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Enterprise AI RFP Template
A well-structured AI RFP is the single best tool for separating serious implementation partners from confident presenters.
What a good AI RFP covers
Most enterprise AI RFPs are either too vague to be useful or too prescriptive to surface the right solutions. A well-structured RFP defines what you need clearly enough that vendors can scope accurately — and gives you a consistent framework for comparing responses.
This template covers the eight sections that matter for enterprise AI procurement. Adapt the language to your context, but keep the sections — each one surfaces something a vendor evaluation will miss without it.
Company & Use Case Overview
Describe the business context, the specific workflow being targeted, and how success is defined from a business outcome perspective.
- Business background and operating context
- The workflow or process being targeted for AI
- Current-state pain points and their quantified impact
- Definition of success: what changes, by how much, by when
Scope of Services
Distinguish clearly between strategy, implementation, integration, change management, and ongoing support. Ambiguity here creates expensive scope disputes later.
- Which services are in scope: strategy, design, build, integration, training, support
- What the vendor is responsible for versus your internal team
- What handoff looks like at the end of the engagement
- What is explicitly out of scope
Technical Requirements
Specify the technical environment in enough detail that vendors can scope accurately — not so much that you inadvertently constrain the solution design.
- Systems integration points: ERPs, CRMs, databases, APIs
- Security requirements: authentication, authorization, data encryption
- Data environment: cloud provider, on-prem, hybrid
- Model preferences or constraints, if any
Governance & Compliance
Enterprise AI that cannot answer governance questions before deployment is not enterprise-ready. This section filters serious vendors from confident presenters.
- Required compliance frameworks: SOC 2, HIPAA, GDPR, ISO 27001, etc.
- Audit documentation format and delivery cadence
- Data residency requirements by geography or regulation
- Human oversight requirements for model outputs
Delivery Model & Timeline
How the vendor structures engagements determines whether delivery is predictable. Vague delivery models produce unpredictable timelines.
- Typical engagement structure: phases, milestones, checkpoints
- Team composition and who the named delivery leads are
- Communication cadence and escalation paths
- Expected internal resource requirements from your team
Success Metrics & KPIs
Define what success means before deployment, not after. Vendors who resist this conversation are telling you something important.
- KPIs to be measured: throughput, error rate, cycle time, cost per unit
- Who owns measurement and how baselines will be established
- Reporting format, frequency, and audience
- What happens if KPIs are not met
Security & Data Handling
AI systems touch data. Every data access decision is a security decision. This section should be written in collaboration with your CISO.
- Data access policies: what the vendor can access and under what conditions
- PII handling: anonymization, masking, access controls
- Model logging policies: what is retained, for how long, and who can access it
- Incident response process: detection, notification, remediation timelines
Pricing & Commercial Terms
AI engagement pricing should be transparent about what is and is not included. Milestone-based billing aligns vendor incentives with delivery.
- Pricing model: fixed-scope, time-and-materials, or milestone-based
- What is included versus billable separately (travel, infrastructure, licenses)
- Payment terms and milestone payment structure
- Change order process: how scope changes are scoped, approved, and priced
Common RFP mistakes
Four patterns that produce proposals that look comparable but aren’t.
Too much focus on tools, not enough on process
Specifying which AI platform to use before scoping the workflow guarantees a solution shaped around the tool rather than the problem.
No governance section
Governance questions raised after contract signing are more expensive to answer and more likely to stall deployment. Include them in the RFP.
Undefined success metrics
An RFP without a clear success definition invites proposals that compete on price and presentation rather than on fit for your actual problem.
No question about post-launch support
Most AI failures happen in the 90 days after go-live, not during implementation. Ask specifically what the vendor provides after the system is live.
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