INTRODUCTION
Artificial intelligence is broad enough to hide a poor question. Asking where to insert AI often produces compelling demos that are hard to sustain. A better question is: Which task do we repeat, where does interpretation happen and what would improve if we handled it better?
That is where a real use case begins.
1. Separate repetition from variability
Many repetitive tasks do not need AI. ‘When the form is complete, create a task and notify the owner’ is a clear conventional automation.
AI becomes relevant when the input changes and must be interpreted: free-form email, inconsistent documents, photographs, requests that need classification or knowledge retrieved by meaning.
Rule or AI?
Automation
AI
Human review or additional control
2. A good use case has an action at the end
Classifying an email is not valuable by itself. Ask what happens next: assignment, priority, field extraction, a draft response, a new record or escalation. If the output changes no decision or process, the case is not yet defined.
Example: supplier invoices
Invoices arrive in different formats. AI extracts fields and assigns confidence. Rules validate required data and amounts. Uncertain cases go to a person. AI does not ‘automate everything’; it is used exactly where variation appears.
3. Enough data does not mean useful data
- Is the information current?
- Are permissions clear?
- Does it represent the problem?
- Is there a source of truth?
- Do users know what to do with the output?
More disorganized information can increase difficulty rather than solve it.
4. Evaluate the cost of being wrong
Classifying a sales inquiry is different from making a high-impact decision. As consequences increase, supervision, traceability, limits, testing, reversibility and escalation become more important.
| Case | Variability | Error impact | Initial approach |
|---|---|---|---|
| Classify inquiries | High | Low / medium | AI + sample review |
| Extract document data | High | Medium | AI + validations |
| Move a CRM state | Low | Medium | Rules |
| Approve a sensitive action | Variable | High | Controls + human |
5. Keep the first pilot small
- One process
- One user group
- One information source
- One output
- One quality criterion
Then measure what actually happens.
6. Not using AI can be the correct decision
If a simple, auditable rule solves the problem, adding a model can increase cost and uncertainty without producing value. Choosing the right tool is not falling behind.
From context to a decision.
Map the process and design a bounded pilot
Evaluate my use case↗Answers with the full context.
Which processes are good candidates for AI?+
Processes that receive variable information, require interpretation and produce a concrete output that can be measured and reviewed.
When is traditional automation enough?+
When inputs, rules, states and actions are sufficiently defined and flexible interpretation is unnecessary.
Do I need a lot of data?+
You need relevant, usable information with suitable permissions. Volume alone does not create a good project.
Where should human review exist?+
Especially where error impact is high, uncertainty remains or the action should not execute automatically.
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
