AI Strategy

Build vs Buy AI: A Decision Framework for Enterprises

Every enterprise faces this question with each AI initiative. Most teams default to "buy" out of urgency, or "build" out of vendor distrust. Both defaults are wrong. This framework gives you the five dimensions that actually drive the right answer.

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TL;DR

Score 5 dimensions 1–5 (strategic asset, time-to-value, regulation, TCO, talent). Sum = decision band: Build / Hybrid / Buy.

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The Framework

Five Dimensions, Scored 1-5, Summed for Decision Band

Score each dimension on a 1-5 scale. 1 means "clear build signal"; 5 means "clear buy signal". Sum the five scores and read the decision band at the bottom of this section.

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1. Strategic Asset

Is the AI capability part of your competitive moat (score 1-2) or table stakes (score 4-5)? Core moat: ranking, risk scoring, demand forecasting. Commodity: FAQ chatbot, transcription, sentiment analysis.

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2. Time-to-Value

12+ months acceptable (1-2) or 90 days needed (4-5)? Most "we need it now" demands are organizational, not market-driven. Pushing back 60-90 days often changes nothing for the business.

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3. Regulatory Fit

Strict (1-2): HIPAA patient data, banking-grade compliance, 152-FZ data residency. Standard (4-5): GDPR with DPA, SOC 2. Vendors will claim compliance — always ask: "has our specific regulator audited you?"

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4. Five-Year TCO

Build cheaper at scale (1-2) — massive usage where vendor pricing prohibitive. Build never cheaper (4-5) — moderate usage, vendor amortizes R&D. The hidden costs of build: talent retention, 24/7 ops, model updates — often 3-5× initial estimate.

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5. Talent Availability

Strong (1-2): 5+ senior ML/AI engineers with production LLM experience plus a platform team. Thin (4-5): a couple of curious engineers with Coursera courses. Most enterprises overestimate in-house AI capability by 2-3×.

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Decision Bands

Sum 5-12: BUILD.
Sum 13-19: HYBRID — buy platform, build on top.
Sum 20-25: BUY — commodity, urgent, no talent.

Hybrid Pattern

The Often-Best Answer for Mid-Range Scores

For scores 13-19, hybrid wins. The pattern: buy the platform (LLM API, orchestration framework, vector DB), build the differentiator (domain ontology, prompts, evaluation harness, specific UX). Over time you can swap the platform without rewriting your differentiator.

This is what Slavin AI delivers in most engagements: a built layer specific to the client's domain, on top of a bought platform (OpenAI / Anthropic / Google / local open-source LLMs depending on regulatory fit).

Pitfalls

Common Mistakes in the Build-vs-Buy Decision

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Scoring "Build" from Vendor Distrust

Vendor risk is a real factor in dimensions 3 (regulatory) and 4 (TCO via lock-in). It shouldn't override the other three.

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Scoring "Buy" from Perceived Speed

Vendor procurement, integration, and customization often take 6 months too. The 90-day promise is usually a sales lie.

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Not Revisiting

Scores change. Year 2 of a buy decision is when you should re-evaluate. By year 3, the right answer is often a different vendor or a build.

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One-Size-Fits-All Decisions

Different AI initiatives in your enterprise can score differently. Treat each one individually rather than picking a network-wide build/buy posture.

Takeaway

What to Apply Tomorrow

Five dimensions, scored 1-5. Sum. Decision band. Hybrid is the answer most often — buy the platform, build the differentiator. Re-score every 18 months: decisions decay. Be honest about talent; most enterprises overestimate their in-house AI capability by 2-3×.

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