From AI-Assisted Inspection to AI-Native Metrology Artificial intelligence is rapidly changing the role of metrology within manufacturing. What began as the application of AI to individual inspection tasks is evolving into something considerably more significant: measurement systems designed around artificial intelligence, data and increasingly autonomous decision-making. This transition from ‘AI-assisted inspection to AI-native metrology’ could become one of the most important developments in industrial measurement. Rather than simply using AI to improve an existing inspection process, the longer-term opportunity is to design measurement, interpretation and decision-making as part of a single intelligent workflow. Examples of this transition are already emerging across industrial inspection. ZEISS, for example, has integrated AI-based defect detection into its INSPECT X-Ray software through its ZADD Segmentation application, while KEYENCE is combining conventional rule-based vision inspection with AI-based inspection within its VS platform. Nikon has also introduced automated microscopy incorporating AI-powered image analysis. These developments demonstrate that AI is already moving beyond experimentation and into practical quality-control applications. From Automation to Intelligence Traditional automated inspection typically follows a predictable sequence. A component is presented to the measurement system, a predefined inspection routine is executed, results are compared with specifications, and a report is generated. AI-assisted inspection improves this process by introducing capabilities such as automated feature recognition, image classification, defect detection and adaptive inspection. These applications can reduce programming requirements and help systems deal with greater variation in components and manufacturing conditions. KEYENCE, for example, describes its AI vision technology as an extension of conventional machine vision, allowing AI models to learn acceptable part appearance and reduce the need for continual manual adjustment of inspection logic. Its VS platform allows traditional rule-based inspection and AI inspection to operate together. However, the underlying architecture remains essentially the same: the inspection system performs a predefined task and AI assists