A practical operating model for teams deploying AI across real business processes.
The risk is in the use case
Two teams can use the same model and create entirely different exposure. A writing assistant for internal drafts is not governed like a system that recommends credit decisions, handles health information, or communicates directly with customers.
The useful unit of governance is therefore the use case: the model, data, user, decision, and consequence taken together. An inventory that records only vendor names misses the point.
Assign three kinds of ownership
Business ownership defines the job and accepts the operational outcome. Technical ownership controls architecture, access, monitoring, and change. Risk ownership sets the conditions under which the use case may operate.
These roles can sit with a small number of people, but the decisions must remain distinct. A team should not be able to deploy a high-impact system merely because the prototype performs well.
Govern the decision an AI system influences, not just the model that produces the output.
Make evaluation continuous
A launch evaluation is a baseline, not a permanent assurance. Data changes, prompts evolve, model providers update behaviour, and users find workflows the original team did not anticipate.
Operational governance needs a defined evaluation set, monitored failure modes, clear thresholds, and a route for incidents and user feedback. Material changes should trigger a fresh decision to operate.
Build the evidence into delivery
Architecture decisions, evaluation results, approved data sources, access rules, and release decisions should be captured as part of the product workflow. That creates an evidence trail without asking the team to reconstruct one later.
Good AI governance is not a brake on delivery. It gives teams a repeatable path from experiment to dependable operation, with the boundaries visible to everyone involved.