Insurance Strategy
Insurance is both an early adopter of AI agents and a future foundation for understanding the risk of AI actions. The bridge between both is practical governance.
Market 1: Today
Using governed AI agents inside insurance workflows. This is about making insurance operations agent-ready.
Main question: Can this insurance workflow safely use AI agents?
Market 2: Future
Using insurance logic to govern, price, approve, cover, and attribute AI agent actions. This is about making autonomous actions easier to review.
Main question: Can this AI action be reviewed, approved, evidenced, risk-assessed, and owned?
Future thesis
The future thesis is strategic. Agent Readiness Audits are the practical starting point today.
AI will move from content to execution.
Every serious agentic action will need authority.
Agentic actions may eventually need action-level coverage or risk review.
Permissions should be scoped, temporary, and reviewable.
Insurance-like risk review may become more granular.
Insurance workflows are a proving ground for agent-ready execution.
How we help today
Map approval gaps, context gaps, evidence gaps, human review points, escalation rules, routing risk classes, and where agents should assist or be blocked.
Turn messy insurance processes into structured, agent-ready workflows with inputs, outputs, decision points, authority boundaries, context requirements, risk classes, and outcome metrics.
Test a controlled workflow where agents can recommend actions while clear business rules determine what is approved, escalated, or held for review.
Define approved sources of truth for policies, claims documents, SOPs, regulatory materials, customer records, coverage rules, and escalation standards.
Help leadership teams define how AI agents should operate inside regulated or high-trust insurance workflows.
How we prepare for the future
For insurers, AI companies, and investors exploring how agentic work may create new risk, coverage, review, and accountability needs.
Define what must be known before an agent takes action: who is acting, what context is used, what approval is required, and what evidence is recorded.
Define what evidence must be captured before, during, and after execution so actions can be reviewed, audited, or evaluated later.
Design how agent permissions are limited to a specific workflow, risk level, approval status, and business outcome.
Design the review layer that checks proposed agent actions before they affect customers, systems, money, records, or regulated workflows.
We help insurance teams use AI safely today, while preparing for the risk, review, and evidence requirements AI actions may need tomorrow.
Before agents can act, the process must be clear. Permissions must be defined. Context must be validated. Evidence must be captured. Outcomes must have an owner.