AI Vendor Evaluation: A Product Manager's Checklist (2026)

Choosing the wrong AI vendor costs 6-12 months and $200K-$500K in wasted investment. Here's a systematic evaluation framework.

**The 8-Dimension Vendor Evaluation Framework:**

1. **Technical Fit (25%)** — Integration, LLM support, latency at scale, ability to run your actual data. 2. **Data & Security (20%)** — Where data is processed, SOC 2/HIPAA, audit capability, data portability. 3. **Pricing & TCO (15%)** — Model cost at 10x volume, hidden costs, price increase caps. 4. **AI Quality & Accuracy (15%)** — Accuracy metrics, POC with your data, edge case handling. 5. **Vendor Stability (10%)** — Revenue growth, enterprise references, engineer-to-sales ratio. 6. **Support & SLAs (5%)** — P1 response time, dedicated CSM, uptime guarantee. 7. **Implementation (5%)** — Realistic timeline, required resources, onboarding quality. 8. **Exit Strategy (5%)** — Data export, termination process, vendor lock-in assessment.

**Red Flags:** "Proprietary AI" without explanation. No SOC 2. Pricing only after demo. No enterprise references. Demo uses their data, not yours.

Use our Vendor Evaluation tool at /vendor-evaluation to score vendors objectively.

About the Author

Durai Rajamanickam is a Business Transformation Leader and author of The AI Inflection Point: Volume 1 - Financial Services. With over two decades of experience, he specializes in AI-driven enterprise transformation, designing evidence-based ROI frameworks, and helping organizations modernize legacy systems with intelligent automation.

His work focuses on translating AI ambition into measurable business outcomes, with case studies spanning Ramp, Nubank, Coinbase, RBC, and Stripe—all showcasing AI ROI between 2.56× and 17×.

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