Module 03 · 20 minutes
Build or Buy
Check data readiness, model capability, uncertainty, and the choice to build or use an existing service.
Written and edited by Cahyanto Arie Wibowo. Last reviewed · version 1.2.
When is the idea of “Build or Buy” most useful?
Product Thinking examines Build or Buy through its inputs, intended result, and most important failure signal. Read Build or Buy from the perspective of the people affected. What looks efficient to a system may not feel clear or fair to them. The final aim is to see the value of Build or Buy without losing sight of the people affected.
After this lesson
- Product Thinking examines Build or Buy through its inputs, intended result, and most important failure signal.
- Use the idea of “Build or Buy” 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.
A valuable idea can remain infeasible when representative data and feedback loops are missing. This lesson uses the idea of “Build or Buy” to examine that situation without treating a single term as the answer to every problem.
Product Thinking examines Build or Buy through its inputs, intended result, and most important failure signal. Check data readiness, model capability, uncertainty, and the choice to build or use an existing service. Connect the term to a decision someone genuinely needs to make.
See how the decision unfolds
Move from the situation to a choice others can review.
01
Situation
A valuable idea can remain infeasible when representative data and feedback loops are missing. This lesson uses the idea of “Build or Buy” to examine that situation without treating a single term as the answer to every problem.
02
Decision
A valuable idea can remain infeasible when representative data and feedback loops are missing. Identify the part of the situation most closely connected to the idea of “Build or Buy”. Use the case as a thinking tool, not as proof that one solution fits every context.
03
Review
A vendor demo does not prove performance on your data and constraints. This mistake often appears when a label is used before the problem is understood. Write down your assumptions so another person can review them.
Visual model
Map the parts before choosing what to do.
Read the diagram as a map of Build or Buy: begin with the context, follow the connections, and inspect the highlighted point before making a decision.
Do not rush the choice
Two ways to look at Build or Buy
Useful when
- Product Thinking examines Build or Buy through its inputs, intended result, and most important failure signal.
- Use the idea of “Build or Buy” to interpret one realistic situation.
- Product Thinking examines Build or Buy through its inputs, intended result, and most important failure signal. Check data readiness, model capability, uncertainty, and the choice to build or use an existing service. Connect the term to a decision someone genuinely needs to make.
Pause and check
- A vendor demo does not prove performance on your data and constraints. This mistake often appears when a label is used before the problem is understood. Write down your assumptions so another person can review them.
- Explain the limits of the concept and the information that still needs to be checked.
The stronger choice is the one whose evidence, owner, and limits can be explained, not simply the more sophisticated option.
Try it on your work
Try it with one small piece of real work.
- Choose one real situation related to Build or Buy.
- 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 “Build or Buy”. Record your reasoning, the limits, and the signal that would make you change course. The larger module activity is: Review the feasibility of one AI idea using the evidence available. Keep the first version small enough for another person to review in a few minutes.
Pause for a moment
What evidence could change this decision?
Answer before opening the discussion. Name one fact and one assumption.
Open the discussion
Product Thinking examines Build or Buy through its inputs, intended result, and most important failure signal. Read Build or Buy from the perspective of the people affected. What looks efficient to a system may not feel clear or fair to them. The final aim is to see the value of Build or Buy without losing sight of the people affected.
A tempting shortcut
A familiar term can still lead us to the wrong decision.
Why this can seem reasonable
A vendor demo does not prove performance on your data and constraints. 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
Read Build or Buy from the perspective of the people affected. What looks efficient to a system may not feel clear or fair to them. Begin with what can be observed, then separate facts, assumptions, and open questions.
Quick practice
Make one small decision with the idea of “Build or Buy”. Record your reasoning, the limits, and the signal that would make you change course. The larger module activity is: Review the feasibility of one AI idea using the evidence available.
Summary
- Product Thinking examines Build or Buy through its inputs, intended result, and most important failure signal.
- Use examples and evidence to test your understanding.
- Record the limits, risks, and conditions that should trigger another review.
Continue from here
- Model Capability: Continue the idea from Assessing AI Feasibility with a closely related example.
- Tools and Handoffs: Connect this lesson to Applied AI and test the idea in another context.
- Tokens and Counts: See how the same decision changes when viewed through Data Literacy.
Sources and further reading
- Rules of Machine Learning: Google for Developers · official-guidance. Primary reference for the definition, evidence, or limits discussed in “Build or Buy”.
- Data on the Web Best Practices: W3C · web-standard. Further evidence and context for checking the explanation in “Build or Buy”.
- Artificial Intelligence Risk Management Framework: NIST · official-framework. Further evidence and context for checking the explanation in “Build or Buy”.