Most production AI failures aren't model failures. They're operational - pipelines degrading silently, governance frameworks mismatched to ML workloads, and infrastructure costs that outpace the value being delivered. Governance, observability, and cost control are the same problem viewed from different angles. Teams that treat them separately end up with compliance processes disconnected from how data actually moves, monitoring that catches failures too late, and cloud bills nobody can fully explain. This session examines how mature AI teams have collapsed these into a single operational discipline across pipeline architecture, data lineage, and inference infrastructure. Key takeaways: →Why siloed approaches to governance, observability, and cost fail at production scale — and what the failure mode looks like →What ML observability actually requires to catch silent pipeline degradation and data drift → How mature teams structure cost attribution across compute, storage, and inference to make spending legible without slowing delivery