Enterprise AI Vendor RFP: 40 Questions to Ask (2026)
Most enterprise AI RFP processes fail because procurement teams ask the wrong questions — or ask the right questions in a format that lets vendors give evasive answers. These 40 questions are designed to surface what AI vendors don’t volunteer on their own.
Standard IT procurement frameworks were built for a world of on-premise software and straightforward SaaS tools. Enterprise AI breaks those frameworks in three important ways. AI systems process your sensitive business data using probabilistic models you don’t control. They often route that data through third-party LLM APIs without making this explicit in sales conversations. And they influence decisions at a scale and speed that traditional software never did.
A typical enterprise AI vendor evaluation that uses a standard IT RFP will miss up to 60% of the risk-relevant questions. The result is a contract signed before the security team has asked where data actually goes, or a deployment that legal later flags for missing GDPR documentation.
This 40-question framework is organized across 7 categories. Use it as a starting point and add questions specific to your industry and use case.
Why Standard IT RFPs Miss AI-Specific Risks
Traditional IT procurement is designed to evaluate features, uptime, support responsiveness, and price. Those factors still matter for AI — but they represent less than half the evaluation surface. The unique risks in enterprise AI procurement fall into three areas that most RFP templates do not cover.
- Third-party model routing: Many AI vendors use commercial LLM APIs (OpenAI, Anthropic, Google Gemini) to process user queries. Your data may be transmitted to these APIs — and your contract may not make this explicit. Standard SaaS RFPs don’t ask about this because it wasn’t relevant before AI.
- Probabilistic output risk: AI outputs are not deterministic. The same query can return different outputs. Standard IT RFPs don’t ask how this is handled, logged, or audited — but for regulated industries where AI outputs influence decisions, this is a material risk.
- Evolving compliance landscape: The EU AI Act, proposed AI liability frameworks, and sector-specific AI guidance are new since most enterprise RFP templates were last updated. Vendors operating in regulated markets need to demonstrate compliance readiness, not just existing certifications.
The 40 RFP Questions: By Category
Category 1: Data Security & Privacy (Questions 1–8)
- Where exactly is our data processed?
Require a specific answer: cloud region, infrastructure provider (AWS, Azure, GCP), and whether processing is single-tenant or multi-tenant. “Data is secure” is not an answer. - Which third-party LLM APIs receive our data?
If the vendor uses OpenAI, Anthropic, Google Gemini, or any other external model API, your data is leaving the vendor’s infrastructure. Confirm which APIs, what data is sent, and under what terms. - What is your data retention policy for query data and AI outputs?
How long does the vendor retain queries entered by your users, AI-generated outputs, and any associated metadata? Is this configurable? - Is our data used to train or fine-tune your models?
Require a written answer. Many vendors include training opt-outs buried in settings — you want explicit confirmation that your business data is not used for model improvement without your consent. - What encryption standards do you use in transit and at rest?
Require specifics: TLS version, encryption algorithm, key management approach. “Industry standard encryption” is not sufficient. - What is your data deletion process at contract end?
Within what timeframe is your data deleted after contract termination? What proof of deletion is provided? Does this cover backup copies? - Can you provide a full sub-processor list?
GDPR requires that data controllers know who their processors’ sub-processors are. A compliant vendor will have this list and provide it on request. - What is your breach notification SLA?
How quickly will you notify us of a data breach? 72 hours is the GDPR minimum for reportable breaches. Confirm this is guaranteed in the contract, not just in policy documentation.
Category 2: Compliance & Certifications (Questions 9–14)
- Do you hold SOC 2 Type II certification?
SOC 2 Type II (not just Type I) is the baseline enterprise requirement. Request the most recent report and note the coverage period and scope. - Can you provide a signed GDPR Data Processing Agreement?
A DPA is legally required before processing personal data of EU residents. If the vendor does not have a standard DPA ready, that is a compliance red flag. - Is a HIPAA Business Associate Agreement available?
Required for any deployment that processes Protected Health Information. Some AI vendors offer BAAs only at higher contract tiers — confirm availability and cost before shortlisting. - What is your FedRAMP authorization status?
US federal or state government deployments typically require FedRAMP authorization. Ask for the specific level (Low, Moderate, High) and confirm it is current, not “in progress.” - Do you have EU AI Act compliance documentation?
The EU AI Act took effect in phases starting 2024. For EU-regulated deployments, ask what AI risk classification the vendor’s product falls under and what compliance documentation they can provide. - How frequently do you conduct penetration testing?
Annual penetration tests are the minimum standard. Ask for the most recent test summary (executive summary is typical) and confirm the testing is conducted by an independent third party.
Category 3: Deployment Architecture (Questions 15–19)
- Do you offer a self-hosted or on-premise deployment option?
Critical for regulated industries. Self-hosted means the software runs entirely within your infrastructure. Confirm this means zero data egress to vendor or third-party systems. - Is an air-gapped deployment option available?
Air-gapped deployments have no network connection to the public internet. Required for certain government, defense, and high-security financial environments. - Which cloud regions are available for hosted deployments?
