How to Run an Enterprise AI Vendor Evaluation in 30 Days
enterpriseai
A step-by-step playbook for scoping, piloting, and scoring an enterprise AI platform in under a month — with the five deciders that actually matter.
Most enterprise AI evaluations stall in demos and RFP theater. This is the shorter path: one team, one project, thirty days, and a scoring rubric that separates table stakes from the five deciders that actually predict outcomes.
Week 0 — Scope the pilot before you invite vendors
Pick one team with a real problem they touch weekly. Not a moonshot. Not a lab. A workflow where time-to-answer and citation quality can be measured against how they work today.
- Name the workflow in one sentence (e.g. "triage inbound RFPs against our win/loss library").
- List the sources: pages, PDFs, screenshots, voice notes, filed documents.
- Write down today's baseline: minutes per task, error rate, rework loops.
- Decide the exit criteria before the demo, not after.
Week 1 — Live demo on your sources, not their sandbox
Any vendor can win a scripted demo. Ask each finalist to ingest a bounded slice of your real material on day zero and answer three questions your team already knows the answer to. You are testing citation quality and retrieval, not fluency.
Week 2–3 — Two-week pilot with the team, not IT
Hand the tool to the practitioners. Track:
- Time-to-answer versus baseline.
- Citation precision — does it point to the right passage, not a paraphrase.
- Coverage — questions it refuses or fumbles.
- Capture surface — can users add screens, voice notes, and field evidence, or only what's already filed.
Week 4 — Score against the five deciders
Three criteria are table stakes: model capability, security and compliance, familiar surfaces. Every serious vendor clears them. The scoring weight belongs on the deciders:
The five deciders scorecard
| Decider | What to test | Pass looks like |
|---|---|---|
| Sovereignty | Where does inference run | Self-hosted or dedicated in your walls |
| Memory ownership | Who owns the knowledge graph | Your data, your weights, your memory |
| Responsible AI | Who governs model behavior | You set citation and refusal policy |
| Exit economics | Day-one leave test | Data and memory stay with you |
| ROI shape | Acceleration vs discovery | Surfaces moves no individual could connect |
For the memory question specifically, see enterprise memory as the compounding layer — it is the difference between opex that evaporates and a moat that compounds.
Week 4 — Decide on evidence, not brochures
Compare the pilot numbers to your baseline. If time-to-answer is halved and citations hold up, scale team by team. If not, keep the money.
For teams running the discovery side of the evaluation in parallel, the companion playbook is [how to