AI governance is sometimes introduced when a product is already close to release. I look at what happens after that point, particularly as AI systems continue to change and organizations increasingly rely on third-party models and services.

One misconception I see is that governance ends once an AI model is approved for production. I see it differently. Deployment introduces a new set of responsibilities because models continue to operate in changing environments. Without continuous monitoring, regular validation, and people trained to recognize when something looks wrong, performance can slowly decline without anyone noticing until a much bigger problem appears.

Executive approval is only the beginning. The real test comes when development teams begin moving faster than governance processes can keep pace, creating a growing disconnect between policy and execution.