AI Procurement Checklist for Government and Healthcare Teams

Government and healthcare buyers should score AI vendors on auditability, data residency, human-override controls, and exit terms before they score them on demo polish. Write those requirements into the RFP itself, because retrofitting them after a system is already touching citizen or patient records is slower, more expensive, and sometimes not possible without a rebuild.

Most procurement teams still run AI vendor selection like a standard software RFP: features, price, references, a demo. That process misses the failure modes that actually get public-sector and healthcare deployments in trouble: an AI system that can't explain a decision, data that lands in the wrong jurisdiction, or a vendor relationship you can't unwind without losing your own data. Here's a checklist built around the questions that matter.

Start with auditability, not accuracy

A vendor demo will always show you the happy path. What you need to see is what happens when the system is wrong, and whether you can reconstruct why.

• Ask for a logged failure case. Not a hypothetical, an actual transcript of the system making a mistake, plus the audit trail that let someone catch it.
• Check what's actually logged. Inputs, outputs, model version, and timestamp at minimum. If the vendor logs only the final answer, you have no way to investigate a complaint six months later.
• Confirm logs are exportable, not locked inside the vendor's dashboard. If your legal or compliance team ever needs records for an audit, you need to be able to pull them without the vendor's cooperation.

This is also where system architecture quietly matters. We've found in our own agent work that a single well-scoped model call is far easier to audit than a five-step agent chain, because there's one input, one output, and one place to look when something goes wrong. A vendor running a long chain of tool calls and sub-agents to answer a simple question isn't automatically doing something wrong, but it is taking on more surface area to explain later, and you should ask them how they log intermediate steps, not just final outputs. If you're evaluating vendors on this axis specifically, single-call vs agent chains is worth reading before the RFP goes out.

Data residency and access are contract terms, not technical details

Where the data lives, who can see it, and what happens to it after the contract ends are procurement questions, not IT questions to be settled later.

• Get explicit answers on where data is processed and stored, including any subprocessors (the vendor's own LLM provider, cloud host, or analytics tools).
• Ask whether your data trains the vendor's models, and if so, for whom. Many API-based AI products default to not training on customer data, but managed or fine-tuned offerings sometimes do, and it needs to be opt-in, not buried in a terms-of-service update.
• For healthcare specifically, confirm the vendor will sign a Business Associate Agreement if PHI is involved; a vendor that hesitates here is telling you something about how seriously they take the rest of the checklist.
• For anything touching EU citizens, understand your obligations under the EU AI Act. Many of its obligations fall on the deploying organization, not just the vendor, so your contract needs to say who does what: who runs the risk assessment, who maintains technical documentation, who handles a regulator's request.

Human override is a design requirement, not a nice-to-have

For anything that affects benefits eligibility, a diagnosis, or a legal outcome, there needs to be a real person who can intervene before harm happens, not after.

• Ask the vendor to show you the actual override interface, not describe it. Where does a caseworker or clinician see the AI's confidence level, and how do they reject or escalate a decision?
• Confirm the override path is fast enough to matter. A review process that takes three days doesn't help someone denied a benefit today.
• Understand default behavior on failure. Does the system fail closed (flag for human review) or fail open (proceed with a best guess)? For high-stakes decisions, you want fail closed as the default, not a configuration option someone forgot to set.

This connects directly to how the underlying system is architected. If you're building or specifying this internally rather than buying it fully built, court-ready architecture for healthcare AI covers what an auditable, defensible system actually looks like at the infrastructure level.

Contract terms that protect you after signing

Procurement doesn't end when the contract is signed. The terms that matter most are the ones you hope you never need.

• Data and model export rights. If you leave the vendor, do you keep your historical data and any fine-tuned model weights, or does the relationship reset to zero?
• Change notification for model updates. LLM-backed systems change behavior when the underlying model changes. You want advance notice and, ideally, a staging window before a new model version goes live in production.
• Incident response SLAs, specifically for the AI system, not just general uptime. A wrong diagnosis or wrongly denied benefit is a different severity class than a slow page load, and the contract should reflect that.
• Liability and indemnification language that names AI-specific failure modes (hallucination, bias, data leakage) rather than generic software defect language written before LLMs existed.

If you want a more general framework for scoring vendors beyond this checklist, our AI agent vendor evaluation checklist covers the broader criteria (cost structure, latency, support model) that apply outside the compliance-heavy sectors too. And if evals are part of your evaluation process, or should be, putting evals into the contract itself is worth reading before you finalize terms.

What this looks like in practice

None of this requires an AI specialist on your procurement team. It requires asking for evidence instead of promises: a real failure case instead of a clean demo, an actual export of logs instead of a description of what's logged, a live walkthrough of the override screen instead of a slide about "human-in-the-loop." Vendors who've actually built defensible systems will have this ready. Vendors who haven't will need weeks to produce it, which is itself useful information before you sign anything.

If you're scoping an AI system for a government or healthcare context and want a second opinion on the RFP or the vendor shortlist, let's talk.