Behavioral scientist Gleb Tsipursky argues that a digital inspection result can contain an accurate measurement and still be wrong for the job. A part checked against an obsolete drawing revision may be measured faithfully, reported cleanly and approved quickly, while the decision rests on the wrong requirement. In AI-assisted inspection, that failure mode is not merely a data-quality problem. It is a version-control problem that can make a polished conclusion less trustworthy than the raw observation.
Tsipursky’s proposal is a “version passport” for every consequential measurement. The term is deliberately modest. The passport need not be a new software platform. It can begin as a set of fields that travel with a result: the approved drawing or model revision, the characteristic identifier, the inspection-plan revision, the instrument or method, the result timestamp, and the identity of the person or system that approved the disposition. Together these fields tell the next decision-maker which requirement was in force and how the result was produced.
Why connected metrology raises the stakes
The argument starts from a familiar industrial risk. A measurement can be numerically correct and still support an invalid conclusion when the specification it is compared with has changed. Connected metrology increases the distance and speed at which that error can travel. Inspection data moves from product definition to inspection plan to result, and then into reports, dashboards and AI summaries. Each handoff creates an opportunity for a result to become detached from the revision that gave it meaning.
Tsipursky points to persistent-identification work involving Quality Information Framework data, 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 ensuring that each decision follows the correct version of the requirement. Those are related but not identical goals. A system can trace a result without guaranteeing that the result is being judged against the right drawing revision.
AI adds another layer of speed. An assistant can summarize an inspection report, point to an apparent deviation or recommend which cases need review. But if the tool receives results without version context, it may compare an observation with the wrong specification. The arithmetic can be correct while the conclusion is invalid. A fluent summary can therefore conceal the very mismatch that a quality engineer needs to see.
The National Institute of Standards and Technology’s 2026 smart-manufacturing roadmap highlights integration of heterogeneous data and trustworthy operation as continuing challenges for industrial AI. A version passport targets a specific part of that problem: it makes the origin and applicable conditions of a measurement visible to the next decision-maker.
The passport as a minimal data contract
The proposed fields are not exotic. They correspond to product-data revisions, characteristic identifiers, inspection-plan identifiers, method descriptions, timestamps and approval identities. The passport idea asks that these facts travel together, in a form the next human or machine decision-maker can inspect.
This is why the proposal is better understood as a data contract than as a product category. It does not require every measurement to carry a large document. It requires the small set of facts needed to establish provenance and applicability. The passport makes the origin and applicable conditions of a measurement visible at the point of decision. That visibility matters when an AI assistant is asked to summarize evidence, flag a deviation or recommend a review path.
Testing before platform integration
Tsipursky proposes a practical test that quality teams can run before integrating a new platform. Select a small set of completed inspection records. Include one legitimate out-of-tolerance result, one conforming part and one example with mismatched revisions. Ask an AI assistant to summarize the evidence, identify the applicable revision and flag missing provenance. Then compare its responses with an experienced quality engineer’s determination.
The evaluation should not stop at how often the assistant labels a part good or bad. That metric can reward agreement while missing the more serious errors. The team 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. These are the errors that digital handoffs often conceal. They are also the errors that a version passport is meant to expose before a shipment dispute or a production decision turns them into a costly argument.
Ownership when records disagree
The proposal also assigns clear ownership when records disagree. A quality engineer may determine whether a measurement is valid, while design engineering confirms the governing definition and manufacturing determines whether production can continue. The escalation route must be settled before the alert appears, rather than negotiated during a shipment dispute. Otherwise, the version passport may reveal a conflict without giving anyone the authority or process to resolve it.
This governance point is central to the argument. A passport is not a substitute for engineering judgment. It is a way to make the relevant judgment visible and auditable. It tells the quality engineer which revision was applied, the design engineer which definition governs and the production team whether the evidence supports continued work. Without ownership, extra metadata can become another layer of ambiguity.
Beyond AI-versus-human comparisons
Tsipursky distinguishes his argument from the familiar demand for more comparisons between AI and human inspectors. Even perfect agreement between a human and an automated inspector can reinforce the wrong decision if both were given an obsolete specification. The reference itself needs scrutiny. A test that asks only whether the AI matches the engineer may therefore miss the shared assumption that caused the error.
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, in this view, should make that connection impossible to ignore. The version passport is a small, concrete step toward that goal: not a claim that AI inspection is unsafe, but a recognition that accurate measurement is only one part of a valid quality decision.