Legacy Data Architectures Keep Insurance AI Stuck in Pilot Mode

In Data Architecture Blocks Insurance AI Scaling, Anil Venugopal makes the case that insurance AI struggles to move beyond pilots not because models fail, but because legacy data architectures were never built for real-time operational decision-making. Systems designed for reporting and analytics cannot support AI working inside live claims and underwriting workflows.

For claims adjusters, this limitation shows up in fragmented access to policy data, loss history, photos, estimates, medical records, and notes. Venugopal emphasizes that unstructured documents contain the most valuable claims intelligence, yet many architectures treat them as static files rather than decision-driving data. Without document intelligence and shared context across systems, AI remains an add-on instead of a production tool.

The article, published on Insurance Thought Leadership, outlines four requirements for scaling AI: connecting data across silos, capturing expert adjuster judgment as training data, managing evolving claim context, and creating realistic environments for AI testing. For claims leaders, the takeaway is clear. Scaling AI depends less on new tools and more on building data foundations that let AI operate where claims decisions are actually made.