Product data shapes whether AI understands and recommends your products. Clear titles, descriptions, categories, variants and structured fields help shoppers find them. Start with your top ten products, improve the details and compare how AI presents them.
Operational Efficiency · Part 5 of 5
This one's probably the least glamorous of the five but it's one of the most important.
Your product data.
In a shop, products are displayed usually in some kind of logical order, on a shelf where customers are able to find them. In an AI answer your product data is that shelf. It's what the machine reads to work out what your product is, who it's for and whether or not to put it in front of them.
A great product with thin, messy data is just a great product shoved in a stockroom.
No one finds it.
Not a shopper, and definitely not a machine.
Earlier we went through the query fan-out, the way an AI breaks a shopper's question into smaller searches and assembles an answer out of whatever comes back. To do that it needs raw material - clear, structured facts about your products that it can read and trust.
Does the AI need your website?
Yes it does, but less than it used to. Shopify tells us that when an AI can get product information from a direct, live feed it'll take that over scraping a website every time, because a feed gives it current pricing, real inventory and accurate attributes with no guessing. We also covered earlier how Shopify hands the AI that feed automatically. So the question stops being "can the AI find my data" and becomes "is my data good enough once it gets there".
The pipe's connected.
What's flowing through it is up to you.
Turns out it's the same handful of things, over and over again.
Title, description, variants, images, price, category, so often half-filled. A two-line description and a clever name is fine for a human skimming on the tram, and nowhere near enough for a machine working out whether you match what someone asked for.
If the title says "Midnight Reverie" and nothing else, the AI has no idea it's a navy silk slip dress. Have the lovely name, and sit the literal description right next to it: "navy silk slip dress, midi length, adjustable straps". Poetry for the human, facts for the machine.
"Footwear" tells the AI almost nothing, where "women's waterproof leather ankle boots" tells it exactly when to recommend you.
The same jumper in five colours living as five separate products, and the machine may never work out they're the same jumper. Group them under one parent.
Buried in a tab, sitting inside an image, hidden behind some custom display logic. A human clicks the tab and finds it eventually. The AI may never see it at all, so to fix this, the real detail needs to live in proper structured fields.
This job's fully in your control and it pays back everywhere at once.
And what does clean data buy you? More than the AI channel. It helps your on-site search, your filtering, your ads, your feed to Google Shopping and your own team trying to find something late on a Friday afternoon. You're not doing a special new task for AI here. You're simply doing good housekeeping that happens to be exactly what AI needs.
AI is pretty good at helping here too. Paste your product page url into ChatGPT or Claude and ask what's missing, or what a shopper would still want to know before buying. Use AI to get your data ready for AI.
The trouble with this job is it sits in the same bucket as doing the accounts, and I tend to leave those to the last possible moment like everyone else.
Don't try to fix the whole catalogue.
Take your top ten products, the ones that make up the core of your revenue and read each one as if you know nothing about it. If you have questions, AI will have questions. Add the plain description next to the brand-voice version, tighten the category, metafields and check the variants are grouped properly. Then go and run the brand test again and see whether your tidied products show up better than they did before. That's your before and after.
You've got more of this in place than you think.
