INTRODUCTION
When a company begins exploring AI, the idea list grows quickly: assistants, classification, analysis, content generation, extraction and customer support. The challenge is not imagining possibilities. It is deciding which one deserves to become a project.
A strong use case has boundaries. You can name the user, input, output, success criterion and what happens when it is wrong.
1. Describe the task without mentioning AI
Complete this sentence: ‘When ______ happens, the person needs ______, but today loses time because ______.’ If the problem cannot be explained without saying ‘implement AI,’ the scope is probably still too abstract.
2. Check whether usable information exists
- Where does it come from?
- Is it current?
- Who has permission?
- Are examples available?
- Is there a correct reference for comparison?
- Does it include personal or sensitive data?
Example: internal support
A team receives repeated questions and maintains current procedures. That can support an assisted-search pilot. If documents contradict each other and no one knows which version is valid, the first project is knowledge governance, not AI.
3. Define what ‘good’ means
- Accuracy by category
- Time saved
- Escalation rate
- Corrected cases
- Internal satisfaction
- Reduction in manual search
‘It answers well’ is not a sufficient quality criterion.
From problem to measurable pilot
4. Evaluate risk and value together
High volume can look attractive, but a high-impact error requires stronger controls.
| Value | Risk | Treatment |
|---|---|---|
| High | Low | Strong pilot candidate |
| High | High | Possible with robust controls |
| Low | Low | Probably lower priority |
| Low | High | Poor initial candidate |
5. The best pilot is rarely the most ambitious
- One area
- One source
- One task
- One small user group
- One limited action
Then learn from real use.
6. Checklist for a strong case
- Concrete problem
- Identified user
- Available information
- Useful output
- Subsequent action
- Quality criterion
- Manageable risk
- Named owner
- A stop and correction path
From context to a decision.
Prioritize candidates and design a pilot
Evaluate my use cases↗Answers with the full context.
How many cases should I evaluate?+
You can list many, but explore only a few deeply and select one manageable pilot.
What makes a poor use case?+
A vague problem, missing or disordered information, no owner, an output with no action or excessive risk for a first pilot.
Must all data be perfect?+
No, but enough relevant information and a way to evaluate the output are necessary.
How do I keep the pilot from becoming endless?+
Define scope, users, sources, metrics and an evaluation date from the beginning.
SOURCES AND REFERENCES3
References consulted for this editorial review.
- AI Risk Management FrameworkNIST
- NIST AI RMF PlaybookNIST AI Resource Center
- AI Risk Management and Human-AI InteractionNIST AI Resource Center
