An AI governance playbook for Chief Risk Officers in regulated energy markets.
Regulators want a paper trail. You are the one in the middle.
Energy markets sit at the intersection of safety, reliability, and public trust.
AI governance is not a compliance overlay.
Most AI governance frameworks we see were written for content moderation or HR screening.
Every model in your dispatch environment needs a model card. The model card documents what the model does, what data it was trained on, what its known failure modes are, and who is accountable for its operation.
Every consequential AI decision gets logged with inputs, outputs, model version, confidence, the human who reviewed it, and the override (if any). Logs are immutable, retained for the full regulatory window, and queryable by case ID.
If the human can't override, it is not human-in-the-loop. It is human-as-decoration.
Every model in your dispatch environment needs a model card.
Every consequential AI decision gets logged with inputs, outputs, model version, confidence, the human who reviewed it, and the override (if any).
If the human can't override, it is not human-in-the-loop.
If you are the risk leader who has to approve AI in dispatch, you do not need another framework.
No, if the governance cadence is tiered correctly. Minor changes ship in 24 hours, major changes go through committee. Operations only sees friction when the impact is high enough that they should.
Each state PUC gets its own audit pack template. The underlying artifacts are the same. The packaging is different.
The audit packs are designed in the formats those regulators already use. We have run this with one ISO and two regional utilities.
