One of the key advantages of AI-driven quality control is its ability to continuously learn and adapt QC criteria. As the AI algorithms analyze large volumes of data from various sources, they can identify patterns and trends that human inspectors may not recognize. The AI system can then adjust its criteria for evaluating product quality based on this newfound knowledge. This adaptive capability allows the AI-driven QC system to continually improve its accuracy and effectiveness over time. Furthermore, by leveraging machine learning techniques, the AI system can update its algorithms based on real-time feedback and new data, ensuring that it stays up-to-date with changing manufacturing processes and evolving quality standards. This dynamic learning and adaptation mechanism of AI-driven quality control not only enhances the efficiency and effectiveness of the QC process but also enables manufacturers to stay ahead in delivering products of the highest quality to their customers.