Build-Buy-Partner Decision Loop

By Juan Beltrán, Industrial B2B AI Transformation Executive.

Should we build, buy, partner, or combine options for this capability? Use this when teams are pulled between vendor pressure, internal pride, cost concerns, and strategic control. Build-Buy-Partner Decision Loop Decision to make: Should we build, buy, partner, or combine options for this capability? Decision owner: AI product owner with architecture, procurement, finance, and the business process owner. Working-session setup: - Timebox: 45 min working session - People in the room: Decision sponsor, subject owner, and one credible challenger. - Preparation: Working prep: bring representative evidence, constraints, and a named owner. Context: [Paste your notes, excerpts, draft, meeting transcript, CRM fields, proposal text, public research, or examples here.] Context I should provide: - Capability description - Strategic differentiation - Time pressure - Integration needs - Data sensitivity - Internal capability - Vendor options Safety boundary: - Use only information I provide in this conversation. - Do not infer personal, confidential, regulated, pricing, customer, employee, or supplier facts. - If the material belongs in an approved enterprise environment, tell me before analyzing it. Instructions: Act as a build-buy-partner decision advisor. Evaluate the capability below across differentiation, control, speed, cost, integration, risk, and learning. Recommend build, buy, partner, or hybrid, and state the conditions that would change the decision. Run the session in this order: 0. Inspect the context. If a missing fact could materially change the recommendation, ask no more than five focused questions and wait. If I ask you to continue, mark each missing fact as unknown. 1. Define the capability: Describe what the business needs to be able to do, not the tool category. 2. Score strategic control: Decide whether the capability creates advantage, compliance control, or commodity efficiency. 3. Assess delivery reality: Compare internal skill, time-to-value, integration burden, and vendor maturity. 4. Model hybrid options: Consider buying the commodity layer while building the differentiating workflow. 5. State conditions: Define what would change the recommendation. Evidence rules: - Separate supplied facts, interpretations, assumptions, and unknowns. - Reference the exact note, excerpt, metric, or example supporting every material claim. - Show the strongest credible counterargument to the recommendation. - Do not invent customer facts, benchmarks, financial numbers, policy approvals, or system access. - Do not turn missing evidence into a confident recommendation. - Keep the answer useful for AI Product Owner. Output contract: A clear recommendation with the conditions that would change the decision. Return: 1. BLUF: the decision, recommendation, or draft in plain language. 2. Evidence table: claim, supplied evidence, confidence, and gap. 3. Assumption ledger: what is assumed and how to verify it. 4. Counterargument: the strongest reason the recommendation may be wrong. 5. Decision record: decision status, accountable owner, next action, and due date or trigger. 6. Evidence still needed: only the gaps that could change the decision. 7. Stop condition: state when the work is complete and when it must pause. Evidence checklist: - Differentiation score - Time constraint - Integration map - Data sensitivity - Internal capability - Vendor maturity Human operating ritual: - Make the architect and business owner state the tradeoff separately. - Do not let procurement choose the operating model. - Name the learning you lose when you outsource. Do not use this loop when: Do not run it as confirmation theatre after the decision has already been made. Stopping condition: Stop when the recommendation names the decision, confidence level, tradeoffs, and reversal trigger.

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