Vendor Selection · Evaluation · Procurement
AI Vendor Selection Framework: Independent Methodology for Choosing AI Platforms Without Vendor Bias
The AI vendor market is engineered to make independent evaluation nearly impossible. Vendor-designed RFPs favor incumbent platforms. Analyst reports accept vendor briefings as primary evidence. PoC engagements are structured to pass. This 48-page framework gives enterprises the methodology to run genuinely independent AI vendor selections, used across 80+ evaluation engagements covering LLMs, MLOps platforms, data infrastructure, AI governance tooling, and vertical AI applications.
What You'll Learn
The 12-dimension vendor evaluation scorecard used across 80+ enterprise AI selections, covering technical capability, security architecture, integration ecosystem, vendor viability, pricing structure, support model, reference architecture quality, compliance posture, roadmap credibility, data handling, lock-in risk, and negotiation leverage.
How to write RFPs that actually differentiate vendors rather than producing nearly identical responses. Includes the RFP template structure, the mandatory technical demonstration requirements, and the question formats that reveal genuine capability rather than enabling vendor-crafted narrative responses.
PoC design that predicts production performance, including how to structure proof-of-concept engagements that use your actual data, test against your actual requirements, and prevent the well-documented failure mode where PoC success does not predict production deployment success.
Contract negotiation leverage and terms to demand, including the 14 contract provisions that enterprise AI buyers routinely fail to secure in initial negotiations, the performance SLA structures that create vendor accountability, and the exit rights that prevent lock-in after go-live.
Vendor category maps across 12 AI technology categories, covering the key players, their market positioning, the selection criteria most relevant to each category, and the conflict-of-interest risks to watch for when taking vendor-sponsored analyst reports or reference checks at face value.
Case study: reversing a $18M platform decision, detailing how a Fortune 500 retailer avoided a poor AI platform selection through independent evaluation after the initial internal process was compromised by vendor influence, and the $7.2M cost avoided over the contract period.
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AI Vendor Selection Framework
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