Organizations are putting more structure around their AI ambitions: strategies, governance frameworks, testing practices, policies, controls, and clearer expectations for how AI should operate at scale. On paper, all of this can feel pretty solid. Operationalizing it across a real organization is another matter, and production usually has other plans. Once an AI use case moves into production, it must hold up against live data, actual customers, shifting conditions, edge cases, overloaded teams, and all the other realities that rarely appear neatly in a framework. That is when organizations discover how much decision confidence they actually have to show for all that preparation. This session explores how organizations build decision confidence once AI becomes part of real operations: what evidence should earn a system more responsibility, how accountability and decision rights evolve as it matures, and which signals should increase confidence or trigger more caution before AI influences real work. Attendees will leave better equipped to evaluate operational readiness and determine what must be true before AI is trusted with more consequential decisions and actions.