In brief
What is the best way to prioritise AI use cases?
Describe each use case consistently, score it on value, feasibility, data readiness, risk and adoption with evidence rather than enthusiasm, weight the criteria to your strategy, and select a balanced set of initiatives that includes early, achievable wins and the foundations needed for larger opportunities.
AI ideation workshops are rarely short of ideas. A few hours with engaged teams can produce dozens of possibilities, from document automation to customer assistants to forecasting. The difficulty is choosing. Without a method, selection tends to follow the most senior voice, the most exciting demo or the most recent vendor presentation.
Describe every use case the same way
Before scoring, capture each idea in a consistent one-page format so that comparisons are fair:
- The problem and who experiences it.
- How it is handled today and what that costs in time, money, errors or experience.
- What the AI would do and what people would still do.
- The data required and where it lives.
- The systems it would need to connect with.
- Potential risks to people, compliance or reputation.
Score on five criteria
| Criterion | Key questions | Evidence to look for |
|---|---|---|
| Business value | How much improvement, for how many people or transactions? | Volumes, time spent, error costs, revenue or service impact |
| Technical feasibility | Is the approach proven for similar problems? | Existing solutions, research maturity, need for novel methods |
| Data readiness | Does usable, permitted data exist? | Samples, quality profile, access approvals |
| Risk | What harm could errors cause, and to whom? | Consequence of mistakes, regulatory exposure, data sensitivity |
| Adoption | Will people use it, and does it fit their work? | Sponsor commitment, user involvement, workflow integration effort |
Use a simple scale, such as one to five, with written descriptions of each level so that scorers interpret them consistently. Score risk so that a higher number means lower risk, making all criteria point in the same direction.
Weight to your strategy
An organisation seeking quick operational savings may weight value and feasibility heavily. A regulated institution may weight risk more. Agree weights before scoring to prevent adjusting them to favour a preferred idea.
Plot, then balance the shortlist
Plotting value against the combined feasibility, readiness and risk score produces four groups.
| Group | Characteristics | Approach |
|---|---|---|
| Early wins | High value, high readiness | Start soon; build momentum and credibility |
| Strategic bets | High value, lower readiness | Invest in data or research first; plan for later phases |
| Useful fillers | Lower value, high readiness | Do when capacity allows or bundle with related work |
| Deprioritise | Lower value, lower readiness | Park and revisit if circumstances change |
A healthy first roadmap usually combines two or three early wins with at least one foundational initiative, such as a data platform or governance framework, that makes strategic bets possible later.
Revisit regularly
Scores change as data improves, technology matures and early projects reveal what works. Review the priorities every quarter or half-year, update scores with evidence from delivered work and move ideas between groups accordingly.
Sources and further reading
About the author: Dr. RohitCo-Founder, Director and Chief Technology Officer, AARHIT Systems. Writes on technology strategy, AI architecture, agents, evaluation and AI governance. More articles by Dr. Rohit