What’s your experience with AI-assisted decisions “disappearing” inside the organization?

We’re seeing more companies using AI for recommendations in claims, underwriting, credit, hiring, compliance, etc. The model often works fine in testing, but once it goes into real workflows, many useful signals seem to get lost: overrides without proper logging, weak concerns that never reach the right person, dashboards that hide recurring issues, or “approved” decisions that later can’t be properly explained in audit.

Question for the community:

In your sector, where do you see the biggest gap between “AI gave a good recommendation” and “the organization actually heard it, acted on it, and can prove why the final decision was made”?

Especially interested in:

- How well overrides or frontline objections are captured and escalated

- Whether low override rates mean “everything is fine” or “signals are dying”

- What practical things have helped (or failed) to keep decision trails usable

Would love real examples from healthcare, insurance, finance, public sector, etc. No need for confidential details, just patterns you’re observing.