── ── AI & agents
Build / Buy / Partner (AI Capabilities)
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.
Run Build / Buy / Partner (AI Capabilities) on a real problem
Bring something you're actually deciding — free, in the browser.
How it works
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
When to use it
- user says 'should we build this AI feature or buy it?', 'should we develop our own agent?', 'which AI vendor should we partner with?', 'build vs buy', 'should we train our own model?', 'the vendor raised prices / changed terms — do we switch?', or is about to sign an annual AI contract, hire an ML team, or commit to a platform for an AI capability
When not to use it
When the decision is routine and reversible, applying a formal method costs more than it returns.
Worked example
BloombergGPT and the General-Purpose Models (2023)
BloombergGPT is the clearest public example of an organization that passed the hardest gate in this skill — proprietary data — chose to build a foundation model, documented the build in detail, and then saw a general-purpose model released two weeks earlier measured against it by an outside team. It is useful precisely because Bloomberg had a strong case for building; the lesson is about what the strong case still has to survive.
Install this skill (free, MIT)
npx skills add deciqAI/knowledge-skillsUseful? Star the repo — stars help other builders find it.
Related mental models
Buyers increasingly ask an AI assistant "what should I use for X?" instead of scanning ten blue links.
When an agent takes over part of a job, the job does not disappear — a bundle of tasks changes hands.
When AI replaces human work, the customer needs fewer seats — so seat pricing moves in the opposite direction from the value delivered.
In the AI era, models and compute can be bought.