The symptom is specific: a tool demo looks impressive but fails on real assignments. Start with the affected item and identify the decision or input that could produce this behavior.
Find the likely cause
The evaluation used polished vendor examples.
Treat this as an explanation to verify against the actual work. Look at the input, the relevant decision, and the final result together. If the evidence does not support this diagnosis, investigate the mismatch before applying a convenient but unrelated fix.
Make the targeted correction
Run representative tasks with your own approved inputs.
A content tool should be evaluated on representative work, total operating effort, and the constraints of the actual team.
Check that the repair worked
Compare the results with your actual acceptance criteria.
Repeat the check on the final version that the reader or customer will encounter. An approved draft, a preview, and a published result can differ; the acceptance decision should concern the version people actually use.
Prevent the next related failure
A separate issue to watch for is this: the cheapest AI tool creates expensive review work. Include correction, integration, and oversight time in the comparison.
Monitor the useful outcome
Track accepted output per unit of total effort, along with failure modes and workflow fit.
Keep a short record of the original symptom, the evidence behind the diagnosis, and the result of the acceptance check. That record makes the solution reusable when the same condition appears again, without assuming that every superficially similar problem has the same cause.