The Fortune 500 AI Vendor Evaluation Checklist: 12 Questions Before You Sign

Enterprise AI vendor evaluation checklist searches spike every quarter as procurement teams and CIOs face an expanding market of vendors making increasingly similar claims. Everyone says they are AI-native. Everyone claims enterprise-grade security. Everyone has a demo that looks impressive.

The challenge is that AI vendor quality is difficult to assess from a proposal and a demo. The real test — can they ship production AI that operates correctly at scale, inside your regulatory environment, and integrated with your existing systems — does not show up in sales presentations. These 12 questions are designed to surface the real signal before you sign.

We have been on the receiving end of these evaluations. We also help our clients run these evaluations when they're selecting other vendors as part of a broader technology ecosystem. These questions work regardless of which vendor you're considering.

1. Show me production AI — not a demo.

What you're testing: Whether the vendor has shipped real AI to real production environments, or whether their portfolio consists primarily of proofs-of-concept and internal tools.

Why it matters: The gap between a compelling demo and a production-grade AI system is where most vendor capability problems live. Demo environments use clean, curated data. Production environments do not. Demos do not experience edge cases. Production systems do, daily.

What a good answer looks like: A vendor who is confident in their production record will name specific clients (with permission), describe the specific AI they deployed, and offer to connect you with a reference at that client. Be specific: "Can we talk to the technical lead at [client] who owns this in production today?"

2. What does your team look like — not the org chart, the people on my account?

What you're testing: Whether the senior talent you meet in the sales process is the talent that will actually work on your engagement.

Why it matters: A common pattern in consulting and technology services: senior partners close the deal, junior staff deliver the work. In AI specifically, the quality of the engineers and data scientists working on your system determines the outcome. The account executive's track record is irrelevant.

What a good answer looks like: The vendor names specific individuals who will work on your account and describes their backgrounds concretely. "Our lead AI engineer for your engagement has shipped production conversational AI for [three clients in your industry]." Ask to meet those individuals before signing, not after.

3. How do you handle PHI, PII, and sensitive data — and do you use client data to train your models?

What you're testing: Data security architecture and whether your organization's data will be used to benefit the vendor's other clients.

Why it matters: Many AI vendors use interaction data to improve their models — which may mean your proprietary customer data trains models that benefit your competitors. This is sometimes buried in service agreements. For regulated industries, data handling practices may also create HIPAA or GDPR exposure.

What a good answer looks like: The vendor should answer clearly and in writing: "We do not use client data to train any model that serves other clients. Client data is isolated to your deployment environment." They should provide their SOC 2 Type II report, execute a BAA if relevant, and describe their encryption and access control architecture without prompting.

4. What is your realistic time to production — and what does "production" mean?

What you're testing: Actual delivery speed and whether the vendor and you agree on what "production" means.

Why it matters: "Production" is used loosely. Some vendors call a demo environment that uses your data "production." Others call an internal pilot with 50 users "production." True production is a system operating with real users, real data, at the scale the business requires, with monitoring and support in place.

What a good answer looks like: A specific timeline with defined milestones. "We typically reach production — live with real users and real data, monitored, with SLA in place — in 60 to 90 days. Here is the milestone structure and what determines whether we're at the faster or slower end of that range."

5. What happens when the AI is wrong?

What you're testing: Error handling philosophy and post-launch support reality.

Why it matters: AI systems produce incorrect outputs. This is not a failure condition — it is an expected behavior that must be managed. How a vendor thinks about error handling reveals whether they have shipped real production systems. Vendors who have not shipped real AI at scale often have no good answer to this question.

What a good answer looks like: A specific escalation path. "When the AI produces an output outside defined confidence thresholds, it escalates to a human. We have a monitoring dashboard that flags anomalies. Our SLA for a production incident is [X hours]. Here is the runbook."

6. What does your governance and oversight model look like post-launch?

What you're testing: Whether the vendor treats launch as the finish line or the starting line.

Why it matters: AI systems require ongoing oversight. Model drift — where AI performance degrades as underlying data changes — is a real and documented phenomenon. AI systems that are not monitored post-launch will underperform their initial results within 3 to 6 months.

