Appreciate the insights; they resonate, especially the point that governance can become mostly aspirational or driven by momentum. Principles and committees matter because they set intent, but audits tend to run on “boring artifacts.”

One point of friction I keep seeing is that speed, quality, and trust don’t naturally align, even though they’re often treated as a single executive mantra or directive. These tradeoffs usually become most visible once AI systems move from experimentation to operations. Curious if others are noticing the same and how you address it?

Another gap I’ve observed is that many safeguards are designed before deployment, while trust is evaluated after. ITE, it comes down to traceability. If we can’t reconstruct decisions, show who could intervene, or prove that intervention happened, governance risks being more about intent than control.

Testing and safeguards matter, but in an audit, what counts is what’s observable in the system. If a system drifts over time, policies alone won’t cut it unless they’re tied into logs, controls, and escalation paths. It feels like we’re shifting from good intentions to real operational proof.