By Steve Merrill, Founder of WRKNG Digital | September 18, 2026
A handful of feed fields decide whether AI recommends your product. The rest are noise. Price, availability, a specific title, and real attributes. Get those right and you're in the running. Miss them and you're skipped.
We've run thousands of Shopify products through an audit. The pattern is always the same. The stores that win aren't writing better copy. They're feeding the machine cleaner facts.
Which fields does AI actually read?
Title, price, availability, brand, and product attributes like size, color, and material. These are the fields an AI can confirm and compare. They answer the shopper's real question, which is 'does this fit what I asked for.'
Google's product data specification is a good map of what matters. If a field is required there, an AI wants it too.
Why does the title carry so much weight?
The title is the first thing an AI matches against a query. 'Blue running shoe' beats 'Model X-100' every time, because it names what the shopper asked for in words the machine understands.
Here's the bottom line: a vague title makes the AI guess what you sell. A specific one makes the match obvious. Specific wins.
What about availability and price?
An agent won't recommend something it can't confirm you can sell. Real-time availability and an accurate price are trust signals. Wrong stock or a stale price and the AI learns not to trust your feed.
Valid Offer markup carries price and availability in a form the machine reads instantly. That's the field that turns a maybe into a recommendation.
Which fields don't move the needle?
Marketing fluff. 'Best-in-class.' 'Premium quality.' An AI can't verify a claim, so it ignores it. The adjectives that sell humans are dead weight to a machine.
Fill the attribute fields instead. Material, dimensions, compatibility, care. Those are facts the AI uses to match your product to a specific need.
Where should you start?
Pick your top ten sellers. For each one, check that title, price, availability, brand, and the core attributes are complete and accurate. That's the 20% of fields that drives most of the recommendations.
You don't need a perfect feed on 2,000 products. You need a clean feed on the products that matter. Start there and expand.
Further reading
- Google Merchant product data specification
- Schema.org Offer reference
- Google product structured data guidelines
- Schema.org Product reference
Frequently Asked Questions
What product feed fields matter most for AI recommendations?
Title, price, availability, brand, and concrete product attributes like size, color, and material. These are the fields an AI can confirm and compare against a shopper's query. Marketing adjectives and unverifiable claims carry no weight because the AI can't verify them.
Does the product title really matter that much?
Yes. The title is the first thing an AI matches against a query. A specific, descriptive title like 'women's waterproof hiking boot' matches shopper intent far better than an internal model code, so it gets surfaced more often in AI recommendations.
How many products should I optimize first?
Start with your top ten sellers. Getting title, price, availability, brand, and core attributes clean and accurate on the products that drive most of your revenue delivers most of the recommendation gains. Expand to the rest of the catalog after that.
Want to know if AI assistants can actually find and recommend your store? Get a free AI-visibility read on your Shopify store at WRKNG Digital. We show you exactly what ChatGPT, Perplexity, and Google AI see when they look at your products.
