The takeaway
A useful workflow example shows what was known at the time, what happened next, and how the result was judged.
A final answer leaves out the path
Consider a hypothetical support case: a customer reports a failed delivery, an employee checks the order, a warehouse team identifies a stock issue, and a replacement is approved. A final status of “resolved” does not explain that work. The intermediate records show the questions, constraints, and handoffs.
Our view is that businesses should inventory those relationships when exploring AI data opportunities. A sequence can provide a more meaningful example of an operational task than a disconnected collection of final statuses. Whether it is useful to a buyer depends on the buyer's objective.
Describe a workflow in five parts
- Trigger: What request or event started the work?
- Available context: What information did the employee have then?
- Actions: What checks, updates, or handoffs happened?
- Decision: What choice was made, and what recorded evidence supports it?
- Outcome: What happened, and how was success or failure established?
Do not invent missing reasoning. A record can establish that an action occurred without establishing why someone chose it. Keep observed events, recorded explanations, and later interpretations separate.
Preserve time and relationships
Ask your system owner whether exports retain event timestamps and stable references between records. A case identifier might connect a support ticket, an order, and an approved refund. If those links disappear during preparation, a reader may no longer be able to follow the work.
Time matters too. Information added after a case was resolved should not be presented as information available at the start. In a hypothetical evaluation of the next action, revealing the final answer would make the exercise misleading.
Include exceptions and human review
Write down what counts as an ordinary case and what counts as an exception. Escalations, reversals, abandoned requests, and unresolved cases can help explain the boundaries of a process. Their presence should be documented, not silently treated as successful demonstrations.
A team member who understands the process should review a proposed example format. Ask whether a reader could distinguish a justified approval from a shortcut or a mistake. Human activity is evidence of what happened; it is not automatically evidence of best practice.
Begin with a workflow map
Before any export, draw up a simple list of the systems used from request to outcome. Identify the record that connects each handoff and the person who can validate its meaning. This is a scoping exercise you can do without sharing the underlying content.
NIST's AI Risk Management Framework provides broader context for evaluating AI systems and their risks. A well-documented workflow is one practical input to that conversation, not a certification that a dataset or future AI system is safe.
Sources & further reading
Sources checked October 2, 2026.