By Steve Merrill, Founder of WRKNG Digital | August 29, 2026
When AI skips your store, it's rarely the product. It's the data. The machine reads a page full of blanks and moves to a competitor it can actually understand.
I've run this audit on more than 40 Shopify stores. The same gaps show up again and again. None of them are hard to fix once you see them.
Here are the gaps that keep you out of the answer.
Which product data gap hurts the most?
Empty attribute fields. Material, gender, age group, color, and size left blank. Each blank is a shopper question you can't answer, so you can't match a query that includes that constraint.
Fill them with real values. The Google Merchant product data specification lists exactly which fields matter and how to format them.
Here's the bottom line: a blank field is a lost query.
What about thin descriptions?
A two-sentence description tells the model almost nothing. "Blue Jacket" answers no questions. AI needs the specifics a salesperson would give: what it does, who it's for, what it's made of, when to use it.
Write the description a shopper would need if they couldn't see the photos. That's the version AI reads.
Is missing schema really a gap?
Yes, and empty schema is worse than none because it looks fine. Most themes output a Product schema shell with no price and no availability inside. Valid structure, zero facts.
Run your pages through the Schema.org validator and confirm the values are present. Fill name, description, brand, offers, price, and availability with real data.
Does inaccurate data count as a gap?
It's the most dangerous kind. A stale price or wrong stock status teaches AI not to trust you. Once a model gets burned on your accuracy, it stops surfacing you even when the rest of your data is good.
Sync price and availability everywhere they appear: page, feed, and schema. Agreement builds trust. Contradiction destroys it.
How do you find your own gaps?
Test five real buyer questions in ChatGPT and Perplexity. Where you don't appear, look at what the query asked for and check whether that fact exists in your data. The missing fact is your gap.
Fix it, wait for a recrawl, and re-run the same questions. The gaps close in the order you fix them.
That gap map is exactly what a WRKNG audit produces for a store.
Further reading
Frequently Asked Questions
What's the single most common data gap?
Empty attribute fields like material, size, color, and age group. They're easy to leave blank and they quietly cost you every query that includes those constraints.
Does empty schema hurt more than no schema?
It can, because it passes a structure check while carrying no facts. A schema block with no price or availability gives AI nothing to quote, yet looks complete to a quick review.
How do I know which gap to fix first?
Test real buyer questions and see where you drop out. The constraint the query asked for that your data doesn't state is your highest-value gap. Fix in that order.
Will fixing data gaps help my Google Shopping too?
Yes. Complete, accurate feed data and schema improve Google Shopping, traditional search, and AI recommendations at once. It's the same underlying data serving all three.
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.
