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Playbook: AI vendor evaluation
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Services 7 min read

Playbook: AI vendor evaluation

A useful evaluation does not ask how many models the vendor uses. It asks what problem they solve, how they integrate, who operates it, and what happens when it fails.

In this article

Define the problem and decision before hearing the pitch

If the team enters the evaluation without a use hypothesis and a buying criterion, the vendor sets the agenda. The starting point should be internal: which workflow the company wants to improve, what evidence it needs, and how much lock-in it can tolerate.

Compare architecture, not just the interface

A convincing demo can hide fragility in integrations, security, or data governance. Ask how it connects, what depends on third parties, which logs it provides, and what part of the system remains truly under customer control.

  • Request the integration diagram and external dependencies.
  • Ask for detail on permissions, traces, and exportability.
  • Clarify which capabilities are roadmap versus already live.

Test with a real case and uncomfortable data

The value test should use a real workflow, imperfect documents, and real constraints. If the vendor only shines with clean data and curated questions, they have not yet proved implementation capability.

Evaluate the operating model, not only initial delivery

Ask who supports changes, how incidents are handled, what metrics they report, and which capabilities the internal team must retain. Many problems do not appear at launch. They appear three months later.

Close with a scorecard, not a feeling

The final decision should leave comparable scores by criterion: fit to use case, integration, security, speed, switching cost, and support. Without that scorecard, the process rewards the best presentation, not the best operator.

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