AI Vendor Selection Guide - Gen AI Podcast

Purpose: Objectively evaluate AI vendors and solutions to avoid costly mistakes, vendor lock-in, and failed implementations.

Use This For: Build vs. buy decisions, RFP evaluation, vendor shortlisting

Time Required: 4-6 hours per vendor (due diligence phase)

⚠️ The AI Vendor Landscape is Full of Hype

Every vendor claims "cutting-edge AI," "enterprise-grade," and "proven results." This guide helps you see through marketing claims and assess real capability, fit, and risk.

Vendor Evaluation Process

Phase 1: Initial screening (eliminate obviously poor fits)
Phase 2: Capability assessment (detailed technical evaluation)
Phase 3: Reference checks and proof of concept
Phase 4: Commercial evaluation and contract negotiation
Phase 5: Final decision and onboarding planning

Phase 1: Initial Screening Criteria

Use these criteria to create a short list of 3-5 vendors for detailed evaluation.

Screening Criteria	Weight	Vendor A	Vendor B	Vendor C
Use Case Fit Do they have proven experience in our specific use case?	30%
Industry Experience Do they understand our industry and regulatory context?	20%
Company Stability Financially stable? 2+ years in business? VC-backed startups: runway?	15%
Technology Maturity Production-ready or beta? Proven at scale?	15%
Geographic Coverage Can they support our locations and compliance requirements?	10%
Budget Alignment Pricing in our range? (order of magnitude)	10%
TOTAL SCORE	100%	/100	/100	/100

Scoring: 1 = Poor | 3 = Adequate | 5 = Good | 7 = Excellent | 10 = Outstanding

Shortlist threshold: Vendors scoring 70+ proceed to detailed evaluation

Phase 2: Capability Assessment

Technical Capability Evaluation

Capability Area	Score (1-10)	Evidence / Notes
Model Performance & Accuracy Published benchmarks? Can they demonstrate on our data?
Scalability & Performance Can handle our data volumes? Transaction volumes? Latency requirements?
Integration Capabilities APIs available? Pre-built connectors? Custom integration support?
Data Requirements How much training data needed? What quality? Can they work with our data?
Customization & Flexibility Can solution be tailored? Or rigid one-size-fits-all?
Explainability & Transparency Can they explain how decisions are made? Black box or interpretable?
Model Monitoring & Maintenance Tools for detecting drift? Retraining process? Who owns it?

Security & Compliance

Security Requirement	Score (1-10)	Evidence / Notes
Data Privacy & Protection GDPR/CCPA compliant? Data residency controls? Encryption standards?
Security Certifications SOC 2? ISO 27001? Industry-specific certs?
Access Controls & Authentication SSO? MFA? Role-based access? Audit logging?
Vulnerability Management Penetration testing frequency? Bug bounty program? Incident response?
Data Ownership & Portability Who owns the data? Our models? Can we export everything?

Implementation & Support

Support Criteria	Score (1-10)	Evidence / Notes
Implementation Methodology Proven process? Project timeline realistic? Phased approach?
Team Expertise Dedicated team? AI/ML expertise? Industry knowledge?
Training & Enablement End-user training? Admin training? Documentation quality?
Ongoing Support SLA commitments? Support hours? Escalation process? Response times?
Product Roadmap Active development? How often updated? Influence on roadmap?

Phase 3: Reference Check Questions

💡 Pro Tip: Always Do Reference Checks

Talk to at least 3 customers—ideally in similar industries with similar use cases. Ask vendor for references, but also find your own via LinkedIn.

Questions to Ask Vendor-Provided References

Use Case & Results: What problem were you solving? Did you achieve your goals? Metrics?
Implementation Experience: How long did it take? On time/budget? Major challenges?
Technology Performance: Does it work as promised? Model accuracy in production? Issues?
Vendor Relationship: Responsive? Proactive? How do they handle issues?
Support Quality: Support SLAs met? Bug resolution time? Knowledge level of support team?
Unexpected Costs: Any surprise fees? Hidden costs? Scope creep?
User Adoption: Did users embrace it? Training adequate? Change management support?
Would You Choose Them Again?: Knowing what you know now, would you still choose this vendor?

Questions to Ask Independent References (LinkedIn finds)

Why did you choose this vendor? Evaluate alternatives?
Any major problems or regrets?
What should we watch out for?
How does pricing compare to competitors?
Any challenges with scaling or performance?

Reference Check Scorecard

Reference Name	Company / Industry	Key Feedback (Summary)	Recommend? (Y/N/Qualified)

Red Flags to Watch For

🚩 Vague or Unverifiable Claims

"Industry-leading accuracy" with no benchmarks. "Thousands of customers" but can't provide references.

🚩 Pressure Tactics

"Special pricing expires Friday." "Competitor just signed." These are sales tactics, not real urgency.

🚩 No POC / Trial Offered

If they won't let you test on your data, they're not confident in their solution.

🚩 Lack of Technical Depth

Sales team can't explain how it works. No access to technical experts during evaluation.

🚩 Opaque Pricing

Won't provide pricing until late stages. Lots of hidden fees. Complex pricing structure.

🚩 Vendor Lock-In Tactics

Proprietary data formats. No export capability. Restrictive contract terms.

🚩 Unrealistic Promises

"Implement in 2 weeks." "No data preparation needed." "100% accuracy." Too good to be true = probably is.

🚩 Poor Financial Health

Startup with <6 months runway. Recent layoffs. Negative press about finances.

Phase 4: Contract Negotiation Checklist

⚠️ Do Not Skip Legal Review

AI vendor contracts have unique risks: data ownership, IP rights, liability for AI errors. Always involve legal counsel.

✓	Contract Term	Notes / Negotiated Terms
☐	Pricing Structure Fixed vs. usage-based? Annual increases capped? Volume discounts?
☐	Contract Duration & Termination Initial term? Auto-renewal? Termination fees? Notice period?
☐	Service Level Agreements (SLAs) Uptime guarantees? Performance metrics? Credits for downtime?
☐	Data Ownership & Rights Who owns training data? Model outputs? Can they use our data?
☐	Data Privacy & Security GDPR/CCPA compliance guarantees? Data breach notification terms?
☐	Intellectual Property Custom development: who owns it? Can we use independently?
☐	Liability & Indemnification Who's liable for AI errors? Caps on liability? Insurance requirements?
☐	Data Portability & Exit Export formats? Transition assistance? Data deletion guarantees?
☐	Change Management Notice for major changes? Backward compatibility? Migration support?
☐	Support Terms Support hours? Response time SLAs? Escalation process defined?
☐	Audit Rights Can we audit their security? Compliance? Performance metrics?
☐	Subcontractors Can they subcontract? To whom? Same obligations apply?

Technical Due Diligence: POC Evaluation

💡 Always Insist on a Proof of Concept

Test on YOUR data, YOUR use case, YOUR infrastructure. Don't accept generic demos.

POC Success Criteria

POC Objective	Success Threshold	Actual Result	Pass/Fail
Model accuracy on our data
Response time / latency
Integration with our systems
User experience / usability
Ease of implementation

POC Timeline: Start: _________ | End: _________ | Duration: _________

POC Participants: _________________________________________________________________

Final Vendor Comparison

Evaluation Category	Weight	Vendor A	Vendor B	Vendor C	Vendor D
Technical Capability	30%
Security & Compliance	20%
Implementation & Support	15%
Reference Feedback	15%
Commercial Terms	10%
Company Viability	10%
TOTAL WEIGHTED SCORE	100%	/100	/100	/100	/100

Recommended Vendor: _________________________________

Justification:

_________________________________________________________________