1. Define the question before collecting everything
Write a specific problem statement with the observed issue, affected process, relevant time window, and known scope. Keep observations separate from assumptions. “Surface marks increased on Line 2 during the evening shift” is a more useful starting point than “the machine is failing.” Record what is still unknown so the investigation does not quietly treat a guess as a fact.
- Identify the case owner and the people who can make containment or disposition decisions.
- Record affected lots or orders and link the original inspection evidence.
- Set the investigation boundary and the next review point.
2. Build a reviewable evidence packet
Collect the records relevant to that question: inspection results, shift notes, process changes, maintenance events, and earlier investigations. Ask AI to summarize each item with its source, date, and relationship to the case. Missing records and conflicting timestamps should remain visible. A polished summary is not enough if the investigator cannot open the evidence behind it.
- Preserve original records alongside summaries.
- Restrict access according to the information each reviewer needs.
- Label incomplete or unverified material clearly.
3. Use suggestions to structure questions
AI can be asked to propose possible explanations and identify evidence that would support or contradict each one. Present these as hypotheses, not findings. The investigator should reject weak suggestions, request missing information, and decide which checks are appropriate. A repeated symptom in an older case can be a useful lead, but it does not establish that the cause is the same.
- For each hypothesis, list supporting evidence, contradictory evidence, and unanswered questions.
- Keep proposed checks separate from approved work instructions.
Illustrative example: recurring surface marks
Imagine a fictional packaging line where inspection notes describe recurring surface marks. A reviewer asks AI to assemble recent defect observations, changeover notes, and maintenance events. The draft timeline highlights a roller adjustment shortly before one occurrence, but also shows another occurrence before that adjustment. The reviewer therefore keeps several explanations open instead of declaring the adjustment the cause.
The team chooses the next checks using its own procedures. AI helps prepare a comparison of the evidence collected; the quality owner decides whether a conclusion is supported and whether any proposed action can proceed. This example describes a possible workflow, not a customer result.
4. Record decisions and revisit the result
Record the accepted explanation, unresolved uncertainty, decision owner, and reason for each action. Give follow-up work an owner and a review date. If the issue returns, the next investigator should be able to see what was checked and why the earlier decision was made. Keep rejected hypotheses where they add useful context, without turning them into established facts.
Start with one evidence-to-review handoff
Choose a recurring investigation type and map how evidence reaches its reviewer today. A sensible first experiment is an AI-prepared evidence packet with source links and a required human review. Define useful measures in advance, such as preparation time, missing information at review, and corrections needed in the draft. Use the results to decide whether to extend the workflow.