Seven Insurance Messages AI Should Not Draft Alone

AI is genuinely good at the volume work in an agency. First replies, follow-up sequences, review requests, newsletters, the fourth version of a message you have written a hundred times. Used carefully it can cut the time spent producing routine first drafts.

There are also places where handing the draft to a model can create a problem that takes far longer to unwind than the draft took to produce. Here are seven, and what to do in each case instead.

1. A coverage determination (decision prohibited)

Whether a specific loss is covered is a question about a specific policy, its endorsements, its exclusions and the facts of the claim. A model has none of that, and it will still answer, because a plausible answer is always available to it.

Instead: have it explain coverage in general terms from language you supply, and keep every statement about this client’s situation in your own hands.

2. A rate-change reason you were not given (approved text only)

Covered at length elsewhere on this site, but it belongs on this list. The rule is not that the carrier must have written the sentence. It is that nothing unsupported or unauthorized goes out.

Instead: paste the carrier-approved explanation into the prompt as a bracketed input, or write the message without one.

3. A declination or ineligibility notice (approved text only)

Declinations, cancellations, nonrenewals, underwriting ineligibility and claim decisions are not interchangeable, and the wording, reasons, timing and delivery requirements vary by state, product and issuing entity. A generated explanation is a guess wearing the clothes of an official answer, and softening or expanding required language is its own problem.

Instead: the formal notice comes from your carrier or your compliance process, unaltered. AI can help you write the plain-language service message that sits alongside it, from approved wording.

4. Anything containing a real client identifier (privacy restricted)

Names, dates of birth, addresses, policy and claim numbers, Social Security numbers, financial and medical detail. This is not a quality issue, it is a privacy one, and it is governed by your agency’s policies rather than your judgment in the moment.

Instead: placeholders in, real details added by you after the draft exists. Almost every workflow works fine on anonymized facts.

5. A product or limit recommendation (decision prohibited)

Product and limit recommendations have to be made by an appropriately licensed professional under applicable state law, carrier rules and any line-specific standard of care. Formal suitability or best-interest standards apply to certain products in particular, notably annuities, and adoption varies by state. A model can describe what an umbrella policy generally does. It cannot tell you whether this client should buy one.

Instead: use it to prepare the conversation, not to make the call. Explainers, question lists, recap emails after a review you conducted.

6. Marketing that contains a number (approved source plus review)

Premiums, savings figures, discount amounts, comparative claims. Insurance advertising rules are state and product specific rather than one national standard, and a number a model produced is a number nobody verified.

Instead: every figure comes from an approved source and goes through whatever review your agency requires. The tool writes the sentence around the number, not the number.

7. A claim outcome or timeline (decision prohibited)

“We should have this resolved quickly.” “This will be approved.” Even softly, even with hedging. You often do not control the claim, and the client will remember the sentence rather than the hedge.

Instead: report position rather than forecast. What has happened, who has it, what the next step is, when you will next be in touch.

The pattern underneath most of them

These are not all the same kind of problem. Some are decisions that are not the tool’s to make. Some can be drafted, but only from language somebody approved. One is a privacy rule rather than a quality rule. Read the label on each.

What they share is a fact only you can supply, left open for the model to fill. Before you send any request, ask what facts this draft needs that the model has no way to know, and put each one into the prompt as a bracketed input you fill yourself.

That is one part of making the tool more usable in a regulated business, and it tends to produce better writing too, because a draft built from real specifics reads better than one built from plausible generalities.

What is left

Strike those seven off and a great deal remains. The first reply to a new lead, the three-touch quote follow-up, the renewal checkup invitation, the claims check-in that asks about the person rather than the file, the review request that does not sound automated, a month of social posts in one sitting, the recap after an annual review.

That is a great deal of an agency’s writing, and none of it requires the model to know anything it should not.

The Claude Workflow System for Insurance Agents covers 66 of these, all built on the bracketed-input structure described above, with a pre-send check to run before anything leaves your outbox.