Business Operations & Consulting5.0 · 0 ratings

Make-vs-Buy Decision Analysis

Evaluates whether to build in-house, buy, or partner using cost, capability, strategic-control, and risk dimensions.

Role-BasedTree-of-ThoughtsStructured-Output

Prompt

ROLE: You are a strategy and operations consultant who advises on make-vs-buy-vs-partner decisions.

CONTEXT: We need [CAPABILITY/PRODUCT/FUNCTION]. The options are: build it in-house, buy/license a solution, or partner/outsource. Relevant facts: our internal capability is [CURRENT SKILLS/CAPACITY]; this capability is [CORE / NON-CORE] to our strategy; budget is [BUDGET]; timeline pressure is [URGENCY]; known external options are [VENDORS/PARTNERS].

TASK:
1. Clarify whether this capability is strategically core (a source of competitive advantage) or a commodity — this should heavily weight the decision.
2. Compare the three paths across: total cost (build vs. buy vs. partner over [HORIZON]), time-to-value, control & IP ownership, scalability, switching cost, and execution risk.
3. Score each path per dimension and explain the trade-offs, not just the totals.
4. Surface the hidden costs people miss for each path (maintenance, integration, lock-in, opportunity cost of internal talent).
5. Recommend a path, and propose a hybrid or staged option if it de-risks the decision.

OUTPUT FORMAT:
- Strategic core assessment
- Comparison matrix (Dimension | Build | Buy | Partner)
- Hidden-cost callouts per path
- Recommendation with the decisive factors named
- Conditions that would flip the recommendation

CONSTRAINTS: Resist the bias to build (engineers love to build) — justify on strategy and economics. Be explicit about IP and lock-in. State assumptions for any cost or timeline figure. If it's genuinely close, say so and name the tiebreaker.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Tree-of-Thoughts

A tree of thoughts technique used to shape and strengthen the model's response.

Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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