AI-assisted inspection can generate a precise measurement and still lead a manufacturer to the wrong decision. If a part is checked against an obsolete drawing revision, the instrument may record the dimension faithfully and the software may produce a polished report, but the conclusion can be invalid. The faster data moves through connected metrology, the more consequential that mismatch becomes.
Behavioral scientist Gleb Tsipursky, CEO of Disaster Avoidance Experts, argues that manufacturers adopting AI-assisted inspection should give every consequential measurement a 'version passport.' The proposal is not necessarily a new software product. It can begin as a set of fields that travel with a measurement and make its applicable conditions visible to the next decision-maker.
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 ensuring that each decision follows the correct version of the requirement.
What the passport would carry
The proposed passport would include the approved drawing or model revision, the characteristic identifier, the inspection-plan revision, the instrument or method used, the result timestamp, and the identity of the person or system that approved the disposition.
- Approved drawing or model revision
- Characteristic identifier
- Inspection-plan revision
- Instrument or method
- Result timestamp
- Identity of the approving person or system
AI can summarize an inspection report, point to 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. The arithmetic can remain correct while the conclusion is invalid.
The argument points to NIST's 2026 smart-manufacturing roadmap, which identifies integration of heterogeneous data and trustworthy operation as continuing challenges for industrial AI. A version passport targets a specific part of that problem by making the origin and applicable conditions of a measurement visible.
A limited test before integration
Quality teams can test the idea before integrating a new platform. Tsipursky recommends selecting a small set of completed inspection records, including one legitimate out-of-tolerance result, one conforming part and one example with mismatched revisions. An AI assistant would then summarize the evidence, identify the applicable revision and flag missing provenance.
Its responses should be compared with an experienced quality engineer's determination. Rather than asking only how often the assistant labels a part good or bad, the test should record whether it catches a revision mismatch, invents an unstated tolerance or can point to the approved source behind a conclusion. Those are the errors that digital handoffs often conceal.
When records disagree, ownership should be clear. A quality engineer may determine whether a measurement is valid, design engineering confirms the governing definition, and manufacturing determines whether production can continue. The escalation route should be settled before an alert appears rather than negotiated during a shipment dispute.
Why human-versus-AI comparisons are not enough
The argument differs from familiar calls for more comparisons between human and automated inspectors. Even perfect agreement can reinforce the wrong decision if both were given an obsolete specification. The reference itself needs scrutiny.
Automation can make measurement data travel farther and faster. A reliable chain of custody ensures that the drawing, feature, method and result still belong together when the decision is made. The next generation of connected inspection, the argument concludes, should make that connection impossible to ignore.