Most AI-for-work demos treat every output the same: the model produces an answer, then the workflow acts on it. In skilled trades, that collapses four very different jobs: Observe — turn photos, voice notes, fault codes, and sensor readings into structured information. Retrieve — find the relevant manual section, service history, or known failure pattern. Recommend — suggest a diagnostic step or likely cause. Authorize — decide that equipment is safe to return to service, close the work order, order an expensive part, or make a promise to the customer. The first two can save real time. The third needs evidence. The fourth is where a probabilistic system can create a safety, warranty, or liability problem. A safer pattern is: let AI capture and organize the evidence make every recommendation show its source and uncertainty require a technician to confirm consequential actions preserve the original inputs and the human decision in the record The critical detail is provenance. A recommendation that says “compressor failure: 82%” is much less useful than one that also shows the fault-code history, the exact manual section, what evidence contradicts the diagnosis, and which measurement should be taken next. Context: I help run a small community focused on AI in the trades. I am interested in where the boundary should sit, not another “AI will replace technicians” argument. For people building or using these systems: which decisions are safe to automate, which should only be suggested, and which should never leave the technician’s hands? submitted by /u/Unhappy-Bunch-4594
Originally posted by u/Unhappy-Bunch-4594 on r/ArtificialInteligence