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.

Most AI governance frameworks do not fail because they are poorly designed. They fail because, as product development accelerates, governance gradually falls outside the execution process. The challenge is keeping governance connected to how AI is actually built and deployed.

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.