Module 22 · Level IV · Leader

Finding and prioritising use cases

Where AI genuinely helps a team, how to find those places, and how to choose what to do first without chasing hype.

6 lessons · about 1 hour

The module in 48 secondsCaptions on · sound optional

What’s inside

  1. 01
    Where AI genuinely helps: tasks, not jobs

    Break roles into tasks, and you'll find where AI helps, where it doesn't, and where the human checks belong. · 9 min

  2. 02
    Running a use-case discovery session

    A 90-minute format that gets the people doing the work to map their tasks, name the pain and surface realistic ideas. · 10 min

  3. 03
    Scoring value, feasibility and risk

    A simple, transparent scoring method for choosing between AI ideas, with a worked table from five fictional UK organisations. · 11 min

  4. 04
    Quick wins and strategic bets

    How to balance fast, low-risk improvements with a small number of bigger changes, and sequence them so one feeds the other. · 9 min

  5. 05
    Is your data ready?

    Six questions and a traffic-light check that tell you whether a use case has the information it needs before you commit. · 9 min

  6. 06
    Piloting properly, and avoiding hype traps

    Baselines, success measures and stop criteria set in advance, plus the hype traps that turn AI pilots into expensive theatre. · 11 min

Where this sits on your path depends on your assessment. Some people skip this module outright; others start here. The free placement assessment decides, and the first lesson of every module is free to read. Find your level →

Also at Level IV

26Writing an AI policy
An acceptable-use policy your team will actually follow: scope, approved tools, data rules, disclosure, and how to keep it current.