Why Clean Product Data Will Decide Which Brands Win Online in 2026.
By Christian Endresen <> January 2026.
Online shopping is changing fast. Not because of better websites, but because AI is starting to shop for people.
In 2026, tools from companies like Shopify and Google are making it possible for AI assistants to search for products, compare options, and even complete purchases automatically.
Specifically, Shopify and Google just launched the Universal Commerce Protocol. Which is a standard for AI agents to discover products and complete purchases.
Think: people buying your products directly in search and Chat GPT.
This new setup is called agent-driven commerce. And it comes with one big requirement:
Your product data has to make sense.
What Changed Recently and Why It Matters
Until now, most online stores were built mainly for humans. People browsed categories, read descriptions, and figured things out along the way.
AI works differently. It asks direct questions and expects direct answers. It does not guess, interpret, or scroll through content.
If your product data cannot clearly answer questions like who the product is for, how it fits, what it’s made of, or whether it’s suitable for a specific use, AI will move on to another brand.
Why Product Data Now Matters So Much More
AI assistants do not care how your website looks. They care about whether your product information is consistent and reliable.
If one product is labelled “boots,” another “ankle boots,” and a third “footwear,” an AI cannot easily understand how they relate. If sizing, materials, or age suitability are hidden in product titles or images, they are effectively invisible.
This means that unclear product data does not just create internal problems anymore. It directly affects whether your products are even considered.
Your data needs to be very well structured.
The Risk Brands Often Miss
Many brands assume this is a future problem. It is not.
As AI-driven shopping grows, brands with unclear or inconsistent product data will quietly disappear from recommendations. Customers won’t actively choose another brand. The AI will.
That makes product data a visibility issue, not just an operational one.
What “Good” Product Data Looks Like in Practice
Good product data is not about complexity. It is about clarity.
This usually includes:
Consistent product types across your catalogue
Clear attributes such as size, color, material, and intended user
Categories that are logical and consistent
Processes that keep data clean as new products are added
This work happens behind the scenes, but its impact is external.
A Shift in Mindset
Product data used to be something brands managed internally to keep systems running.
Today, it shapes how AI understands your products. It influences which items are recommended, compared, or excluded entirely.
Being easy to find is no longer enough. Your products must be easy to understand.
Conclusion on UCP
The tools for AI-driven commerce already exist. The standards are being rolled out now.
The brands that succeed in 2026 will not be the ones chasing every new technology. They will be the ones who invested early in clear, structured product data.
Because when AI starts making buying decisions, clarity is what gets you chosen.
Why Better Product Data Improves Returns and Exchanges
Clean product data doesn’t just matter before purchase. It also makes returns and exchanges easier and more effective.
When customers exchange an item, they’re looking for similar products that match size, fit, or style. If your product data is well structured, relevant alternatives can be suggested instantly, making exchanges fast and self-serve.
Poor data leads to irrelevant suggestions and more refunds instead of exchanges.
Better product data helps brands recommend the right alternatives, retain revenue, and keep customers engaged even after a return.