Many factories find that AI does not reduce defect rates for certain products because AI can only propose conditions within past production history. When improvement stalls, adjusting long‑static parameters within expert‑defined textbook ranges creates new production data, strengthens the AI model, and reveals opportunities beyond the existing history.

This article explains why AI struggles with new products in manufacturing. AI relies entirely on past data, yet factories must constantly launch new items with no production history. The only effective approaches are using similar‑product models and expert‑defined process prescriptions. Based on six years of real factory work, the article shows that true AI implementation begins with designing the environment in which AI can learn.