AI Vendor Assessment ToolUse this AI procurement checklist to request comparable proposals for a defined AI service. State the intended use and boundaries first; ask vendors to identify evidence, assumptions, exceptions, and product version behind each answer. Adapt these procurement prompts with the buying team and counsel.
Define the request and response format
Begin with a context statement all bidders can answer against. Define purpose, intended users, affected people, decisions the system may inform or take, and how a human participates. Specify product and edition, deployment option, integrations, model providers, regions, and support arrangements in scope. Describe data inputs, outputs, telemetry, support records, sensitivity, expected volume, retention needs, and prohibited data. State operating needs such as availability, response time, accessibility, human review, fallback, and support.
For every requirement, request an answer, evidence reference and date, assumptions, exceptions, responsible owner, and customer action. Separate mandatory requirements from scored preferences. Ask each bidder to identify the exact service configuration and model chain its proposal covers. This keeps an AI software procurement guide tied to a common scope rather than comparing unrelated product claims.
Security and data questions
Ask questions tied to the access and data in the purchase. Request an artifact and scope, not a bare yes:
- How are identities, roles, administrative access, MFA, and tenant separation managed for the proposed service?
- What data is encrypted in transit and at rest? Which personnel or subprocessors can access it, and how is privileged access approved and logged?
- How are vulnerabilities, incidents, backup, recovery, and customer notification handled?
- Can the system write to another system or take actions? What least-privilege scopes, approvals, audit records, and rollback processes apply?
- For prompts, files, outputs, feedback, logs, telemetry, and support records, state purpose, location, retention, deletion process, and receiving parties.
- Can any data type be used to train or improve a model? State defaults, opt-in or opt-out settings, contract terms, exceptions, and whether model-provider terms differ.
- List subprocessors and downstream model providers that process scoped data. Explain change notice, objection, and transition processes.
- Describe export, return, deletion, backup expiry, legal-hold exceptions, and assistance at termination.
Ask vendors to distinguish a changeable console setting from a contract commitment. For personal data, have privacy counsel determine the parties’ roles and applicable terms from the facts. The FTC’s vendor security guidance is a primary reference for supplier security practices.
Generative AI procurement requirements
Generative AI procurement requirements should follow the actual proposed workflow. Ask which model or providers are involved, what version changes customers may encounter, how changes are tested and communicated, and what limitations or failure modes are known. Ask what a user can inspect, correct, appeal, or override; what happens when output is uncertain or unavailable; and whether the service can trigger actions. Request a description of how the vendor addresses risks relevant to the use, while treating the description as evidence to evaluate rather than proof that controls work.
Ask which jurisdictions, sectors, intended purposes, and configurations a compliance statement covers. Request the vendor’s role assumptions and records, technical documentation, user instructions, human-oversight information, and change notices where available. The EU AI Act contains role- and context-specific provisions. This RFP prompt does not classify a system or determine legal applicability; verify current text and transaction facts with qualified counsel.
Evaluation and evidence status
Publish criteria and weights before opening responses. Score commercial and functional fit under the organization’s normal procurement method. Keep security, privacy, legal, and safety requirements as review items with named owners. A procurement score is not a probability, compliance score, or measure of AI risk.
For evidence-dependent requirements, use visible statuses such as “demonstrated for this scope,” “partial / follow-up needed,” and “unknown.” Keep unanswered items unknown until evidence is reviewed. Do not turn missing evidence into a favorable result or hide it in an average.
Worked hypothetical example: A company requests proposals for an assistant that drafts internal help-desk replies from approved articles. Bidder A says prompts are not used to improve models, but cites a general privacy page that does not name the enterprise service. The evidence remains unknown for that service. Bidder B provides a service-specific contract schedule and dated subprocessor list, but incident-notice terms remain under review. Procurement compares functionality and cost while privacy and security owners track their open items. Neither bidder is selected through an invented risk probability or aggregate “compliance” score. This is a hypothetical process, not a real bid evaluation.
Policy and contract handoff
An AI procurement policy can require the buying process to capture the intended use, data categories, service configuration, accountable reviewers, and unresolved evidence before production use. It can also require material model, data-use, subprocessor, or security changes to return for review before an approved use expands. This is a policy prompt, not a vendor warranty or legal clause. Define who owns each step, what records are kept, and how exceptions are escalated.
Keep requirements traceable from the request through vendor answers, contract terms, configuration, and post-award review. Preserve the vendor’s evidence and note its date and scope. If the selected service changes, check whether the original requirements and evidence still apply. Ask counsel to adapt contract language and resolve role-specific obligations.
For a detailed question set, see the AI Vendor Assessment Questionnaire. For contract discussion prompts, use AI Contract Clauses and DPA Checklist and the AI vendor evaluation guide.
Frequently asked questions
How is an AI procurement checklist different from a general software request?
It asks vendors to explain model and data dependencies, intended use, change practices, output limitations, and human oversight alongside ordinary security, commercial, and service requirements.
Should every AI requirement be scored?
No. Some requirements may be mandatory review conditions. Keep those distinct from weighted preferences so an attractive average cannot hide a material unresolved issue.
What should a vendor include with an answer?
Ask for the evidence reference, version or date, scope, exceptions, assumptions, responsible owner, and customer actions. A general statement may not cover the proposed service configuration.
Who decides whether the proposed service can be used?
Authorized people in the organization make that decision after reviewing evidence and applicable requirements. The RFP template organizes information; it does not determine legal compliance or approve a vendor.
Updated 2026-10-07. Sources are linked on this page.