AI Governance
AI Governance Before Model Selection
Responsible AI starts with use-case governance, not with the model catalogue.
AI adoption conversations often begin with tools, models, and vendors. In regulated enterprises, that sequence is risky. Before model selection, leaders need clarity on the use case, decision impact, data sensitivity, human accountability, policy boundaries, and expected controls.
AI governance is not a brake on innovation. It is a way to separate viable use cases from unclear experiments and to create confidence that adoption decisions can withstand executive and regulatory scrutiny.
The most practical starting point is a use-case intake model: what decision or workflow is affected, what data is used, what risks are introduced, who owns the outcome, and what monitoring is required.
Key takeaways
- Model selection should follow use-case qualification.
- AI risk ownership must be explicit before deployment.
- Responsible AI requires governance routines that executives can understand and operate.