Choosing AI use cases: find and prioritise the right applications
The best first AI use case is rarely the most spectacular one — it is the one that combines measurable value with good feasibility, existing data and manageable risk. This page shows where good ideas come from and gives you a transparent six-criteria matrix to make AI use cases comparable.
Where good AI use cases are found
Most economically worthwhile use cases sit in these six patterns. Walk through them with the business departments.
Documents that get retyped
Invoices, delivery notes, forms and emails whose contents are keyed into systems by hand.
Knowledge people search for
Manuals, policies and past cases where staff look for answers every day.
Recurring communication
Enquiries, quotes and follow-ups answered along similar patterns.
Visual inspections
Quality checks where people inspect images, parts or documents for defects.
Planning & forecasting
Demand, stock, staffing or maintenance planned on gut feeling today.
Content at volume
Product copy, translations and variants that must come out in consistent quality.
The scoring matrix: six criteria, 1 to 5 points each
Score every candidate on all six criteria. The total (6 to 30 points) makes ideas comparable. Two rules prevent false starts: a use case with data readiness 1 becomes a data project first, and a use case with risk 1 needs a human review step before it qualifies.
| Criterion | 1 point | 5 points |
|---|---|---|
| Business value | rare, barely measurable effect | frequent, clearly measurable saving or revenue |
| Feasibility | research question, no proven approach | proven solutions exist |
| Data readiness | data missing or inaccessible | data available, accessible, usable quality |
| Risk (high = low risk) | errors would be serious, no control | errors are noticed and easy to correct |
| Time to results | results only after many months | pilot demonstrable in 6 to 12 weeks |
| Adoption | team sceptical, no owner | team wants it, ownership is clear |
Worked example
An illustrative example of a mid-sized company with four ideas — not real client results. Order of points: value, feasibility, data, risk, time, adoption.
| Use case | Points | Total | Assessment |
|---|---|---|---|
| Automatically read incoming invoices | 4 · 5 · 4 · 4 · 5 · 4 | 26 | Good first pilot |
| Knowledge assistant for service | 4 · 4 · 3 · 4 · 4 · 4 | 23 | Good, after data preparation |
| Camera-based quality inspection | 5 · 3 · 2 · 3 · 2 · 3 | 18 | Collect image data first |
| Fully automated replies to customers | 4 · 3 · 3 · 1 · 3 · 2 | 16 | Only with human approval |
Common selection mistakes
- Starting with the most spectacular rather than the most economical use case.
- Checking the data only once the project is running.
- No baseline measured — then success cannot be shown.
- Nobody in the business owns it; everything hangs on IT or external experts.
- Starting too many use cases at once instead of finishing one successfully.
AI use cases — common questions
What is an AI use case? +
An AI use case is a concrete application where artificial intelligence takes over or supports a task in a business process — for example reading invoices, answering questions from internal documents or inspecting parts for defects.
How do I find the right AI use case? +
Collect ideas with the business departments along typical patterns such as retyped documents, searched-for knowledge or visual inspections. Then score each idea on value, feasibility, data readiness, risk, time to results and adoption, and start with the candidate that scores solidly across the board.
Which AI use cases are good for getting started? +
Good starting points are often document processing, knowledge assistants over internal documents and automating recurring communication — because data usually exists and errors are easy to spot.
How many use cases should you start at once? +
One, at most two, to begin with. A successfully completed pilot builds experience, trust and a baseline for the next initiatives.
Who should score the use cases? +
Ideally together: business departments know value and process, IT knows data and systems, and an external AI expert can assess feasibility and effort realistically.
Is the matrix scientifically validated? +
No. It is a practical decision aid with openly documented criteria, not a validated instrument. Above all it helps structure discussions and make assumptions visible.
Found your use case? Find experts who have delivered it before.
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