A commentary by behavioral scientist Gleb Tsipursky argues that manufacturers adopting AI-assisted inspection should attach a “version passport” to every consequential measurement. The proposal addresses a failure mode in digital metrology: an inspection result can contain an accurate measurement and still be wrong for the job if the part was checked against an obsolete drawing revision. The instrument records the dimension faithfully, the software produces a polished report, and the wrong conclusion can travel with impressive speed.
Connected metrology raises the stakes. Quality Information Framework data can preserve the identity of a measured characteristic across product definitions, inspection plans and results, the commentary says. That supports traceability, but it also creates a management challenge: each decision must follow the correct version of the requirement. A version passport is intended to make the origin and applicable conditions of a measurement visible to the next decision-maker.
Passport fields
The passport need not be a new software product, Tsipursky writes. 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.
The concern is not arithmetic alone. AI can summarize an inspection report, point to an apparent deviation or recommend cases for review, but a tool that receives results without version context may compare an observation with the wrong specification. The arithmetic can be correct while the conclusion is invalid. The commentary cites the National Institute of Standards and Technology’s 2026 smart-manufacturing roadmap, which highlights integration of heterogeneous data and trustworthy operation as continuing challenges for industrial AI.
A small pilot
Quality teams can test the idea before integrating a new platform. The commentary 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 then be compared with an experienced quality engineer’s determination.
The evaluation should not stop at how often the assistant labels a part good or bad. Teams should record 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 are the errors that digital handoffs often conceal, according to the commentary.
Clear ownership is also required when records disagree. A quality engineer may determine whether a measurement is valid, design engineering may confirm the governing definition, and manufacturing may decide whether production can continue. The escalation route should be settled before an alert appears rather than negotiated during a shipment dispute.
Tsipursky distinguishes the proposal from familiar 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 argues. The reference itself needs scrutiny. Automation can make measurement data travel farther and faster; a reliable chain of custody should keep the drawing, feature, method and result together when the decision is made. The next generation of connected inspection should make that connection impossible to ignore.