A system that works in a pilot can break at ten times the users, ten times the data and a risk committee review. Leaders in AI lifecycle management, applications and product development discuss what keeps AI reliable and affordable as usage grows. This panel will cover how teams manage models and applications after launch, how product and engineering share ownership, and where cost, quality and governance start pulling against each other. Key Takeaways: → How to manage the full AI lifecycle, from release through monitoring, retraining and retirement → Where product, engineering and governance responsibilities should sit as usage grows → Which scaling problems show up first, and how to budget and plan for them early