AI Vendor Selection Checklist
The right AI vendor decision holds up to security, legal, and finance scrutiny. Use this checklist to evaluate vendors on what matters, not just the demo.
Fit
- Confirmed fit with your specific use case
- Checked integration with your existing stack
- Reviewed the product roadmap for alignment
Security & data
- Clarified data handling, ownership, and retention
- Verified security certifications and practices
- Confirmed where data is stored and whether it trains models
Risk
- Assessed model transparency and explainability
- Evaluated vendor stability and track record
- Confirmed exit, portability, and lock-in terms
Commercial
- Understood the pricing model and total cost
- Reviewed SLAs and support commitments
Proof
- Checked reference customers and case studies
- Run a pilot or trial against real data
- Scored vendors against defined evaluation criteria
Why this matters
AI vendor choices carry data, security, and lock-in consequences that outlast the buying decision. A structured evaluation protects you from impressive demos that don't hold up in production.
Want help working through this?
I help teams define evaluation criteria and run AI vendor selection so the choice is defensible to security, legal, and finance.
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