By Steve Merrill, Founder of WRKNG Digital | August 26, 2026
The product feed fields that decide whether AI recommends you are the ones a shopper would name in a question: title, description, product type, brand, GTIN, price, availability, material, color, and age group. Fill them all and you become matchable.
We audited 2,400 products this year. Only 11% had the structured data needed for AI to recommend them. These ten fields are where most of that gap lives.
1. Title
Your title is the first thing AI reads, so it needs to name the product, the use, and the key attribute. "Men's Waterproof Winter Jacket" beats "Blue Jacket" every time. Specific titles win specific queries.
2. Description
A real description gives AI the details to match nuanced questions about fit, use, and materials. Two vague sentences won't do it. Write like a good salesperson answering a shopper.
3. Product type / category
Accurate categorization tells AI where your product fits so it surfaces for the right intent. Google's product data spec covers category best practices. Miscategorized products get shown to the wrong shoppers or nobody.
4. Brand
Brand is a trust and disambiguation signal that helps AI connect your product to known entities. Leave it blank and AI has one less reason to trust and place your product.
5. GTIN / MPN
Global identifiers let AI match your exact product across sources and confirm it's real. Products with valid GTINs are easier for AI systems to verify and recommend with confidence.
6. Price
Accurate price is what makes your offer usable in AI shopping results and comparisons. I've seen Product schema with a blank price field. AI can't recommend a product it can't price.
7. Availability
Real-time stock status keeps you eligible when a shopper is ready to buy. Stale availability gets products dropped. AI won't recommend what it thinks is out of stock.
8. Material / attributes
Material and detailed attributes answer the specific questions that decide a purchase. "Recycled nylon" or "14k gold" is exactly what a filtered AI query looks for.
9. Color
Color is one of the most common shopper filters, so a filled color field expands your matches. Standard color naming helps AI map your product to how people actually search.
10. Gender and age group
These fields target the right audience and keep you out of mismatched recommendations. A "men's" or "kids" tag is the difference between a relevant match and a wasted one.
How We Chose These Fields
We ranked the fields by how often a missing value blocked an AI match in our audits, cross-checked against Google's product data specification. Every field here maps to a real buyer question.
Frequently Asked Questions
Which feed field matters most for AI recommendations?
Title and description carry the most weight because they answer the most buyer questions, but AI matching depends on the full set. A strong title with blank material or availability still loses filtered queries.
Do I need GTINs for every product?
Where they exist, yes, because global identifiers help AI verify and match your exact product. For custom or handmade items without GTINs, focus on rich titles, attributes, and MPNs.
How many products should I fix first?
Start with your top 20% by revenue. That's where completing feed fields returns the most AI visibility per hour of work.
Will fixing feed fields help my Google Shopping too?
Yes. The same complete, accurate feed data that helps AI recommendations also strengthens Google Shopping and Merchant listings. One cleanup helps multiple channels.
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.
