AI Procurement & Vendor Evaluation Playbook (Build vs Buy)

Ask whether a competitor buying the same off-the-shelf vendor product would erase your advantage in this specific capability. If yes, it's commodity and buying is usually right; if the value comes from proprietary data, workflow, or domain expertise the vendor can't replicate, building or a tightly scoped partnership is worth the extra time to value.

A vendor unwilling to test against a reasonable, appropriately anonymized sample of your real data is itself a red flag worth weighing heavily in the vendor-viability score — confident, mature AI vendors expect and support this kind of evaluation as standard practice.

Model versioning commitments. Many AI vendor contracts don't specify whether the underlying model can be silently upgraded or swapped mid-contract, which can change output behavior, cost, or accuracy without any formal notice — always ask for a defined change-notification process in writing.

There's no universal weighting — a healthcare or financial services buyer should weight data governance far higher than a marketing tool evaluation would, and a fast-moving competitive category should weight time-to-value higher than a stable, low-urgency one. Set the weights explicitly with stakeholders before scoring, not after seeing the results.

Generally yes for first-time AI vendor relationships, even at a modest price premium, since a shorter initial term with a defined renewal review limits your exposure if the vendor's real-world performance diverges from the proof-of-value, as it did in the claims-summarization example above.