Phase 1: Prepare
Step 1 Warm-up recall
From M1.L1: name the family of a system that routes invoices by a fixed rule.
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Step 2 Learning goal
By the end of this lesson you can:
- state tests for a good AI use case (Foundation);
- apply the tests to a candidate (Core);
- explain why a poor candidate should wait (Core).
Phase 2: Teach
Step 3 A familiar example
A task done once a year by one person is rarely worth automating, however clever the tool. A task done thousands of times a month with clear inputs often is.
Step 4 Clear explanation
Four tests help to sort promising from poor use cases.
- Frequency and volume. The task is repeated often enough for savings to add up.
- Usable data. The inputs exist in a form a system can use, and you may legally use them.
- Tolerable errors. An occasional mistake is affordable or caught before harm is done.
- Measurable result. You can state the current cost, time or quality and see a change.
A candidate that passes all four is worth a value case. One that fails the second or third test should wait until data or controls improve. A candidate that fails the fourth cannot be managed: without a measure you cannot tell whether it worked.
Notice also what the tests omit: whether the technology is fashionable. Leaders often start with a tool and search for a problem; reverse that. The entry on defining project scope and managing risk describes how to bound a small first project.
Step 5 Worked example (4 marks)
Candidate: using generative AI to draft board minutes that the company secretary will review.
- Frequency: monthly meetings, modest volume. (1 mark)
- Data: meeting notes exist and are confidential, which limits tool choice. (1 mark)
- Errors: a reviewer catches them, so tolerable. (1 mark)
- Measure: hours saved per meeting. Verdict: small gain, so a low priority unless volume is high. (1 mark)
Phase 3: Practise
Step 6 Learner practice (instant-marked)
1. Which is a test for a good use case?
Without a measure you cannot manage the result.
2. A task done once a year by one person is usually:
Savings do not add up at low volume.
3. A candidate that fails the data test should:
Without usable data the system cannot work.
4. Leaders should start with:
Reverse the common habit.
Phase 4: Close
Step 7 Recap
- Four tests: volume, data, tolerable errors, measurable result.
- Fail the data or error test and wait.
- Start with the problem, not the tool.
Step 8 Exit ticket (ungraded)
Apply the four tests to one AI idea in your organisation.
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Step 9 Go further (Extension)
List three processes with high volume and a measurable cost in your organisation.
Common mistakes
| Mistake | Why it fails |
|---|---|
| Choosing the most exciting use case | Choose the one that passes the tests. |
| Ignoring where the data are held | Access and legality matter. |
| No baseline measure | You cannot show improvement. |
Limits. The four tests are a screening tool, not a full business case, which Module 2 develops.