1.Perspective 1
AI governance fails as a document and succeeds as a framework teams can run: inventory, classification, approval, monitoring and evidence under one institutional rhythm.
2.Perspective 2
Generative and agentic systems add failure modes — over-reliance, prompt injection, uncontrolled tool use and opaque reasoning — but they should not sit in a parallel committee. One framework should cover classical machine learning and newer interaction patterns.
3.Risk tiers keep the framework usable
Risk tiers keep the framework usable. Low-impact advisory work should move quickly. Systems that influence rights, entitlements, credit, employment or public services need stronger documentation, human review and a path to pause.
4.Roles must stay distinct
Roles must stay distinct. Business owners accept outcome accountability, technology teams own platform and evaluation hygiene, and risk teams challenge residual exposure. When those roles blur, issues surface late.
5.Perspective 5
The framework is real only when evidence can be produced on schedule: inventory completeness, approval status, monitoring coverage and exception handling. If assurance cannot test those artefacts, the model is still aspirational.
