Module 01 · 20 minutes
When Not to Use AI
Find recurring tasks worth improving by starting with the problem, not the tool.
Written and edited by Cahyanto Arie Wibowo. Last reviewed · version 1.2.
When is the idea of “When Not to Use AI” most useful?
Within Applied AI, the point of When Not to Use AI is clearer when its benefit, owner, and limits are visible. Instead of memorizing When Not to Use AI, compare one clear example with another that looks similar but works differently. You will leave with a simple way to explain When Not to Use AI and one question that tests its limits.
After this lesson
- Within Applied AI, the point of When Not to Use AI is clearer when its benefit, owner, and limits are visible.
- Use the idea of “When Not to Use AI” to interpret one realistic situation.
- Explain the limits of the concept and the information that still needs to be checked.
Start with the situation
Understand the situation first. The label can come later.
Summarizing hundreds of survey responses is safer to test than handing contract termination decisions to AI. This lesson uses the idea of “When Not to Use AI” to examine that situation without treating a single term as the answer to every problem.
Within Applied AI, the point of When Not to Use AI is clearer when its benefit, owner, and limits are visible. Find recurring tasks worth improving by starting with the problem, not the tool. Connect the term to a decision someone genuinely needs to make.
Visual model
Map the parts before choosing what to do.
Read the diagram as a map of When Not to Use AI: begin with the context, follow the connections, and inspect the highlighted point before making a decision.
Let’s see how it works
Reading the situation in practice
Instead of memorizing When Not to Use AI, compare one clear example with another that looks similar but works differently. Begin with what can be observed, then separate facts, assumptions, and open questions.
Summarizing hundreds of survey responses is safer to test than handing contract termination decisions to AI. Identify the part of the situation most closely connected to the idea of “When Not to Use AI”. Use the case as a thinking tool, not as proof that one solution fits every context.
Working definition
What it means, and when to be careful with it.
Within Applied AI, the point of When Not to Use AI is clearer when its benefit, owner, and limits are visible. Find recurring tasks worth improving by starting with the problem, not the tool. Connect the term to a decision someone genuinely needs to make.
- When Not to Use AI
- Within Applied AI, the point of When Not to Use AI is clearer when its benefit, owner, and limits are visible. Instead of memorizing When Not to Use AI, compare one clear example with another that looks similar but works differently. You will leave with a simple way to explain When Not to Use AI and one question that tests its limits.
- Boundary to check
- A frequent task is not automatically valuable or safe to automate. This mistake often appears when a label is used before the problem is understood. Write down your assumptions so another person can review them.
Pause for a moment
What evidence could change this decision?
Answer before opening the discussion. Name one fact and one assumption.
Open the discussion
Within Applied AI, the point of When Not to Use AI is clearer when its benefit, owner, and limits are visible. Instead of memorizing When Not to Use AI, compare one clear example with another that looks similar but works differently. You will leave with a simple way to explain When Not to Use AI and one question that tests its limits.
Try it on your work
Try it with one small piece of real work.
- Choose one real situation related to When Not to Use AI.
- Separate what you can observe from what you are assuming.
- Write one decision, its owner, and the evidence needed to review it.
- Name the signal that would make you stop or change direction.
Make one small decision with the idea of “When Not to Use AI”. Record your reasoning, the limits, and the signal that would make you change course. The larger module activity is: Rank ten tasks by value, risk, and repeatability. Keep the first version small enough for another person to review in a few minutes.
A tempting shortcut
A familiar term can still lead us to the wrong decision.
Why this can seem reasonable
A frequent task is not automatically valuable or safe to automate. This mistake often appears when a label is used before the problem is understood. Write down your assumptions so another person can review them.
How to check it
Instead of memorizing When Not to Use AI, compare one clear example with another that looks similar but works differently. Begin with what can be observed, then separate facts, assumptions, and open questions.
Quick practice
Make one small decision with the idea of “When Not to Use AI”. Record your reasoning, the limits, and the signal that would make you change course. The larger module activity is: Rank ten tasks by value, risk, and repeatability.
Summary
- Within Applied AI, the point of When Not to Use AI is clearer when its benefit, owner, and limits are visible.
- Use examples and evidence to test your understanding.
- Record the limits, risks, and conditions that should trigger another review.
Continue from here
- Pain, Frequency, and Value: Continue the idea from From Work to AI Opportunities with a closely related example.
- Outcomes: Connect this lesson to Product Thinking and test the idea in another context.
- Goals and Context: See how the same decision changes when viewed through Prompting.
Sources and further reading
- People + AI Guidebook: Google PAIR · official-guidebook. Primary reference for the definition, evidence, or limits discussed in “When Not to Use AI”.
- Artificial Intelligence Risk Management Framework: NIST · official-framework. Further evidence and context for checking the explanation in “When Not to Use AI”.
- Rules of Machine Learning: Google for Developers · official-guidance. Further evidence and context for checking the explanation in “When Not to Use AI”.