Most AI inspection pilots fail on scoping, not on the model. This checklist draws on the published deployments in the AI Inspection Atlas. Where a case illustrates a point, it’s linked.

Weld inspection is one of the most common targets for AI visual inspection. Welds vary naturally in appearance, so rule-based vision either misses defects or floods the line with false rejects. This guide covers only published deployments from the AI Inspection Atlas. Each result below is quoted from its source, and the original is linked.

Aerospace AI inspection gets plenty of headlines, but much less is in production than in automotive. Here is what the AI Inspection Atlas can actually source. It’s split into deployed, MRO (maintenance) and still R&D, and every number is quoted from the source.

A recent discussion started with a familiar complaint: someone said they could not get a Renishaw TP20 to report a certified ring gage correctly. Predictably, the replies split into the usual camps. Some assumed the machine had to be faulty. Others assumed operator error. Still others flatly rejected the premise, as though a CMM struggling with a ring gage were too absurd to consider.

AI in quality is no longer a future concept. Entering 2026, it’s a practical tool we can use right now.

Integrating new measurement technology into an existing quality workflow is one of the most deceptively complex tasks in dimensional metrology. Whether you’re upgrading your CMM software, moving to a new platform entirely, or simply installing a modern release after years of static processes, stakeholders will always expect one thing:

Over the last year, I’ve been deeply involved in a project that forced me to rethink how quality organizations store knowledge, retrieve information, and support the people doing the real work on the floor.