CEO
Which AI workflow creates visible operating leverage and can become reusable company capability?
Enterprise AI RFP checklist
Enterprise AI RFP checklist.
Evaluate AI consulting partners on implementation, not promises. The RFP should test whether one workflow can become governed software: data access, RAG quality, agent boundaries, security, evaluation, monitoring, and production ownership.
- Workflow outcome
- Architecture depth
- Security and GDPR
- Production handover
RFP priority
Start by making the first build specific enough to evaluate.
- Ask for one workflow, one business owner, one user group, and one measurable proof target.
- Require a data access plan: systems, documents, permissions, quality gaps, and hosting constraints.
- Ask how RAG, agents, dashboards, and Company Brain components will be evaluated before rollout.
- Require a production path: monitoring, audit trails, support model, handover, and ownership after launch.
- Separate strategy claims from delivery ability by asking what can be shipped in the first MVP or PoC.
Stakeholder questions
A serious RFP answers business, finance, technical, and procurement concerns.
CFO
Which result justifies spend, and how will value be measured before scaling?
CTO
How will the system enforce permissions, evaluations, monitoring, security, and integration ownership?
Procurement
What entity contracts, what is delivered, what is owned, and what evidence shows delivery capability?
RFP question bank
Questions to include before selecting an AI implementation partner.
1. Workflow scope
Which exact workflow will be improved first, and which current steps are manual, slow, duplicated, or hard to scale?
2. Business metric
What measurable result proves the MVP worked: cycle time, quality, reporting speed, adoption, margin insight, or risk reduction?
3. Data access
Which systems, documents, dashboards, databases, tickets, or tools must be connected, and who can approve access?
4. Permission model
How will the AI system respect role-based access, confidential information, source boundaries, and audit needs?
5. RAG evaluation
How will retrieval quality, source grounding, citations, coverage, failure cases, and reviewer confidence be measured?
6. Agent boundaries
Which actions can agents take, which require approval, and how are escalation paths, logs, and tool limits enforced?
7. Security and GDPR
Where is data processed, what vendors are involved, how is sensitive data handled, and what review points exist?
8. Integration path
How will the AI system connect to existing ERP, CRM, BI, document stores, identity, workflows, or internal software?
9. Usage profile
How many users, how much concurrent usage, what latency expectations, and what availability assumptions matter?
10. Human review
Where should humans approve outputs, inspect evidence, handle exceptions, and override AI-assisted decisions?
11. Handover
Who owns prompts, evaluations, architecture decisions, deployment scripts, documentation, and post-launch monitoring?
12. Scale gate
What evidence is required before moving from assessment to MVP, MVP to production, or one workflow to many?
Red flags
Avoid AI proposals that cannot become production systems.
Generic chatbot demo
The proposal shows a chat interface but no workflow, data boundary, evaluation method, or ownership model.
No evaluation plan
There is no way to measure retrieval quality, agent behavior, user adoption, failure modes, or production readiness.
Weak security posture
Data residency, permissions, vendor exposure, audit trails, and human review are left until after the prototype.
Strategy without build path
The engagement produces slides but no MVP scope, architecture choices, implementation sequence, or scale decision.
Ferre Torres B.V. fit
Use the RFP to test whether the engagement can start narrow and scale responsibly.
- Capability statement: procurement-friendly summary of services, delivery model, and governance posture.
- Delivery model: founder-led senior delivery network with the ability to scale implementation around the scope.
- Security and GDPR posture: early questions for data boundaries, permissions, hosting, review, and auditability.
- Guided demo previews: synthetic system surfaces for Company Brain, RAG, finance intelligence, AI PM, and agents.
Enterprise AI RFP questions
Questions procurement and leadership should ask before selecting an AI partner.
What should an enterprise AI RFP evaluate first?
An enterprise AI RFP should first evaluate the business workflow, expected outcome, data access, security constraints, users, success metric, production ownership, and whether the MVP can become reusable AI infrastructure.
How should companies compare AI consulting partners?
Compare partners by implementation ability, architecture depth, RAG and agent evaluation discipline, GDPR and security posture, workflow understanding, handover model, and ability to ship working software.
Should an AI RFP ask for a demo?
A demo can help, but the RFP should not stop at a generic chatbot demo. It should ask how the demo pattern maps to real company data, permissions, users, evaluation criteria, and production constraints.
What is a red flag in enterprise AI vendor selection?
Red flags include no clear workflow owner, no evaluation plan, no permission model, no production monitoring, vague ROI claims, overreliance on one vendor, and strategy without a build path.
RFP next step
Use one workflow to make enterprise AI evaluation concrete.
Share the intended workflow, users, data sources, security requirements, target metric, and expected procurement constraints.