- AI leaders are shifting from pilots to end-to-end, domain-wide gen/agentic AI across underwriting, claims, and distribution, and are materially outperforming peers.
- At-scale adoption is delivering measurable gains such as faster onboarding, higher premium growth, better agent conversion, and improved claims accuracy.
- Capturing value requires reusable AI components plus modern data/tech foundations, agile operating models, strong governance, and disciplined change management.
- Insurers must manage risks including vendor lock-in, weak data quality, talent gaps, cultural resistance, and rising AI liability and regulatory exposure.
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The McKinsey report “The Future of AI in the Insurance Industry” (July 2025) lays out a clear distinction between insurers that are AI leaders and laggards, showing leaders have achieved 6.1× total shareholder return relative to laggards over the past five years. This underscores that AI is no longer a differentiator but a foundation for competitive positioning in insurance.
At the domain-level—claims, underwriting, sales/distribution—deep transformation proves its worth. When insurers adopt end-to-end AI workflows instead of bolted-on pilots, performance metrics improve materially: onboarding costs drop by up to 40%, premium growth climbs, and claim accuracy edges upward. These gains multiply when AI investments are channeled through reusable components and multi-agent systems that can be repurposed across multiple business functions.
Implementing this scale demands not only technology investment but a rewiring of operating models. Critical moves include aligning the C-suite around measurable KPIs, choosing operating models (product-platform, agile pods, centralized/decentral teams), building a robust in-house technical bench (70-80% digital talent internally), and strengthening data governance and infrastructure layers. Meanwhile, change management—shifting culture and employee behavior—is emphasized as comprising half of transformation effort.
External pressures are forcing insurers to clarify liability and risk exposure. For example, U.S. insurers such as W.R. Berkley, AIG, and Great American are introducing exclusions for AI-induced errors in liability insurance. Regulatory and reputational risks tied to AI’s misuse are demanding formal governance and underwriting reflections. Operational risks—supplier lock-in, legacy systems, and data quality—pose real threats if left unmanaged.
Comparative data from other voices confirms direction and scale: A BCG survey finds that insurance firms are now at near-parity with technology/media/telecom in AI adoption, with “leading” firms showing >30% productivity gains via knowledge assistants; AI in communications (e.g., customizing claimant letters) is already in production in many cases. McKinsey’s own survey of European insurers estimates gen AI’s potential to lift premium growth by 1.5-3% and improve technical insurance result by a similar magnitude.
Strategic implications: Insurers that don’t commit to domain-wide, deeply integrated AI strategies risk being left behind. Those that do can earn top-quartile returns, but must invest across people, process, tech, data, and governance. Open questions include how regulation will evolve (especially AI liability), how to attract and retain skilled AI-native talent, and how incumbent insurers with complex legacies will compete with nimble insurtechs.
Supporting Notes
- AI leaders in insurance have delivered 6.1× total shareholder return over peers in the past five years.
- Domain-level AI transformations have yielded 10–20% improvement in new agent success/sales conversion, 10–15% premium growth, 20–40% lower onboarding costs, 3–5% improvement in claims accuracy.
- Aviva deployed over 80 AI models in its claims domain, saving £60 million (~US$82 million) in 2024 and cutting liability assessment time for complex cases by 23 days, routing accuracy up ~30%, and customer complaints down ~65%.
- McKinsey survey of large European insurers: gen AI expected to drive 10–20% productivity gains; 1.5–3.0% premium growth; improvement in technical results by 1.5–3.0 ppt.
- BCG findings: insurers using AI-empowered knowledge assistants show over 30% productivity gains in service and operations roles; communication with claimants being largely automated.
- Insurers such as W.R. Berkley, AIG, Great American are adding liability policy exclusions for AI-related errors.