A private company can adopt AI on a hunch. A public agency has to be able to explain it to a vendor, an auditor and a records requester. Here is how to evaluate AI in procurement software, ending with a checklist you can print.
Every procurement software vendor now has an AI story. For a public agency the useful question isn’t whether the AI is impressive in a demo. It’s whether the agency can stand behind what the AI did, months later, in front of someone who disagrees with the outcome.
Why AI Evaluation Is Different for Public Agencies
Three things set government apart from a typical enterprise buyer:
- Transparency. Procurement decisions spend public money, and the reasoning behind them is expected to hold up to public scrutiny.
- Protests. An unsuccessful bidder can challenge an award. If AI shaped the evaluation, the agency has to be able to show how, and why that was fair.
- Records law. Documents and decision records may be subject to public records requests and retention rules that vary by state. AI outputs that influence a decision can be part of that record.
An AI tool that works well but can’t be explained or retained properly isn’t an asset in this setting. It is a liability that hasn’t been triggered yet.
Explainability: What “Show Your Work” Should Look Like
Ask the vendor to show a real output, not a description. A defensible result should let a reviewer see:
- What the AI read. The specific documents, sections and fields it used, not a general claim that it “analyzed the proposal.”
- What it concluded. A clear statement, in plain language, rather than a score with no derivation.
- Where each claim came from. Citations that link back to the source text, so a person can verify in seconds instead of re-reading a 200-page response.
If the answer to “why did it say that?” is “that is how the model works,” the tool isn’t ready for a decision that might be challenged.
Data Residency and Model Training
Procurement documents include pricing, proprietary vendor methods and sometimes personal information. Ask directly:
- Where is our data stored and processed, and can it be kept in a specific region?
- Is our data, or our vendors’ submissions, ever used to train models, yours or a third party’s?
- Is our environment isolated from other customers, and how is that enforced?
- What happens to our data, and to derived outputs, if we end the contract?
A vendor who has good answers will give them in writing. A vendor who hedges is telling you something.
Human Approval Points
The strongest design principle in public sector AI is simple: AI reads and drafts, people decide. Ask the vendor to show exactly where a person approves, rather than where one could.
- Can the AI ever award, reject, release payment or change a record on its own?
- Is a named person recorded as the approver for every decision?
- Can a reviewer override or edit the AI’s output, and is the original preserved?
If approval is optional, or buried in a setting that can be turned off, it will eventually be skipped under deadline pressure.
Audit Trail Requirements
An audit trail is not a log file somebody can export if asked. For AI it should capture the inputs, the output, the version of the system that produced it, who reviewed it and what they decided, all in a form that is time-stamped, tamper-evident and retained according to your records schedule. Ask whether you can reconstruct, for any past decision, what the AI said and what the person did with it, without calling the vendor.
If you can’t reconstruct what the AI said and who approved it, you don’t have a trail. You have a story.
A Printable 12-Question Checklist
Bring this to your next vendor evaluation. A strong vendor will answer all twelve without hesitation.
- Can the AI show which documents and sections each output was based on?
- Does every claim link back to a verifiable source passage?
- Can a non-technical reviewer understand the reasoning without help from the vendor?
- Where is our data stored and processed, and can we choose the region?
- Is our data ever used to train models, yours or anyone else’s?
- Is our environment isolated from other customers?
- Can the AI take any action, such as an award, a rejection or a payment, without a person approving it?
- Is the approving person named and recorded for every decision?
- Can a reviewer override the AI, with the original output preserved?
- Does the audit trail capture inputs, outputs, system version, reviewer and decision?
- Can we export the full record for a protest, an audit or a records request without the vendor’s help?
- What happens to our data and outputs if we end the contract?
If you’d like to see how SmartDocs answers these, you can book a discovery call and we will walk through all twelve.
Good breakdown of the shift from search to execution — that is where this actually starts changing day-to-day work.