5 Critical Mistakes When Adopting Next-Generation Manufacturing AI (And How to Avoid Them) I've watched manufacturers invest millions in AI initiatives only to see them stall in proof-of-concept purgatory or fail to deliver projected ROI. After working on digitalization projects across multiple plants and consulting with quality control engineering and process automation teams, I've identified recurring mistakes that derail even well-intentioned AI deployments. The promise of Next-Generation Manufacturing AI —improved OEE, reduced waste, predictive maintenance that actually predicts—is real. But the path from pilot to production is littered with avoidable mistakes. Here are the five most damaging errors and practical strategies to prevent them. Mistake #1: Starting Without Clean Data Infrastructure The Error: Launching AI projects before establishing reliable data collection, validation, and integration across MES, ERP, and SCM systems.…