Introduction
How to evaluate, approve, and monitor AI use cases in a way that balances innovation, risk management, and real-world impact. The goal is not governance for its own sake. The goal is to make better decisions, strengthen accountability, and help AI deliver real value in the environments where it will actually be used.
1. Clarify the use case and intended outcome
Define the problem, intended benefit, success criteria, and mission connection before selecting or deploying a tool.
2. Identify affected people and potential impacts
Consider employees, residents, patients, applicants, communities, and other groups who may be affected, including equity, privacy, and access concerns.
3. Assess data, evidence, and fit for purpose
Review data quality, relevance, limitations, supporting evidence, and whether the system is suitable for the specific context.
4. Define human oversight and controls
Establish roles, review points, intervention authority, thresholds, and escalation paths.
5. Monitor performance after deployment
Track expected outcomes, unintended effects, feedback, changes, and reassessment triggers over time.