Most vendor evaluations are won by the slickest demo and the loudest internal advocate, not by the objectively best fit. Claude can structure the evaluation to surface real differences — but you have to brief it carefully to avoid amplifying bias.
Vendor evaluations are anchored by the first vendor demoed, swayed by the loudest internal voice, and often skip the boring criteria that matter most (support, contract terms, true total cost).
A structured AI-assisted evaluation forces consistent comparison across criteria you define up front, not criteria you discover after the demo cycle starts.
I am evaluating vendors for [USE CASE]. Vendors we are considering: [LIST] Our specific requirements: [LIST] Our budget range: [RANGE] Our team size and technical capacity: [SHORT] Generate an evaluation matrix: 1. Define 6-8 evaluation criteria specific to our use case (NOT generic "ease of use") 2. Weight each criterion 1-10 based on importance to us 3. For each vendor, score on each criterion with reasoning (use publicly available info) 4. Calculate weighted totals 5. Identify the criterion where the choice actually depends (where vendors differ most) 6. Flag any criterion where we do not have enough info to score 7. Recommend top choice with 2 specific reasons Format: clean table. Important: be honest about limitations. If you cannot reliably score a vendor on a criterion, say so rather than guess.
AI evaluations can hallucinate scores or favor vendors with bigger public footprints (more training data = more "evidence" to cite). Watch for this.
Mitigations:
1. Always verify the top 3 facts driving the recommendation directly with the vendor or via current docs.
2. Talk to 2-3 current customers of each finalist, regardless of what Claude scored.
3. Pay attention when Claude says "I cannot reliably score X" — this is honest and important.
4. Do not let the matrix be the final decision. It is decision support, not the decision.