AI RFP Checklist

Most AI RFPs are software RFPs with the word AI added: feature matrices, generic security questionnaires, and price tables. They reward the vendors best at filling in forms. An RFP built to reveal method rewards the vendors who will deliver. This checklist covers what to include, what to ask, and how to score. It is the operational form of how to write an ai rfp and complements the AI procurement for CIOs whitepaper and the ai vendor evaluation checklist.

Who should use this checklist?

Procurement, technology, and business leaders issuing RFPs for AI development, AI products, or AI-capable engineering partners, with their legal and security teams.

Is the outcome stated with acceptance criteria?

1. The business outcome is stated in measurable terms with a baseline or a plan to establish one.
2. Acceptance criteria define correctness, quality thresholds, and the evaluation method.
3. The autonomy level expected at launch and later is stated.
4. Out of scope is written.

Reference: how to write acceptance criteria for ai.

Is the context complete?

1. Data sources, formats, volumes, quality known, and sensitivity classification.
2. Systems to integrate with interfaces and constraints.
3. Users and workflows affected.
4. Security and compliance requirements: data handling, provider restrictions, residency, regulation.
5. Decision rights: what humans must decide or approve.
6. Timeline drivers and constraints, without demanding guarantees.

Reference: how to scope an ai project.

Do the questions reveal method?

Question	Artifact requested
How do you define correctness for this system?	Specification from comparable work
How do you measure it?	Evaluation report with metrics by category
How do you build and label evaluation datasets?	Dataset methodology description
How do you launch systems that act?	Shadow and autonomy graduation practice
How do you handle provider model changes?	Change control and re-evaluation process
How do you test for injection and leakage?	Adversarial test approach and results
How do you monitor quality, cost, and drift in production?	Observability description and sample dashboards

Reference: the spec-driven development for AI whitepaper and the AI evaluation and testing whitepaper.

Are security and data questions specific?

1. Data flows including model providers and subprocessors, with terms.
2. Access model for engineers and systems.
3. Asset ownership: will code and infrastructure live in our accounts?
4. Secrets, logging, and redaction practices.
5. Provider terms on training, retention, and residency.
6. Audit reports or equivalent evidence; incident history.

Reference: ai vendor security questionnaire and the LLM security checklist.

Are operations and handoff covered?

1. Monitoring and support model.
2. Incident process for AI-specific incidents.
3. Change management for prompts, models, and retrieval.
4. Handoff: documentation, runbooks, training, transition period.
5. Knowledge transfer approach during delivery.

Reference: the ai project handoff checklist.

Are people questions included?

1. Who specifically will do the work, with production history.
2. Access to those people during evaluation.
3. Continuity practice when engineers change.
4. For cross-border vendors, contracting entity, governing law, and overlap commitments.

Reference: the cross-border engineering delivery model whitepaper.

Are commercial and legal terms stated?

1. IP assignment of custom work; data-use restrictions; exit and export rights.
2. Pricing requested by drivers with volume assumptions; no outcome guarantees accepted.
3. Change notification and re-validation rights.
4. Governing law preference.
5. Pilot structure if planned, with acceptance criteria and IP terms.

Reference: the outsourcing contract checklist.

Is the scoring model defined?

1. Gates: method, security and data handling, IP and terms, each with minimum evidence.
2. Weighted criteria: capability evidence, operations, stability, references, three-year total cost of ownership.
3. Evidence quality scoring guidance for reviewers.
4. Reference call protocol focused on outcomes and problem handling.
5. The scoring model is shared with vendors so responses target it.

Reference: the AI total cost of ownership whitepaper.

Is the process designed to verify?

1. Technical sessions with named engineers after written responses.
2. Reference calls before shortlisting.
3. Paid, scoped pilot for significant purchases with defined acceptance criteria.
4. Decision documentation for auditability.

Reference: how to structure an ai pilot agreement.

Are the common RFP mistakes avoided?

1. Feature matrices as the primary instrument.
2. Requiring guarantees of outcomes or timelines.
3. Generic security questionnaires with no AI-specific questions.
4. No request for artifacts.
5. Scoring price before gates.
6. No pilot for significant purchases.

How should responses be scored?

Score each requirement independently with defined criteria before reading pricing, weight evaluation evidence and production references above presentations, and record the reasoning so the decision can be defended later.

How FISTA Solutions responds to RFPs

FISTA Solutions responds to RFPs built this way with the artifacts they ask for: specifications and evaluation reports from comparable work, named engineers, data-flow and security documentation, buyer-owned asset practice, IP assignment and data terms, and scoped pilot proposals with acceptance criteria. Engagements run through forward deployed engineers, AI enablement, AI agents, and staff augmentation, backed by 150+ projects for 50+ companies with 99.9% uptime.

To structure an AI RFP or invite FISTA to respond to one, message us on WhatsApp, or read how to evaluate ai vendors for the evaluation side.