The shopping journey is moving into the chat window
More and more product discovery now starts inside an AI assistant. A shopper asks for “a warm waterproof jacket for autumn commutes under 200 euros,” and the assistant replies with specific products, complete with prices and links. OpenAI has formalized this with its Agentic Commerce Protocol, and retailers like Target, Sephora, and Best Buy are already part of it. If you sell on Shopify, your catalog is wired in automatically.
This is a different motion from classic search. The shopper never scrolls a results page full of blue links. They get one answer, or a short shortlist. Either your product is in that answer or it isn't.
Agents read feeds, not pages
To recommend a product, an AI agent first has to understand it. It does that by reading a structured product feed: the identifiers, titles, descriptions, attributes, prices, availability, and images that describe each item. OpenAI's own feed specification asks for exactly this kind of structured detail. The agent matches the shopper's question against that data and surfaces what fits best.
So the deciding factor sits in your product data. Your marketing site barely enters into it.
Which means your product data is the product
An agent can only recommend what it can understand. A product with a vague title, three empty attributes, and a one-line description gives it almost nothing to match against, so it gets passed over for a competitor whose data is clear and complete. The shopper never learns your product existed.
This is the uncomfortable part for a lot of brands. A catalog that has been “good enough” for a human browsing your site, who can infer from a photo and fill in the gaps, is often not good enough for an agent that has only the data to go on.
To an AI agent, your product data isn't the description of the product. It's the product.
What a feed-ready catalog looks like
Getting ready is less about buying a new tool and more about the state of your product data. A catalog an agent can confidently recommend tends to have:
- Attributes filled in, not just present. Material, fit, dimensions, use case, and whatever your shoppers filter on. Empty fields are missed matches.
- Descriptions that answer real questions. Shoppers ask in full sentences now, and copy that responds to “is this good for X” gives the agent something concrete to cite.
- Every market written natively. An agent answering in German recommends products described in German, and translated-as-an-afterthought copy reads thin.
- The same product looking the same everywhere, whether an agent reads it from your Shopify feed, a marketplace, or a syndication partner.
- Copy that keeps up. The way people ask keeps shifting, and launch-day descriptions slowly fall out of step with how shoppers describe what they want.
This is product operations, not a bolt-on
It's tempting to treat AI visibility as a separate project: hire a tool, optimize, export a spreadsheet, upload it. But the feed an agent reads is just your product catalog. If the source is incomplete, optimizing the output is rework you will repeat next season.
So fix the source instead. Structure the catalog once, enrich it with the descriptions, attributes and FAQs that answer how people search, hold it in every language, and let it reach every channel on its own.
None of which is theoretical. ICIW backfilled their entire assortment to 100% coverage in hours. Djerf Avenue generates on-brand, search-optimized copy across every language in minutes. Neither bought an “AI visibility” product to get there.
The work that makes a catalog good has not changed. It has just started deciding whether an AI recommends you.