AI consulting with a sharper answer than the last — for Insurance

Insurance carriers and brokerages run on legacy systems — most core policy admin, claims, and underwriting platforms in DACH and the broader EU were architected 15–30 years ago and have grown layers of integrations on top. The result is a sector where data lives in 8–15 systems per business unit, where analytics runs through Excel exports and weekly reports, and where every new product launch waits 6–12 months for IT bandwidth. The decision-makers are typically risk-averse for good regulatory reasons (BaFin, EIOPA, Solvency II) but increasingly under board-level pressure to modernise. The buying pattern: cautious vendor selection, long procurement cycles, but real budget once trust is established and a small win has been shipped.

• ▸Start with a use-case map across claims, underwriting, distribution, fraud — most carriers can name 30+ candidate use cases on the first call.
• ▸Score each use case on regulatory exposure × ROI × technical feasibility — the order changes dramatically once you do this exercise honestly.
• ▸Pilot the highest-ROI/lowest-regulatory-exposure use case first — typically intake document extraction or fraud signal aggregation.
• ▸Build the data-readiness picture before promising any AI outcome — most insurance data lakes have integrity issues that need fixing first.
• ▸Set up a governance forum that includes compliance, claims/underwriting ops, and IT — AI without this fails politically, not technically.
• ▸Plan for the EU AI Act explicitly — high-risk classifications affect underwriting, claims, and pricing decisions. Mapping each use case is part of the consulting engagement.

For a top-20 EU insurer, our 8-week AI-roadmap engagement identified 12 prioritised use cases, killed 6 others on regulatory grounds, and produced a 24-month execution plan that the board approved unanimously.