Most AI projects fail not because the models are bad, but because the systems around them can't scale. A prototype that works in a notebook looks nothing like a system that serves hundreds of use cases across an enterprise - different data contracts, different deployment cadences, different failure modes. This session breaks down what it actually takes to make that transition: how teams move from one-off pipelines to shared infrastructure, what governance looks like when models are running in production rather than sandboxes, and where most platform efforts stall before they deliver value.