Insurance AI Governance Framework
A working framework for sorting insurance AI use cases by risk, setting clear review expectations, and building the controls you'd actually need before deploying anything.
The problem
Insurance firms are under pressure to adopt AI, but most teams still don't have clear internal rules for what's low risk, what needs review, and what shouldn't be automated at all. I've seen this gap up close - the pressure to move fast without the structure to move safely.
What this builds
A governance framework that insurance leaders, product teams, and operators can actually use to evaluate AI use cases before deployment. Not a policy statement - a working tool.
Core framework questions
- What is the use case?
- What business function does it affect?
- Could the output influence a regulated decision or client outcome?
- What level of human review is required?
- What logging, audit, and escalation expectations should exist?
- What failure modes are most important?
Example risk tiers
- Tier 1 - Low risk support tasks: internal summarization, note cleanup, meeting recap drafts.
- Tier 2 - Moderate risk workflow support: renewal prep drafts, document extraction, internal research support.
- Tier 3 - High risk decision-adjacent support: outputs that materially influence underwriting interpretation, claims handling, or client advice.
- Tier 4 - Restricted / not appropriate for automation: unsupervised decisions with regulatory, legal, or client-impact implications.
Governance principles
- Use case before technology
- Explicit human accountability
- Traceability and logging
- Privacy and data minimization
- Documented limitations
- Escalation paths for failure or ambiguity
What's next
Finishing the full risk matrix, adding example review workflows, mapping common brokerage and carrier use cases, and creating a one-page governance summary that an executive could actually use in a meeting.