Look for repeated information work
AI is often useful when people repeatedly collect, classify, summarise, transform, or route information. The workflow should have recognisable inputs, a clear output, and an accountable owner.
- Turning structured briefs into first drafts for human review.
- Classifying enquiries and routing them to the right workflow.
- Summarising long material while preserving links to the source.
- Extracting consistent fields from documents with an exception queue.
Do not automate an unclear process
If the team cannot agree on the inputs, decision rules, or definition of a good outcome, automation will reproduce that ambiguity at greater speed.
First document how the work happens today, including the exceptions people solve informally. Remove unnecessary steps, then decide which parts need deterministic rules, AI assistance, or human judgment.
Design for failure before scale
A dependable workflow makes uncertainty visible. Confidence thresholds, source references, validation rules, review queues, access controls, and audit records matter more than a polished demonstration.
- What happens when the input is incomplete or contradictory?
- Who reviews a low-confidence output?
- Can a person reconstruct why an action was taken?
- What is the consequence if the system is wrong?
Measure operational value
Track time returned, error reduction, turnaround, completion rate, and the volume of exceptions requiring human attention. A workflow that saves drafting time but creates a large review burden may simply move the work elsewhere.