Data residency requirements often specify that data must remain in a particular country or region. Confirm which regions are available and whether region selection is guaranteed in the contract. - Who is your infrastructure provider and what redundancy do you have?
Understand the underlying cloud provider, the redundancy architecture (multi-AZ, multi-region), and how this affects uptime commitments. - Is your architecture multi-tenant or single-tenant?
Multi-tenant means your data shares infrastructure with other customers. Single-tenant isolates your environment. For high-security deployments, single-tenant is typically required and should be confirmed in writing.
Category 4: AI Model & Outputs (Questions 20–25)
- Which model versions are currently in production?
Specific version information (not just “GPT-4 class” or “large language model”) should be documented. This matters for audit trails and for understanding when behavior may change. - How are model updates communicated and managed?
How much notice do customers receive before a model version change? Is there an option to stay on a previous version? Who approves model updates in the vendor’s change control process? - What explainability documentation is available?
For regulated industries where AI outputs influence decisions (credit, hiring, healthcare), explainability is a compliance requirement. Ask what the vendor provides to support explainability obligations. - What are your false positive and false negative benchmarks?
If the AI is used for classification, flagging, or recommendation, ask for documented accuracy benchmarks. “Highly accurate” is not a benchmark. - Are human review checkpoints available for AI-driven actions?
If the AI can trigger automated actions (send emails, update records, create tasks), confirm that human approval gates are available and configurable — not just optional settings buried in advanced configuration. - Is full output audit logging enabled?
Every AI-generated output should be logged with timestamp, user attribution, input query, and output text. Confirm log retention period and whether logs are exportable for compliance review.
Category 5: Integration & Connectivity (Questions 26–30)
- Which CRM and ERP systems do you support natively?
Distinguish between “native integration” (built and maintained by the vendor) and “via connector” (Zapier, third-party middleware). Native integrations are more reliable and easier to support. - Is full API documentation available?
Enterprise deployments often require custom integrations. Confirm that comprehensive API documentation is available, whether it is publicly accessible or provided under NDA, and what authentication methods are supported. - Do you support webhooks, and how are they secured?
Webhooks are a common integration mechanism but a common attack surface. Ask how webhook endpoints are authenticated and what payload validation is in place. - What is the data sync frequency and is it configurable?
How often does the AI sync data from connected CRM or ERP systems? Is near-real-time sync available? Can sync frequency be adjusted for performance or compliance reasons? - Who owns integration maintenance when connectors break?
When your CRM vendor releases an API update that breaks a connector, who is responsible for the fix — the AI vendor, your IT team, or a third party? Get this in writing.
Category 6: Pricing & Contract (Questions 31–35)
- Is pricing per user or usage-based?
Per-user pricing is predictable but expensive for large orgs with occasional users. Usage-based pricing can create budget surprises. Understand the model and model your expected cost at 50%, 100%, and 150% of projected usage. - Are there data volume limits, and what are the overage charges?
Some AI platforms cap the volume of records synced, queries processed, or data stored. Confirm these limits and the cost of exceeding them before signing. - What is the uptime SLA and what are the remedies for downtime?
99.9% uptime means up to 8.7 hours of downtime per year. 99.5% means up to 43.8 hours. Ask for the exact SLA, how it is measured, and what credits or remedies apply when the SLA is missed. - What is the exit clause and data portability process?
You should be able to export your data in a standard format within 30 to 90 days of contract termination. Confirm this is in the contract, not just in a policy document the vendor can change unilaterally. - What are the price escalation terms for renewal?
Annual price increases of 5–10% are common in enterprise SaaS. Ask whether price increases are capped, and if so at what percentage, to avoid budget surprises at renewal.
Category 7: Support & Implementation (Questions 36–40)
- Is implementation support included or separately priced?
Many enterprise AI vendors charge separately for implementation services. Confirm what is included in the base contract, what is upsold, and whether a dedicated implementation manager is assigned. - What is the average deployment timeline for a similar customer?
Ask for the typical timeline for customers of comparable size and complexity — and ask what the longest deployment has taken and why. This surfaces realistic expectations vs. sales projections. - Is a dedicated Customer Success Manager assigned post-launch?
A dedicated CSM is standard for enterprise contracts above a threshold price point. Confirm whether you get a dedicated CSM or a pooled support model, and what the escalation path looks like. - What is the escalation SLA for critical support issues?
How quickly does the vendor respond to critical (P1) issues? What constitutes a P1? Is this guaranteed in the contract or just in a support policy? Ask for the last 12 months’ P1 resolution time average. - Can you provide 3 reference customers in our industry?
References should be real customers in your industry or with comparable compliance requirements — not case studies approved by marketing. Speak directly with a peer, not through a vendor-mediated reference call format.
Red Flags to Watch for in Vendor Responses
The answers you receive are as important as the questions you ask. These are the most common red flags in enterprise AI vendor RFP responses.
