The short version

You do not need to bet on shoppers adopting AI agents to justify this audit. Where AI already recommends products, bad product data is showing up as returns, wrong listings and lost shortlist spots today.

The useful question is not "how do I optimise for agents?" It is:

"If a machine read only my structured product data, would it describe and recommend my product correctly?"

This guide walks Amazon and Shopify sellers through four checks, plus one checkout review:

1. Shop Direct check: is Amazon already showing your website's products, and are the details right?
2. Attribute audit: are the attributes agents rely on (specs, compatibility, sizing, materials) complete and consistent across Amazon, Shopify and Google Merchant Center?
3. Shopper-prompt testing: what do ChatGPT, Google AI Mode and Alexa for Shopping actually say about your products?
4. Returns-to-data loop: which return reasons point to a gap in your product data?
5. Checkout review: which of your products and checkout features break in Google AI Mode's direct checkout?

The best mental model:

• Your product record is the source. One governed set of facts per product.
• Feeds and listings are the pipes. Merchant Center, Shopify Catalog, Amazon attributes, Shop Direct feeds.
• AI assistants are the readers. They repeat what the pipes give them, including your mistakes.
• Return reasons are the error log. They tell you where the record failed a real customer.
• You are still the accountable operator. Nothing in this guide should be published or switched off without a human check.

Who it is for: DTC brands on Shopify, Amazon sellers and vendors, and agencies running catalogues for either. Most of the channel features below are US-only for now; the data work applies everywhere.

Where each AI channel gets your product data

Before auditing anything, know which pipe feeds which assistant. Fixing your PDP copy does nothing for a channel that reads only your feed.