AI vendor evaluation playbook

Vendor evaluations often involve uneven source material: polished sales decks, dense RFP responses, demos, security questionnaires, pricing models, and stakeholder notes. AI can reduce review friction, but only if the process is source-disciplined.

The playbook should require side-by-side evidence, citations back to source material, explicit unknowns, and evaluator review before scores or recommendations are accepted.

1. Confirm requirements and scoring weights before analysis begins.
2. Summarize each vendor separately to reduce cross-contamination.
3. Map evidence to evaluation criteria.
4. Draft clarification questions for missing or ambiguous responses.
5. Prepare a human-reviewed decision brief with risks, tradeoffs, and open items.

• Do not upload confidential proposals unless the tool and data use are approved.
• Do not let AI invent scoring evidence or fill gaps without labeling assumptions.
• Keep vendor selection, negotiation, and award decisions with authorized people.