How to Score and Compare RFP Responses
Use a weighted scoring model to compare vendor responses across the seven categories. Adjust weights to reflect your organization’s priorities. For regulated industries, weight Categories 1 and 2 heavily. For organizations prioritizing time-to-value, weight Category 7 more.
| Category | Questions | Suggested Weight (regulated) | Disqualifier if Failed? |
|---|---|---|---|
| Data Security & Privacy | 8 | 25% | Yes |
| Compliance & Certifications | 6 | 20% | Yes (for regulated orgs) |
| Deployment Architecture | 5 | 15% | Yes (if self-hosted required) |
| AI Model & Outputs | 6 | 15% | No |
| Integration & Connectivity | 5 | 10% | No |
| Pricing & Contract | 5 | 10% | No |
| Support & Implementation | 5 | 5% | No |
| Total | 40 | 100% |
How Worqlo Answers These 40 Questions
Worqlo is purpose-built for enterprise teams in regulated industries where these 40 questions are not optional. Here is how Worqlo addresses the categories most critical to regulated deployments.
- Data Security & Privacy: Worqlo offers fully self-hosted deployment — your data is processed entirely within your infrastructure. No data is routed to third-party LLM APIs. No vendor training opt-out is needed because Worqlo never has access to your data.
- Compliance: SOC 2 Type II, GDPR DPA, and HIPAA BAA are available for enterprise contracts. EU AI Act compliance documentation is provided for EU deployments.
- Deployment: Self-hosted and air-gapped options are available. Data residency is fully controlled by your infrastructure team.
- AI Model Governance: Model versions are documented and communicated in advance of updates. Full output audit logging is enabled by default.
- Integrations: Native integrations with Salesforce, HubSpot, Zoho, Odoo, Slack, and Power BI. Full API documentation available under enterprise agreement.
See How Worqlo Handles Enterprise Procurement
Worqlo’s enterprise team is accustomed to detailed RFP processes. We answer every question in this checklist in writing, with documentation. Book a demo to see the platform and receive our standard enterprise RFP response pack.
Frequently Asked Questions
What questions should I ask enterprise AI vendors?
The most important questions cover where your data is processed and stored, which third-party LLMs receive your data, whether a self-hosted or air-gapped deployment option exists, what certifications the vendor holds, how model updates are communicated, and what the exit clause and data portability terms look like. Vendors that deflect these questions in writing warrant extra scrutiny before shortlisting.
What is an AI vendor RFP?
An AI vendor RFP (Request for Proposal) is a structured document that enterprise procurement and IT teams use to evaluate AI software vendors. Unlike a standard IT RFP, an AI vendor RFP includes questions specific to AI risk areas: model governance, data routing to third-party LLMs, explainability, output logging, and regulatory compliance documentation that standard SaaS tools don’t require.
What certifications should enterprise AI vendors have?
At minimum, enterprise AI vendors should hold SOC 2 Type II certification and provide a signed GDPR Data Processing Agreement. Depending on your industry: HIPAA Business Associate Agreement (healthcare), FedRAMP authorization (US federal or state government), and EU AI Act compliance documentation (EU-regulated sectors). Penetration test reports should be available on request, typically conducted annually by an independent third party.
How do I compare enterprise AI vendors?
Score vendor RFP responses across seven dimensions: data security and privacy, compliance certifications, deployment architecture, AI model governance, integration capabilities, pricing and contract terms, and support and implementation. Weight each dimension according to your organization’s priorities — for regulated industries, security and compliance scores should carry the most weight. Disqualify vendors who cannot answer Category 1 and 2 questions in writing with verifiable documentation.
What are red flags in an enterprise AI vendor evaluation?
Key red flags include: a vendor cannot confirm in writing where your data is processed; they do not have SOC 2 Type II; they cannot confirm whether your data is used to train models; there is no exit clause or data portability option; uptime SLA is below 99.5%; implementation support is heavily upsold; and reference customers are unavailable or all outside your industry. Any single red flag in Category 1 or 2 warrants disqualification for regulated deployments.
How long should an enterprise AI RFP process take?
A thorough enterprise AI RFP process typically runs 6 to 10 weeks: 1 to 2 weeks to build and distribute the RFP, 2 to 3 weeks for vendors to respond, 1 to 2 weeks to score responses and shortlist, and 2 to 3 weeks for demos, reference checks, and contract negotiation. Rushing this process is a common cause of post-deployment security and compliance issues — and typically costs more to remediate than the time saved by shortcutting evaluation.
Should enterprise AI vendors offer self-hosted deployment?
For regulated industries — healthcare, financial services, government, legal — self-hosted or on-premise deployment is often a requirement, not a preference. Self-hosted deployment means your data never leaves your infrastructure and is never routed to a third-party cloud or LLM API. Not all AI vendors offer this option; those that do typically price it as an enterprise tier. If self-hosted is a requirement for your organization, confirm availability and pricing before shortlisting any vendor.
What should the exit clause in an AI vendor contract include?
The exit clause should specify a defined data export format and timeline (typically 30 to 90 days post-termination), confirmation that your data is deleted from vendor systems after export, a process for exporting any model fine-tuning data if applicable, and that no vendor lock-in prevents you from migrating to another system. Vendors who resist adding clear exit terms are signaling a long-term lock-in strategy that should factor into your evaluation score.
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