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AI governance is not a document that sits on a shelf. It is the operating layer that helps an organization decide which use cases to pursue, what risks to accept, who owns the decision, and how the organization will learn from real use.
1. What is the organization actually using?
Start with visibility. Identify approved tools, employee experimentation, embedded AI features in existing software, data flows, and use cases being considered. The inventory does not need to be perfect to be useful; it needs clear ownership and a path for updates.
2. Which use cases require deeper review?
Not every use case carries the same risk. Establish practical categories based on factors such as sensitive data, customer impact, automated decisions, human oversight, material business consequences, and vendor dependence. The goal is proportional review, not a process that stops every experiment.
3. Who can approve, pause, or stop an AI use case?
Define decision rights before a high-impact use case reaches production. Teams should know who owns business value, security, privacy, legal or compliance review, model or vendor risk, and final approval. A clear escalation path is part of responsible adoption.
4. What information must be documented?
Useful records may include the purpose of the use case, data involved, vendor or model, expected users, human review, known limitations, testing approach, monitoring plan, and the person accountable for the outcome. The record should support a real decision rather than become paperwork for its own sake.
5. How will the organization learn after launch?
Governance continues after approval. Establish a review rhythm for incidents, quality, drift, user feedback, vendor changes, policy exceptions, and whether the use case is still producing the intended value.
Use frameworks as decision support
Organizations may use resources such as the NIST AI Risk Management Framework or ISO/IEC 42001 to inform their approach where appropriate. Framework alignment should reflect the organization’s risk tolerance, obligations, operating model, and available evidence.
This article is general information, not legal or regulatory advice. Explore AI Governance services or Request a Strategic Technology Session.
