Learning from Implementation Failures Our first AI-driven inspection project failed spectacularly. After six months and $180,000 invested, we had a system that worked beautifully in the lab but achieved only 73% accuracy on the production floor. The vendor blamed our data. We blamed their algorithm. Eventually, we acknowledged the real culprit: we made preventable mistakes that dozens of manufacturers make when deploying AI inspection systems. Three years and five successful implementations later, I've compiled the most common pitfalls I see quality teams encounter with AI-Driven Visual Inspection . These aren't minor issues—they're project killers that waste capital, erode stakeholder confidence, and set back automation initiatives by years. If you're planning an AI inspection deployment, read this before signing purchase orders. Mistake #1: Training Data That Doesn't Match Production Reality The single biggest killer of AI inspection projects is the training dataset problem.…