AIG: AI Underwriting + Claims Assistance at scale
Production GenAI assistance for underwriting and claims workflows, designed for adoption, governance, and measurable operational impact.
Results
Reduced cycle time for key workflows
Increased throughput and user productivity
Scaled assistance across major underwriting and claims journeys
Built foundations for reuse across teams
Context
AIG needed AI assistance embedded directly into real underwriting and claims workflows — not a demo, but a system people would trust and use at scale.
The problem
Enterprise constraints were real: security, privacy, governance, integration complexity, and a need for measurable outcomes and broad adoption across stakeholders.
The approach
We focused on workflow-first design, tight integration with existing systems, and an evaluation and rollout strategy that balanced speed with risk management. The system was built to support iteration: measure usage, improve quality, expand coverage.
What I led
I owned day-to-day technical leadership: architecture decisions, integration strategy, delivery execution, and cross-functional alignment. I translated business goals into an executable technical plan and drove the work through production realities.
Results
The outcome was production AI assistance that improved speed and usability in underwriting and claims contexts, with a platform approach that supported expansion across the business.
Lessons
Enterprise GenAI succeeds when it's integrated into workflows, measured like a product, and rolled out with a clear governance and iteration model.
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