How to Vet an AI Vendor for Your Insurance Agency

Most agencies will not build their own AI. They will buy it: a call summarizer here, a quoting assistant there, a lead scorer bolted onto the CRM. That makes the purchase decision the place where AI risk actually enters the agency, and most owners make it with a demo and a gut feeling.

Regulators are already looking at this exact gap. The NAIC’s page on artificial intelligence reports that “Roughly half of the models used for marketing were developed by third-party vendors,” and notes that a working group was formed in 2024 to “evaluate and develop a regulatory framework around the use of third-party AI data and models” used by insurers, per the NAIC’s artificial intelligence topic page. The page is about insurers, not agencies, so it does not set your obligations. It does show where the attention is heading: toward the tools you did not build.

This guide gives agency owners and managing producers a practical way to vet an AI vendor before signing. It is a workflow aid, not legal advice, so loop in your compliance contact and your E&O carrier for anything binding.

Why vendor vetting is different for AI

Buying a CRM is mostly a feature comparison. Buying AI adds a second question: how does it behave when it is wrong?

The National Institute of Standards and Technology makes the point in its AI Risk Management Framework: “The technologies acquired from third-party entities may be complex or opaque, and risk tolerances may not align with the deploying or operating organization.” In plain terms, the vendor’s idea of acceptable error may not match yours, and you may not be able to see inside to check. The framework is published as NIST AI 100-1.

For a life agency, a wrong answer is not abstract. A bad carrier suggestion can end in a declined application. A wrong summary of a client call can end up in the case file. We covered the failure modes in AI hallucinations in insurance, and they apply to purchased tools just as much as home-built ones.

A six-question vendor checklist

NIST’s framework is written for organizations building and deploying AI, not for a ten-person agency. Still, its third-party language translates into questions any owner can ask. Here are six, each tied to the framework text where it fits.

1. Who is accountable when the tool is wrong?

The framework’s GOVERN function says that “Contingency processes are in place to handle failures or incidents in third-party data or AI systems deemed to be high-risk.” Ask the vendor what happens when a recommendation is wrong: who gets notified, how fast, and what the process is for correcting records. If the answer is a link to terms of service, that is an answer.

2. What data does it touch, and where does that data go?

Insurance conversations carry health details, finances, and family information. Ask where client data is stored, whether it is used to train models for other customers, how long it is retained, and how it is deleted if you leave. Get the answers in writing and compare them with your agency’s privacy policy and your carriers’ contracting requirements.

3. Can it show its reasoning?

If a tool recommends a carrier, a product, or a next step, can the agent see why? A suggestion you cannot inspect is a suggestion you cannot defend to a client, a carrier, or a market conduct examiner. This is the same logic behind explainable AI in insurance: the agent needs the why, not only the answer.

4. Where is the human in the loop?

The framework calls for human oversight processes to be “defined, assessed, and documented in accordance with organizational policies.” Translate that to your shop: which outputs can go straight to a client, and which need a licensed agent to review first? A good vendor makes review easy. A risky one makes it optional or invisible.

5. How is the tool monitored after you buy it?

The same framework says that “AI risks and benefits from third-party resources are regularly monitored, and risk controls are applied and documented.” Ask what the vendor monitors, and then decide what you will monitor yourself. A simple version is a monthly spot check: pull ten outputs, compare them against the source documents, and record what you found.

6. What can you get out, and what happens if you leave?

Ask for exports of your data and of any logs or summaries the tool generated. A tool that traps your case notes is a switching cost you only discover later.

Where the regulators are, and where they are not

The NAIC page describes a Model Bulletin on the use of AI by insurance companies, adopted in December 2023, which “sets forth expectations as to how insurers will govern the use of AI.” It also says that “decisions or actions made or supported by AI must comply with all applicable insurance laws and regulations.” Our overview of what the NAIC’s AI rules mean for agents walks through how that reaches producers in practice, often through carrier expectations and state law rather than a direct rule on the agency.

The practical takeaway: do not wait for a rule aimed at agencies. Carriers are the ones under the bulletin, and an agency that can show it vetted its tools will have an easier conversation when a carrier asks.

Document the decision

Whatever you decide, keep a one-page record: the tool, the use case, the six answers, who approved it, and the date. Revisit it when the vendor changes its terms or adds a feature that touches client data. If your team already works from a list of AI tools, add a column for the date each one was last reviewed.

How Peach Pilot thinks about this

We build tools for life insurance producers, so we are on the vendor side of this conversation. We would rather you ask us these six questions than skip them. Peach Quote is built to support the agent’s work, not replace the agent’s judgment, and the checklist above is the standard we expect to be held to.

Peach Pilot supports licensed agents’ workflow. Carriers make final underwriting and issue decisions.