Build the inventory before the framework
Without a usable inventory, governance operates on assumptions. The inventory should connect AI systems and use cases to business purpose, owner, provider, data, affected stakeholders, risk classification and current control status.
Ownership must follow the lifecycle
AI governance needs clear responsibility for intake, classification, approval, change, monitoring, incidents and retirement. Accountability becomes weak when ownership exists only at project start or only within a technical team.
Classification should drive control depth
Not every AI use case requires the same review. Risk, regulatory status, data sensitivity, business impact and level of autonomy should determine the evidence, approval and monitoring required.
ISO 42001 can formalise the operating model
ISO 42001 provides management-system structure for AI governance, including policy, objectives, roles, risk management, operational controls, performance evaluation and continual improvement. It is most useful when the underlying operating model is already understandable.
Evidence should show how decisions were made
A strong governance record shows why a use case was approved, what risks were considered, what controls were required, what changed over time and how performance or incidents are monitored. This turns AI governance from policy into demonstrable management control.