AI Procurement RFP Guide
AI procurement RFPs should specify production outcomes, VPC deployment, eval evidence, ops model, and outcome-based pricing — not licence seats or hourly rates alone.
AI procurement RFPs should specify production outcomes, VPC deployment, eval evidence, ops model, and outcome-based pricing — not licence seats or hourly rates alone.
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Overview
What to include in RFPs when buying enterprise AI outcomes vs software licences.
What to include in RFPs when buying enterprise AI outcomes vs software licences.
AI Procurement RFP Guide is written for AI programme owners, technology leaders, and operations executives in regulated enterprises. Most organisations fail not because models are inadequate — but because context, governance, evaluation, and operational ownership are missing when pilots attempt to reach production.
Derisk360 practitioners embed Forward Deployed Engineers inside your business and run structured accelerators — from discovery through governed go-live in your VPC. This guide reflects that delivery model: practical steps you can execute with embedded teams, not abstract best practices that stall at proof-of-concept.
Practical steps for regulated enterprise environments
Designed for production go-live — not endless pilots
Aligns with Derisk360 accelerator delivery model
Typical governed production in under 12 weeks
Before you start
Align business, risk, and technology stakeholders on the highest-value use case — not the most fashionable one. Confirm data access, regulatory constraints, and who owns production operations after go-live.
If you lack unified context infrastructure, plan context engineering as the first accelerator phase. Agents built on demo datasets will fail model risk review.
How Derisk360 applies this guide
We implement every guide through outcome-based services — embedded FDEs, FDEE-led evaluation, and 24/7 managed operations. Book a discovery call to map your use case and scope an accelerator tailored to your industry.
Step-by-step implementation.
- 1
Define production success criteria
Workflow KPIs and go-live gates — not demo requirements.
- 2
Require embedded delivery
FDE accountability through go-live in your environment.
- 3
Specify eval and red team
Evidence pack for model risk before production.
- 4
Mandate VPC and audit
Data sovereignty and immutable action logs.
- 5
Outcome-based commercial model
Milestones tied to production — compare to TCO of shelfware+SI.
- 6
Reference regulated experience
Banking/insurance case studies and accelerator patterns.
Four phases to production go-live.
Embed & discover
FDEs embed inside your business, learn the domain, and scope the highest-value use case for this accelerator.
Unify context
Connect source systems into a governed context layer — MCP, knowledge graphs, and field mapping in your environment.
Configure & evaluate
Build governed agent workflows, run eval harnesses, and tune against your policies before go-live.
Deploy & monitor
Go live securely in your cloud with FDEE-led monitoring, continuous evaluation, and proactive tuning.
Related resources
Ready for an AI implementation partner?
Book a discovery call and we'll map your highest-value use case — and exactly how we get it into production.
Frequently asked questions
- What is ai procurement rfp guide?
- What to include in RFPs when buying enterprise AI outcomes vs software licences.
- How long does production go-live take?
- Typical accelerator engagements reach governed production go-live in under 12 weeks for priority use cases in banking and insurance.
- Who should read this guide?
- AI programme owners, technology leaders, and operations executives responsible for moving enterprise AI from pilot to production.
- How do I engage Derisk360?
- Book a discovery call at derisk360.com/book to map your use case.
- Can Derisk360 implement this guide for us?
- Yes. Every guide maps to accelerator delivery with embedded FDEs who implement in your environment.