Organizations are under pressure to adopt AI quickly, but many are still trying to govern AI-enabled systems with approaches suited for more predictable environments: policies, approvals, framework mappings, testing, and after-the-fact monitoring. Those mechanisms still matter, but they are not enough when systems retrieve changing context, invoke tools, operate through delegated authority, and create consequences at speed and scale. In this live session, Cheri Hotman and Diane Jones will discuss AI Admissibility as a practical way to think about control before consequence. The focus is not on making the model “safe” in isolation, but on making the system around it governable: evaluating who or what is acting, what action is attempted, what context is influencing it, and where control must be applied before action occurs.

Participants will leave with a clearer way to evaluate AI-enabled workflows, communicate risk to leadership, and enable AI adoption without losing accountability or control.