A digital inspection result can contain an accurate measurement and still be wrong for the job. If a part is checked against an obsolete drawing revision, the instrument can record the dimension faithfully and the software can produce a polished report while the organization reaches the wrong conclusion faster. Gleb Tsipursky, a behavioral scientist and CEO of Disaster Avoidance Experts, argues that manufacturers adopting AI-assisted inspection should attach a “version passport” to every consequential measurement.
Connected metrology makes this failure mode more consequential, Tsipursky writes. He points to persistent-identification work around the Quality Information Framework, which 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 each decision follow the correct version of the requirement.
The passport need not be a new software product, according to the proposal. It can begin as a set of fields that travel with a measurement:
- approved drawing or model revision;
- characteristic identifier;
- inspection-plan revision;
- instrument or method;
- result timestamp;
- identity of the person or system that approved the disposition.
Version context matters because AI can summarize an inspection report, highlight an apparent deviation, or recommend cases for review. A tool that receives results without that context may compare an observation with the wrong specification. The arithmetic can remain correct while the conclusion is invalid. Tsipursky notes that the National Institute of Standards and Technology’s 2026 smart-manufacturing roadmap identifies integration of heterogeneous data and trustworthy operation as continuing challenges for industrial AI. A version passport addresses a specific part of that problem by making a measurement’s origin and applicable conditions visible to the next decision-maker.
Quality teams can test the idea before integrating a new platform. Tsipursky recommends selecting a small set of completed inspection records that includes one legitimate out-of-tolerance result, one conforming part, and one example with mismatched revisions. An AI assistant should summarize the evidence, identify the applicable revision, and flag missing provenance. Its responses should be compared with an experienced quality engineer’s determination.
The test should not stop at how often the assistant labels a part good or bad. It should record whether the assistant catches a revision mismatch, whether it invents an unstated tolerance, and whether it can point to the approved source behind a conclusion. Those errors are the ones that digital handoffs often conceal, according to the article.
Ownership also needs to be settled in advance. A quality engineer may determine whether a measurement is valid; design engineering confirms the governing definition; manufacturing decides whether production can continue. The escalation route should be agreed before an alert appears rather than negotiated during a shipment dispute.
Tsipursky distinguishes the argument from familiar calls for more AI-versus-human comparisons. Even perfect agreement between human and automated inspectors can reinforce a wrong decision if both were given an obsolete specification. The reference itself needs scrutiny. Automation can make measurement data travel farther and faster, but a reliable chain of custody must keep the drawing, feature, method, and result together when the decision is made.