification. Insurers are unusually good at graduating individual pilots and unusually poor at compounding those graduations into the six-to-eight-function breadth leadership requires.

That is the shape of the debt. A carrier can point to a production deployment in every conversation about AI maturity while holding a model inventory that lists vendor tools by name but not by validation status, and a change-management log that stops updating once a vendor pushes a refresh nobody retested. Neither shows up in an executive survey response, which asks whether capability exists rather than whether its governance would survive an exam.

Further Reading on actuary.info

• Insurance AI Hits the Pilot-to-Portfolio Wall - The adjacent finding on why individual AI pilots stall before reaching enterprise scale.
• 42% of P&C Insurers Never Measured AI Outcomes: Inside the Capgemini Measurement Gap - A companion data point on why insurers cannot reliably judge their own AI performance.
• EXL's AI-Led Revenue Hits 61% While Its Legacy Operations Line Shrinks - How the same vendor's growth reshapes the build-versus-buy calculus this piece's governance-debt section extends.
• AI Regulation in Insurance 2026: The NAIC Model Bulletin, State Adoption, and the Federal Preemption Battle - The full state-by-state mechanics of the oversight regime discussed above.
• When Your AI Governance Controls Are Vendor-Patented - What follower carriers risk when governance tooling itself comes from the same vendor supplying the model.