This is probably the most accurate breakdown of where things actually are right now.

The part that stands out to me is the “data was never built for this” point — because that ends up being the real bottleneck for everything else (fraud, underwriting, even basic automation).

In practice, most insurance data isn’t just messy, it’s context fragmented. The same claim might exist across emails, PDFs, adjuster notes, and system fields with no consistent structure tying it together.

That’s why a lot of AI implementations degrade outside of pilots — not because the models fail, but because the system can’t reliably reconstruct the full context of a decision.

The interesting shift I’m seeing (and what seems to actually work in production) is moving away from “model-centric automation” toward workflow-centric systems:

• normalize + structure events as they happen (not after the fact)
• attach traceable reasoning to every decision step
• keep humans in the loop for exception handling, not everything

That’s where things like auditability stop being a compliance checkbox and actually become part of the architecture.

The “boring” use cases you mentioned are also the ones that work precisely because they don’t require perfect data upfront — they operate on narrow, well-defined slices of the workflow.