The huge gap between IoT and AI is not technology. It is training data. In manufacturing, AI for defect reduction requires more than collecting equipment data. Manufacturing data must be aggregated at the batch or lot level and linked to quality inspection results before meaningful training data can be created. Bridging this gap is often the key to successful AI-driven quality improvement and defect reduction.

AI cannot understand manufacturing defects with numerical data alone. Integrating process images and inspection images lets AI learn real defect causes and turn inspection into a data‑generation process.