Policy documents do not govern AI in practice.
They can define expectations, but they do not decide who owns data quality, who approves model change, who monitors drift, or who carries accountability when automated decisions affect clients or control outcomes.
That is why AI governance is not just a policy question.
It is an operating model question.
In regulated environments, four things need to be explicit:
- who owns the decision to deploy
- who owns the data lineage underneath it
- which controls must exist before the model is considered usable
- how exceptions and failures are handled in live operation
The institutions getting this right are not treating AI governance as a compliance artefact to be produced and filed.
They are building it into the day-to-day operating model so the controls survive contact with delivery.