Prioritisation guide
Prioritising AI means deciding which problem deserves investment before choosing the tool.
We compare opportunities through a common matrix to identify cases with a real need, frequent users, sufficient data and an improvement that can be verified.
Direct answer
A strong use case combines impact, feasibility and adoption.
Priority increases when the problem is frequent, the outcome matters, sufficient data exists, risk is manageable and the team can incorporate the solution into its work.
- Impact and frequency
- Data and technical feasibility
- Risk, adoption and measurement
01 / Criteria
Prioritisation prevents investment in attractive but low-value cases.
A strong opportunity combines a clear need, frequent users, sufficient data and an improvement that can be measured.
FAQ
Frequently asked questions
Which AI use case should be prioritised first?
The one that combines a frequent need, measurable impact, available data, controllable risk and implementation capacity.
Should a high-impact opportunity always start first?
No. A high-impact opportunity may first require better data, permissions, processes or adoption capacity.
Content reviewed: 29 July 2026