Three years ago, our production line at a mid-sized automotive components facility was hemorrhaging money. We were running at 68% OEE, experiencing chronic bottlenecks in our stamping operation, and our quality team was drowning in FMEA documentation that never seemed to prevent the same defects from recurring. Like many industrial manufacturing operations, we had invested in sensors, data historians, and dashboards—but the real insights remained buried under terabytes of unused data. That changed when we took our first cautious steps into generative AI, and the lessons we learned along the way transformed not just our production metrics, but our entire approach to continuous improvement. The journey into Generative AI in Manufacturing wasn't what I expected. I had imagined a clean, consultant-led implementation with predictable milestones. Instead, what we encountered was a series of hard-won lessons, unexpected breakthroughs, and a fundamental shift in how our teams approached p...