The strongest use case is rarely the one with the most impressive demonstration. It is the one where a defined group of users has a repeated problem, the necessary data and authority exist, the benefit can be measured and the failure modes can be controlled.
Start with the work, not the model. Identify repeated friction, delay, rework, avoidable manual handling or decisions that already consume time. Then ask whether artificial intelligence is necessary. A rules-based workflow, search improvement, process change or conventional automation may be cheaper and easier to control.
A useful shortlist should record
- intended purpose;
- users and affected people;
- accountable owner;
- current process and baseline cost;
- data and system dependencies;
- supplier and model dependencies;
- expected value;
- unacceptable outcomes;
- human review;
- evidence needed for approval;
- decision to build, buy, integrate, defer or stop.
The opportunity should be rejected or deferred when ownership is unclear, the data cannot be used lawfully, the outcome cannot be evaluated, a failure would be unacceptable without review, or the implementation cost exceeds the plausible value.
The final output is not a list of ideas. It is a decision-ready portfolio with one or two bounded next steps and explicit reasons for not pursuing the rest.
DDAI view: an opportunity sprint should reduce the number of projects, not create a longer wish list.