PRACTICAL GUIDE
How to evaluate an AI use case: criteria and next steps
A promising AI idea is not automatically a viable project. This guide outlines the questions teams should answer before starting a proof of concept.
Created with AI assistance and editorially prepared for initial orientation. This content does not replace individual professional, legal or privacy advice.
What makes an AI use case suitable?
Suitable initiatives combine a clear business benefit with available data, a definable process and realistic delivery conditions.
- What measurable problem should be solved?
- Who will use the result and how will the process change?
- Which data is available, current and permitted for the intended use?
The most important assessment criteria
A sound decision considers more than expected benefit. It brings together commercial, technical and organisational perspectives.
- Business value and expected impact
- Data readiness and data quality
- Technical feasibility and integration effort
- Risks, privacy and regulatory considerations
- Resources, ownership and delivery timeline
From assessment to next step
Start by prioritising use cases with a traceable benefit and manageable risk. This can become a focused proof of concept with clear success criteria.
- Define a hypothesis and success criteria
- Involve business, IT and relevant control functions early
- Document and prioritise findings
Frequently asked questions
How long does an AI use-case assessment take?
It depends on the data situation and complexity. A first structured assessment can provide orientation quickly, while a robust project decision needs the relevant business and control functions involved.
Does an AI assessment replace a compliance review?
No. It supports early orientation. The responsible teams must carry out legal, privacy and security reviews as appropriate for the initiative.