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Build vs Buy

Summary​

Build vs buy decisions in enterprise AI should be made per capability, not once for the entire program. This page provides a practical decision model that balances differentiation, speed, risk, and total cost of ownership.

Why This Matters​

  • Over-building wastes time and talent on non-differentiating components.
  • Over-buying can lock the business into rigid platforms and rising long-term costs.
  • A repeatable decision framework helps leadership make faster, defensible choices.

Core Concepts​

  • Capability decomposition: split AI stack into data, model, orchestration, safety, and UX layers.
  • Decision lenses: strategic differentiation, time-to-value, compliance, and operational burden.
  • Hybrid model: buy the commodity layer, build where domain-specific advantage matters.
  • Exit strategy: ensure contract, architecture, and data portability from day one.

Use this flow to set decision order, gate criteria, and rollout readiness before implementation starts.

Diagram​

Implementation Steps​

  1. Break the initiative into clear capabilities (for example: model hosting, retrieval, orchestration, monitoring).
  2. Score each capability against differentiation, urgency, risk, and cost profile.
  3. Choose build, buy, or hybrid per capability with documented rationale.
  4. Define decision gates for revisit (for example at 3, 6, and 12 months).
  5. Track realized value and technical debt to refine future decisions.

Decision Matrix (Suggested)​

  • Differentiation value: high or medium or low.
  • Time-to-value urgency: immediate or near-term or long-term.
  • Regulatory/control criticality: strict or moderate or minimal.
  • Internal skill readiness: strong or developing or weak.
  • Portability/lock-in risk: acceptable or caution or high.

Use weighted scoring per capability and publish rationale with named approver.

Realistic Example​

An enterprise legal team needed contract intelligence quickly. They bought document parsing and foundation model access, but built their own policy reasoning layer and citation pipeline. This hybrid approach cut launch time in half while preserving domain differentiation and explainability controls.

Senior Tech vs Dev Conversation​

Senior Tech: Why do build-vs-buy meetings drag on for weeks? Dev: Teams debate preference, not capability-level evidence. Senior Tech: What makes decisions objective? Dev: A scoring model with thresholds and a planned re-evaluation cadence.

UX/UI Checklist​

  • Present capability-level decision scores with clear rationale.
  • Show assumptions behind cost and staffing projections.
  • Highlight vendor lock-in and portability risk indicators.
  • Display decision age and next review date to prevent stale choices.

Common Pitfalls​

  • Making one global decision for all AI capabilities.
  • Ignoring long-term operations and integration burden in buy decisions.
  • Treating build choices as permanent instead of revisitable.
  • Letting procurement timelines drive architecture without technical review.

References and Next Steps​

  • Pair this page with Operating Model and Transformation Phases.
  • Publish a lightweight build-vs-buy decision rubric for all AI programs.
  • Run a post-decision review after the first release to calibrate the scoring model.