A digital inspection result can include an accurate measurement and still be wrong for the job. Suppose a part is checked against an obsolete drawing revision: the instrument records the dimension faithfully, the software produces a polished report, and everyone receives the wrong conclusion with impressive speed.
That risk is the subject of a commentary by Gleb Tsipursky, PhD, a behavioral scientist and chief executive of Disaster Avoidance Experts, published by Metrology News. His proposal is that manufacturers adopting AI-assisted inspection give every consequential measurement a "version passport."
Why version context matters
Tsipursky points to a Metrology News account dated October 26 on persistent identification, which describes how Quality Information Framework data can preserve the identity of a measured characteristic across product definitions, inspection plans and results. The technical promise is traceability; the management challenge is making sure each decision follows the correct version of the requirement.
AI tools can summarize an inspection report, flag an apparent deviation, or recommend which cases need review. But a tool that receives results without version context may compare an observation with the wrong specification. As Tsipursky puts it, the arithmetic can be correct while the conclusion is invalid.
What a passport would carry
The passport does not have to be a new software product, the author writes. It can start as a set of fields that travel with a measurement:
- the approved drawing or model revision;
- the characteristic identifier;
- the inspection-plan revision;
- the instrument or method used;
- the result timestamp;
- the identity of the person or system that approved the disposition.
Tsipursky links the problem to the National Institute of Standards and Technology's 2026 smart-manufacturing roadmap, which he says highlights integration of heterogeneous data and trustworthy operation as continuing challenges for industrial AI. A version passport addresses one part of that problem by making the origin and applicable conditions of a measurement visible to the next decision-maker.
A small test before a platform decision
Quality teams can try the idea before integrating a new platform. The suggested method is to take a small set of completed inspection records — including one legitimate out-of-tolerance result, one conforming part and one example with mismatched revisions — and ask an AI assistant to summarize the evidence.
The assistant should be required to identify the applicable revision and flag missing provenance, and its answers compared with an experienced quality engineer's determination. Rather than asking only how often the assistant labels a part good or bad, the author recommends recording whether it catches a revision mismatch, whether it invents an unstated tolerance, and whether it can point to the approved source behind a conclusion. Those, he argues, are the errors that digital handoffs often conceal.
Ownership must be settled in advance
When records disagree, responsibility should already be clear: a quality engineer may determine whether a measurement is valid, design engineering confirms the governing definition, and manufacturing decides whether production can continue. The escalation route should be agreed before an alert appears rather than negotiated during a shipment dispute.
Tsipursky separates his argument from the familiar demand for more AI-versus-human comparisons. Even perfect agreement between human and automated inspectors can reinforce the wrong decision if both were given an obsolete specification, he writes; the reference itself needs scrutiny. Automation can move measurement data farther and faster, but a reliable chain of custody is what keeps the drawing, feature, method and result together at the moment of decision.