Build / Buy / Partner (AI Capabilities)

Overview

The core mechanism: build only the layer that differentiates you and depends on data only you hold; buy or partner for everything else — and put maintenance and model-replacement cost on the ledger before comparing.

Two failure modes recur. Teams build generic capabilities (support chatbots, OCR, transcription) in-house and turn them into a liability. Or they outsource the part that actually differentiates them, and data and customer relationships end up locked inside a vendor. AI sharpens both: foundation models are superseded quickly, so an in-house "lead" can be erased by the next general-purpose model or a vendor's native feature; and ML systems carry hidden maintenance costs far larger than the initial build (Sculley et al., NeurIPS 2015: "it is common to incur massive ongoing maintenance costs in real-world ML systems").

The differentiation test is Prahalad & Hamel's core-competence logic (HBR, 1990) applied to AI; the lock-in test is Shapiro & Varian's switching-cost analysis (Information Rules, 1998).

Compose with: switching-costs (lock-in assessment) · premortem (stress-test before signing) · theory-of-constraints (confirm this is the bottleneck first) · proprietary-data-moat (is the "unique data" actually a moat?) · ai-pilot-to-scale (run the chosen option as a gated pilot).

When to Use

• Before committing engineering headcount, an annual contract, or a data migration to an AI capability
• After a vendor raises prices, changes terms, or is acquired — re-evaluate
• The team wants to "train our own model," or sales is pushing an "all-in-one platform"

When NOT to use: low monthly fee, cancel any time · capability unrelated to business outcomes · regulation already mandates the solution.

Coaching Novices (Adaptive Front Door)

• Engine mode: user has a concrete capability and candidate options → run The Process directly.
• Coach mode: user is unfamiliar → one step at a time. Each [WAIT] is a hard stop — output only that step's question.

1. What it is. "Build only the layer that makes customers choose you; buy or partner for the rest."
2. Check fit. "Can you exit this commitment within a month at low cost? If yes, you don't need this."
3. Elicit the real case. "Which capability, serving which workflow, and what are the candidate options?"

[WAIT — do not advance until user responds]

4. Run steps 1–3: the differentiation test and data/IP control.

[WAIT — do not advance until user responds]

5. Name the insight. The user states the verdict (build/buy/partner) and the re-evaluation trigger.

[WAIT — do not advance until user responds]

The Process

Output: Decision matrix + chosen option + exit plan

1. Define the capability. Write the capability, the workflow it serves, and the success metric (e.g. first-response time on tickets). Gate: no metric → stop; set a baseline first via ai-pilot-to-scale.
2. Differentiation test. Ask: if a competitor used the same off-the-shelf tool, would customers notice a difference? No → default to buy. Gate: keep "build" on the table only if the answer is yes and the difference comes from your proprietary data or process.
3. Data & IP control. List the data that flows in and what each contract says about training use, retention, deletion and export. Gate: core customer data would be used for training, or cannot be exported → that option is out.
4. Time to value. Estimate weeks to first usable output per option. If build takes more than ~3× as long as buy, the differentiation value must cover the gap.
5. Three-year TCO. License/usage fees + integration + maintenance headcount + evaluation + rework when the underlying model is replaced + exit/migration cost. Gate: maintenance and model replacement not costed → no comparison allowed.
6. Lock-in & vendor risk. Switching cost, single-vendor dependency, vendor viability and roadmap, SLA. Require exportable formats and a model-swappable interface.
7. Score the matrix and choose. Weighted dimensions (differentiation, control, time, cost, lock-in, risk); write the exit plan and the re-evaluation triggers.
8. Stop-rule: if the capability has not yet been shown to create value, run a minimal buy pilot first, then return to step 2.

Output template: Capability | Metric | Differentiated? (yes/no + evidence) | Data terms | Weeks to value | 3-yr TCO (build/buy/partner) | Lock-in score | Choice | Exit plan | Re-evaluation trigger

→ Method in Action: BloombergGPT and the general-purpose models (2023)

Applying It Well

1. Buy by default; build carries the burden of proof. Evidence of differentiation is customer behavior, not team interest.
2. Partner = shared data or revenue in exchange for capability. Write data ownership and post-termination handling into the contract first.
3. Treat the model as a replaceable part. Build or buy, keep your own evaluation set and an abstraction layer, so swapping models does not mean rewriting the business logic.
4. Re-run on every major model release. A build decision is a bet that the gap between your model and the next general one stays open; check it when the general one moves.

→ Primary sources: references/sources.md

Common Rationalizations

[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.

Red Flags

• Decision made with no three-year TCO · data-training clauses unread · the reason to build is "technically interesting" · no exit plan · no owned evaluation set to compare options

Verification

• Differentiation test backed by customer-side evidence
• TCO includes maintenance, evaluation, model replacement and exit cost
• Data training/retention/export terms checked clause by clause
• Matrix scores and weights archived; re-evaluation triggers written (incl. major model releases)

Part of deciqAI Knowledge Skills — 253 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/s/build-buy-partner-ai · Built by deciqAI · github.com/deciqAI · Contributions welcome.

Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/build-buy-partner-ai.json