Illustrative deliverable
Two conflicting dates. One decision the process can apply.
A diagnosis closes with an operational boundary: what AI may prepare, who handles an exception and what evidence decides whether to continue.
Illustrative, non-client case. The scenario and conditions show the shape of a deliverable. They are not results from a client engagement.
Inputs
The request and the source disagree.
An email asks to move a delivery to 12 May. The order document states 15 May. AI compares the two sources and prepares a proposed change, but the conflict is not resolved by generating a plausible date.
Exceptions
What must become visible.
- The dates conflict.
- One source is missing or unreadable.
- The available order is not the current version.
- The request does not identify who authorised the change.
Owner
Operations accepts or stops the change.
The operations owner confirms which source is valid, corrects the proposal or stops the step. AI does not alter the order when the sources conflict. That boundary keeps accountability with the process owner.
Evidence
Measure the work around the answer.
The test records the share of requests with consistent sources, conflicts caught before a change, human time spent checking each exception, corrected errors and rework. These are proposed measures, not reported client results.
Decision
A controlled test, with a stop condition.
AI may prepare a change only when sources agree. Every conflict goes to the operations owner before the order is updated. Stop the test if conflicts are not visible before a change, no owner is available or validation takes more time than the workflow saves.
Run the test on an agreed sample, record each exception and compare time saved with validation cost. The review may extend the test, change the process without AI, gather missing evidence or stop.