AI Vendor RFP Examples
Templates and frameworks for AI vendor selection — RFP structures, evaluation criteria, proof-of-concept requirements, and contract considerations for AI procurement.
AI Platform RFP Template
intermediateA comprehensive RFP template for selecting an enterprise AI platform, covering functional requirements, technical architecture, security, data handling, pricing, support, and vendor viability assessment criteria.
Key takeaway: AI vendor RFPs should include a mandatory proof-of-concept phase with your actual data — vendor demos with sample data tell you very little about real-world performance.
AI Vendor Scoring Framework
beginnerA weighted scoring framework for comparing AI vendors across technical capability, data security, pricing model, scalability, support quality, and strategic fit, with minimum thresholds for mandatory criteria.
Key takeaway: Setting minimum thresholds for non-negotiable criteria (security, compliance, data handling) before scoring prevents choosing a vendor that scores well overall but fails on critical requirements.
AI Vendor Security and Compliance Questionnaire
intermediateA detailed questionnaire covering data processing practices, model training policies, encryption, access controls, compliance certifications, incident response, and data residency for AI vendor assessment.
Key takeaway: The most important vendor security question is: 'Will our data be used to train your models?' — many vendors default to yes unless explicitly opted out.
AI Proof-of-Concept Requirements Document
intermediateA structured requirements document for running a proof-of-concept with shortlisted AI vendors, including test scenarios, evaluation criteria, data requirements, success metrics, and timeline.
Key takeaway: PoC requirements should test the vendor on your hardest use cases, not your easiest — the easy cases will work with any vendor.
AI Contract Negotiation Checklist
advancedA checklist of key terms to negotiate in AI vendor contracts including data ownership, model update notification, SLA definitions, exit provisions, pricing escalation caps, and liability for AI outputs.
Key takeaway: Exit provisions are the most under-negotiated terms in AI contracts — define data portability, model export rights, and transition assistance before you need them.
Patterns
Key patterns to follow
- Include mandatory proof-of-concept with your own data as part of the vendor evaluation process
- Set minimum thresholds on security and compliance criteria before comparative scoring
- Prioritise vendors who are transparent about data handling and model training practices
- Negotiate exit provisions and data portability before signing — they are nearly impossible to add later
FAQ
Frequently asked questions
Structure your RFP around: business problem and objectives, functional requirements, technical requirements (integration, security, scalability), data handling requirements, pricing model, support expectations, and evaluation criteria with weights. Include a mandatory PoC phase.
Shortlist 3-5 vendors for detailed evaluation. More than 5 creates evaluation fatigue. Fewer than 3 limits your negotiating leverage and comparison data. Use initial screening criteria to narrow from a longer list.
Red flags include: unwillingness to run a PoC with your data, vague answers about data handling and model training, no customers in your industry, pricing that is unclear or lacks caps, and resistance to discussing exit terms or data portability.
Compare total cost of ownership over 3 years, not just per-unit pricing. Include: licence/subscription fees, API costs at projected volume, implementation costs, training, support, and the cost of internal resources needed. Ask for volume discounts and pricing caps.
Startups often lead in capability but carry viability risk. Established vendors offer stability but may lag in features. Evaluate vendor financial health, customer base, and what happens to your data if the vendor is acquired or shuts down. For critical systems, vendor stability matters more.
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