The question a governance framework has to answer is simple to state and hard to operationalise: if an AI system gets something wrong, who is accountable, and can the organisation prove what the system actually did? Without that answer in place before deployment, AI projects in regulated industries stall in procurement, or get pulled back after the fact when a risk committee asks a question nobody prepared for.
What it actually covers
In practice, governance means defining a risk classification for each AI use case, deciding where a human has to stay in the loop rather than the system deciding alone, building an audit trail detailed enough for a regulator or compliance officer to reconstruct a decision path, and setting up ongoing monitoring so a model that drifts or degrades gets caught before it causes damage, not after.
Why it has to be an architecture decision, not a final step
Governance retrofitted onto a finished system is expensive and often incomplete, because audit logging, explainability, and human checkpoints are structural choices, not settings switched on later. This is one of the six dimensions Kelriva's AI Readiness Assessment evaluates directly, because organisations that treat it as a final compliance review consistently end up rebuilding.