AI Vendor Evaluation Kit

Evaluating AI vendors is fundamentally different from traditional software procurement. Model performance degrades over time as data distributions shift. Pricing models are usage-based and can surprise you at scale. Lock-in risks are amplified by proprietary fine-tuning, data formats, and prompt engineering investments. And the vendor landscape changes quarterly as new entrants emerge and existing players pivot.

This kit provides a structured evaluation process that accounts for these AI-specific risks while maintaining procurement velocity. It is designed to be handed to your procurement team, your technical evaluation committee, and your legal team so everyone is working from the same framework.

The evaluation follows a progressive funnel that narrows the field at each stage while increasing evaluation depth. This prevents the common failure mode of spending weeks on deep technical evaluations of vendors that fail basic screening criteria.

Use this rubric to score each vendor consistently across evaluation dimensions. Each dimension is scored 1-5 where 1 indicates significant gaps and 5 indicates best-in-class capability. Weight the dimensions based on your organization's priorities.

Your RFP should be structured to elicit specific, comparable responses from each vendor. Avoid open-ended questions that produce marketing copy. The following sections ensure you collect the information needed for rigorous evaluation.

Reference checks are the most underutilized part of vendor evaluation. Vendors provide their happiest customers as references, so you need to ask questions that reveal useful signal even from favorable references.