AI Strategy & Consulting for Insurance teams — built for the way you actually work.

What we see in Insurance today

Pain points we hear from chief operating officers

• Claims data scattered across multiple systems — no single source of truth for fraud signals or reserving
• Underwriting still partially manual — referral queues take 3–7 days for complex risks
• Customer-service teams answering policy questions by hand because the self-service portal lags by years
• Compliance reports built in Excel each quarter, reproducing the same joins every time
• New product launches blocked by core-system change windows, even for small variants

What AI strategy looks like for an insurance carrier

Decision-makers: Chief Operating Officer, Head of Claims, Head of Underwriting, IT Director, Chief Data Officer. Here is what we focus on when ai strategy meets insurance:

• 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.