5 Critical Mistakes to Avoid Six months ago, our discrete manufacturing facility launched an ambitious generative AI initiative. The goal was straightforward: use AI to optimize production scheduling, reduce waste, and improve OEE. The execution was anything but straightforward. We made mistakes—expensive, time-consuming mistakes—that delayed value delivery by four months. If you're planning a similar initiative at your facility, learn from our failures. This article covers the five most common pitfalls we encountered when implementing Generative AI Manufacturing capabilities, along with concrete strategies to avoid them. These lessons apply whether you're at a large OEM like General Electric or a mid-sized supplier focused on discrete manufacturing. Mistake #1: Starting Without Clean Data Infrastructure What we did wrong : We assumed our existing ERP, MES, and PLM systems contained production data ready for AI consumption. We were wrong. Historical production orders had inconsistent status codes.…