A plausible instruction can still be wrong for the machine, format or document revision in front of the team. For production knowledge, a useful AI response needs a path back to the evidence and a clear boundary around what it can decide.
Give the information a home
Before introducing AI, identify the sources a supervisor already trusts: approved instructions, authorized run records and reviewed observations. Record which line and transition each source applies to, who owns it and whether it is current.
The same phrase can refer to different equipment at different sites. A system that loses those distinctions may retrieve a convincing answer for the wrong situation. Good source organization is part of the product, not an administrative step to postpone.
Make the suggestion inspectable
A proposed workflow should show the source record alongside a summary or suggested tag. The reviewer should be able to see what was retrieved, whether it applies and where uncertainty remains.
For example, a fictional suggestion might connect two notes about guide-rail availability. The reviewer still needs to inspect the original events. Similar wording is a reason to look closer, not proof that the runs share a root cause.
Define who decides
NIST’s AI Risk Management Framework emphasizes defining responsibilities for human–AI configurations and oversight. For Cadensa, that means assigning a real reviewer and a clear decision boundary.
Cadensa’s focus is assistance with organizing records, locating relevant observations and drafting material for review. It does not include automatically changing operating procedures or machine settings. Existing site approvals remain in place.
The review should allow a person to reject a suggestion and record why. Treat incorrect or unsupported suggestions as evidence for evaluating the system, rather than hiding them behind a polished answer.
Evaluate a specific task
Begin with a limited question such as retrieving relevant lessons for one transition family. Test it on an authorized sample and compare the output with the sources. Check missed records, incorrect matches and time spent verifying the response.
Agree what information may be used and who may access it before connecting any source. A useful pilot evaluates the whole review workflow, including the burden it places on supervisors.
Cadensa is at MVP stage, with one pilot already in place. The public website demonstrates the workflow with fictional data, separate from pilot records.
Take it to the line.
For one proposed AI feature, write down its source, its reviewer and the decision it must never make automatically.
Further reading
A Cadensa discussion guide. Adapt the ideas to your site’s approved procedures.
