really good observation. The confidence of the output can sometimes mask the lack of grounding behind it. It almost feels like the emerging pattern is, let AI assist with generation and explanation, but keep the verification layer deterministic and measurable. Curious if you’ve seen teams building internal tooling around that, or if people are mostly relying on existing CI/testing framew

It's exactly the experience I had while building iQWEB.

I spent months trying to make an executive narrative generated by AI, but eventually moved away from that approach. The results were often inconsistent or overly generic, which made it difficult to rely on the output for serious reporting.

In the end I shifted to a fully model-driven approach where the narrative is built directly from structured signals and scoring logic. That made the reports far more accurate and evidence-based, and it keeps the output consistent from scan to scan.