How AI Search Summaries Describe Your Returns Policy
AI summaries now paraphrase returns policies for shoppers before they visit. When the paraphrase is wrong, the store lives with a promise it never made.
A growing share of shoppers meet a stores returns policy first as an AI summary: a sentence or two, generated from the policy page, presented as settled fact. If the summary drops a condition, shortens a window, or invents free returns, the shopper arrives holding a promise the store never made, and the argument happens at the returns desk.
This guide covers how summaries go wrong on policy pages, what stores can do to make the source text harder to misread, and how to check what the summaries currently say.
Paraphrases drop conditions first
Returns policies are built from conditions: windows, categories, condition of goods, who pays postage. Summarisation compresses, and conditions are the natural casualty. Thirty days for unworn items with tags becomes thirty day returns, and the dropped words are the ones the policy exists for.
Exceptions fare worst of all. Final sale categories, hygiene exclusions, and personalised goods are exactly the lines a summary trims for length, and exactly the lines that generate disputes when a shopper relies on the trimmed version.
Write policy pages that summarise safely
Lead with a short, plain summary paragraph written by the store itself: the window, the condition, the cost, and the main exceptions in complete sentences. A summary model given a clean summary to work from mostly repeats it. A model given forty paragraphs of legal texture improvises one.
Keep the critical facts in sentences, not only in tables, footnotes, or expandable sections. Content hidden behind interactions is content summaries may never see, and a fact the summary cannot see is a fact shoppers will be told wrongly or not at all.
Check what is being said, regularly
Search the questions shoppers ask, returns window, free returns, final sale, and read what the summaries answer for your store. Log the answers with dates. The first check usually finds at least one condition dropped, and finding it yourself is considerably cheaper than hearing it quoted by a customer.
Where a summary is wrong, the fix is almost always the source page: clearer lead summary, conditions in plain sentences, exceptions stated near the headline terms. Correct the source, then recheck, because summaries follow sources with a lag.
Brief the returns desk on the gap
Until sources and summaries align, the desk needs to know the summary version exists: what it says, where it differs, and how the store wants those conversations handled. A shopper quoting a wrong summary is not lying. They are citing the internet as it was presented to them.
Track disputes that cite a summary. A rising count is a content problem with a measurable cost, and it justifies the policy page rewrite in the terms finance understands: fewer arguments, fewer goodwill refunds, fewer escalations.
Related reading
If this topic is useful, these earlier guides go deeper on connected questions: When AI Search Answers Send Shoppers to Out of Stock Products, AI Written Product Descriptions, Accuracy, and the Returns They Cause, and When AI Recommendations Get Prices and Stock Wrong.
Frequently asked questions
Why do AI summaries get returns policies wrong?
Because summarisation compresses, and policies are made of conditions. Windows lose their conditions, exceptions get trimmed, and a careful legal page becomes a simple claim that is broader than anything the store actually offers.
What is the single best defence?
A short, plain summary paragraph at the top of the policy page, written by the store, stating the window, condition, cost, and main exceptions in full sentences. Summaries repeat good summaries. They improvise around everything else.
How can a store find out what summaries say about it?
Search the returns questions shoppers ask and read the generated answers for your store, logging them with dates. Recheck after any policy page change, since summaries follow sources with a lag.
What should staff do when a shopper quotes a wrong summary?
Treat it as accurate reporting of what they were told, resolve it under a briefed policy, and log it. Those logs are the evidence that turns a vague worry about AI accuracy into a costed case for fixing the source page.