What a good answer looks like: A defined post-launch monitoring protocol, regular review cadence, and a clear process for updating model behavior when needed. The vendor should be able to describe what their monitoring looks like today for an existing production client.

7. What are all the costs — including what's not in the proposal?

What you're testing: Total cost of ownership transparency.

Why it matters: AI vendor proposals often understate total cost. Common additional costs not in the initial proposal: cloud infrastructure (charged at cost or marked up), API call volume overages, data storage, model retraining fees, ongoing support beyond an initial period, and custom integration work that "wasn't in scope."

What a good answer looks like: A line-item breakdown of all costs over 24 months, including infrastructure, support, model maintenance, and usage-based components. A vendor with nothing to hide will provide this without being asked twice.

8. How do you handle regulatory compliance specific to our industry?

What you're testing: Whether the vendor understands the regulatory environment your AI will operate in or is assuming compliance is your problem to solve.

Why it matters: In healthcare, financial services, and government, the regulatory constraints on AI are not theoretical — they determine what the system can be built to do. A vendor who builds your AI without understanding these constraints will produce a system that cannot be deployed in your environment.

What a good answer looks like: The vendor describes specific compliance considerations for your industry without prompting, asks about your specific regulatory environment early in the conversation, and has delivered compliant AI in your industry before.

9. What is your track record in our specific industry or with our type of use case?

What you're testing: Relevant experience versus general AI capability.

Why it matters: A vendor who has shipped conversational AI for automotive lead conversion has learned lessons about automotive buyer behavior, dealership management systems, and inventory data integration that a general-purpose AI vendor has not. Industry-specific experience compresses deployment timelines and reduces risk.

What a good answer looks like: Named clients in your industry, with specific use cases described, and references you can contact. Not "we have experience in your sector" — specific organizations and specific systems.

10. What does your minimum viable engagement look like, and how do you structure pilots?

What you're testing: Whether the vendor is willing to start small and earn a larger engagement, or whether they require a large commitment before demonstrating value.

Why it matters: A vendor that requires a six-figure commitment before proving anything has misaligned incentives. A vendor that structures a 30 to 60 day pilot with clear success criteria, reasonable cost, and a specific production path is structuring for your success.

What a good answer looks like: A defined pilot structure with specific deliverables, a timeline, a cost that reflects the scope, and a clear escalation to full production if the pilot succeeds. The pilot should use real data and produce a real assessment of production readiness.

11. Who owns the AI after you build it?

What you're testing: IP ownership, portability, and what happens if the vendor relationship ends.

Why it matters: Some vendors build AI on proprietary platforms that cannot be transferred. If the relationship ends, you lose the system. Others build on open-source or standard infrastructure that your internal team or another vendor can maintain. The difference is significant for long-term total cost of ownership and negotiating leverage.

What a good answer looks like: Clear IP ownership terms in the contract: you own the models, the training data, and the outputs. The vendor retains ownership of their underlying platform (reasonable) but not of the AI they build for you (not reasonable). Have your legal team review the IP provisions before signing.

12. Can you give me three references who will tell me what went wrong?

What you're testing: Vendor honesty and resilience.

Why it matters: Every vendor has references willing to say positive things. References who will describe a problem the vendor encountered and how they responded are far more valuable — they reveal whether the vendor takes accountability, communicates proactively, and improves under pressure.

What a good answer looks like: The vendor provides references without defensiveness and does not try to control the narrative of those conversations. References who describe a challenge — a timeline slip, an integration problem, a model performance issue — and describe how the vendor handled it are the most credible signal you will get.

How to Use This Checklist

Run these 12 questions in your next AI vendor evaluation. Score each vendor on a simple scale: strong answer, acceptable answer, weak or absent answer. Weight questions 1, 3, 5, and 11 most heavily — they are the highest-risk areas where vendor weakness is most likely to surface in production.

If a vendor cannot answer questions 1, 3, and 5 clearly and with evidence, they are not ready to be your production AI partner regardless of how compelling the demo is.

[Download the Vendor Evaluation Checklist PDF — coming soon]