AI Consulting
How to Prioritise AI Use Cases Without Chasing Hype
A practical scorecard for comparing AI opportunities by business value, readiness, risk, ownership and adoption effort.
Start with the work that needs to change
An AI use case is not a model or a product feature. It is a defined change to a task, decision or service. The clearest opportunities describe who does the work, what information they use, where delay or inconsistency occurs and how a better outcome will be measured.
Beginning with the work prevents teams from building demonstrations that cannot be adopted or supported.
Use a balanced opportunity scorecard
- Business value: Is there a meaningful reduction in effort, risk, delay or missed opportunity?
- Frequency and scale: Does the task happen often enough to justify change?
- Information readiness: Are the examples, data and knowledge sufficiently reliable and accessible?
- Risk and reversibility: Can uncertain output be reviewed, corrected and contained?
- Ownership and adoption: Is there a process owner who can change the surrounding workflow?
A smaller opportunity with clear ownership and good evidence often creates more value than a larger idea with uncertain data and no route into daily work.
Create a shortlist, not a wish list
Compare opportunities using the same scoring scale, record assumptions and identify the evidence needed to improve confidence. Select one or two candidates for deeper discovery rather than trying to prototype every idea.
The next step should produce a process map, representative examples, a risk assessment, an evaluation plan and a clear decision about whether to build.
Turn the guidance into a practical next step
Use the article as a starting point for a focused review of your process, platform or AI opportunity. Bring representative examples, current constraints and the outcome you want to improve.
Discuss your requirements