Most organisations do not need another list of AI use cases. They need a disciplined way to choose one workflow where better information or automation would materially improve the work.
Begin with the workflow
Write down the trigger, the people involved, the systems they use, the decision being made, and the point where work stalls or quality drops. If the process cannot be described without saying “AI”, it is not yet understood well enough to automate.
Test the evidence
A credible opportunity has observable volume, delay, rework, risk, or missed value. Establish the current baseline before proposing an outcome. Where the baseline is missing, measurement is the first intervention.
Keep accountability visible
Name who may approve an AI-supported action, who reviews exceptions, and how the organisation can reverse or investigate a decision. Human accountability is part of the workflow design—not a disclaimer added after deployment.
Choose a bounded first move
Prefer one workflow, one accountable owner, a defined data boundary, and a short learning cycle. The aim is evidence for a production decision, not a theatrical prototype.
StrategyBees uses this discipline in an AI Opportunity Session: bring one workflow, and leave with a clearer decision about whether to measure, redesign, automate, or stop.