A good first AI workflow proves more than a model can produce plausible text. It shows that intelligence can improve a real operation inside acceptable controls.
Look for repetition with judgement
Tasks that involve repeated classification, extraction, summarisation or routing may benefit when rules alone are too brittle.
Prefer visible outcomes
Define what better means: shorter handling time, fewer hand-offs, more complete preparation or easier access to trusted knowledge. Avoid measures based only on model activity.
Design for uncertainty
The workflow needs a place for confidence thresholds, human review and exceptions. A system that cannot say “this needs attention” is difficult to operate safely.
Choose representative evaluation data
Test against the variety, ambiguity and edge conditions found in real work. Evaluation should continue after deployment as inputs and expectations change.