How do I choose between the suggestions?

Pick the one where you could tell within two weeks whether it is working. Speed of feedback matters more than size of prize at the start of a programme.

What if none of them fit?

That is a useful answer in itself — it usually means the work is judgement-heavy or the data is not accessible yet. Both are worth knowing before spending a budget.

AgenticX AI Labs

Free Tool

AI Use-Case Finder

Not sure where AI fits? Pick your industry and department for tailored, high-ROI use cases.

Your industryYour department

Pick an industry and department to see recommended AI use cases.

How the AI use-case finder works

A tool that will not show its arithmetic is worth less than one that does.

1
You describe your industry, department and the shape of the work — volume, structure, how repetitive it is.
2
That is matched against patterns from delivered work: extraction, classification, retrieval, summarisation, agentic workflow.
3
You get candidate use cases with an indication of effort and what would have to be true for each to succeed.

What it assumes

Pattern matching, not analysis of your organisation. It suggests where to look, and cannot tell you what will work.
Suggestions assume the underlying data is reachable. Where it is not, that becomes the project before any AI work starts.
Effort indications assume a competent team with access to the systems involved.

How to read the result

Take the smallest suggestion with the clearest success criterion, not the most impressive one. The most common way an AI programme dies is starting with the ambitious use case, spending two quarters on it and losing sponsorship before anything ships. A narrow win that reaches production buys the credibility to attempt the ambitious one.

Questions

How do I choose between the suggestions?: Pick the one where you could tell within two weeks whether it is working. Speed of feedback matters more than size of prize at the start of a programme.
What if none of them fit?: That is a useful answer in itself — it usually means the work is judgement-heavy or the data is not accessible yet. Both are worth knowing before spending a budget.