Why Product Data Is Becoming the Competitive Battleground in Modern Commerce

When customers ask AI, does your product show up in the recommendations?

If you sell products online - as a retailer, brand or manufacturer - AI is becoming a major product discovery channel, and product data increasingly determines whether AI recommends you or your competitor. More and more customers now ask ChatGPT, Gemini or Copilot what to buy before they ever visit a webshop. That quietly changes the most important question in commerce: not "do we rank at the top?" but "does the AI recommend us?" The reason is simple: AI can only recommend what it can understand,  and product data is what makes products understandable.

The contest shifts from rankings to recommendations

A search engine gave you ten links and the customer chose. AI-based discovery works differently: it forms an answer and often surfaces just one or two products. You're no longer competing for a ranking on a results page, you're competing for a place in the AI's recommendation. And if an AI can't understand your product, it can't recommend it.

AI is shaping the choice, not replacing checkout

Despite early expectations, most buying still doesn't happen inside AI interfaces. Customers research and compare with AI, but they still want to validate the product, seller and delivery terms before buying. For now, AI is influencing the choice, not replacing the checkout. The webshop is still where the sale closes, and AI is increasingly what sends customers there. So the priority today is discoverability: if AI doesn't recommend you, the customer never arrives.

Product data powers more than your webshop

Product data no longer serves only product pages and filters. It now powers search, personalization, marketplaces, supplier collaboration and AI-driven discovery alike. It also shapes how well products can be targeted, promoted and monetized in retail media programs. That makes product data a strategic capability rather than operational maintenance: when the same information feeds every channel, its quality and structure become a competitive lever, not a back-office detail.

What makes AI recommend you

Three things matter, and they're surprisingly down-to-earth.

Product data an AI can understand.
Clear, consistent information, structured attributes and machine-readable feeds are the language an AI uses to grasp what you sell. Messy or incomplete data stays blurry.

Context an AI can reason with.
Technical specifications alone rarely explain whether a product fits a real need. "Cordless drill, 18V" doesn't say it suits installers, works with common batteries, and is light for indoor work, but it isn't built for heavy concrete.

External trust signals an AI can validate.
AI leans on outside voices: reviews, comparisons, and discussions. In fact, much of what shapes an AI's recommendation comes from outside your own site, independent sources you don't control.

You don't own those external sources, but you can influence them, and you should watch what AI actually says about you today.

You used to help customers buy. Now you help AI answer customers' questions.

The supplier bottleneck behind your product data

For retailers and marketplaces, the hard part isn't only your own product data,  it's everyone else's. Suppliers send product information in different formats and wildly varying quality, often by spreadsheet and email. Without scalable supplier onboarding and enrichment, expanding the assortment stays slow, manual and expensive,  and that limits both growth and speed to market. It also affects retail media, where poor supplier data limits campaign targeting and monetization. The richer and more AI-ready you want your catalog to be, the more it depends on getting good data in from your suppliers in the first place.

The same applies to B2B and manufacturers

This isn't limited to consumer retail. B2B buyers research and compare with AI just as consumers do, often in Copilot, in the flow of work,  before they get in touch or place an order. And for a manufacturer that sells through dealers or direct to consumers (D2C), product data quality counts twice: it has to be found and understood both in AI and across the dealers' own channels. The more complex the product, spare parts, technical attributes, and compatibility,  the more structured, rich product data pays off.

What to do now

• Make product data clean, structured and machine-readable, and distribute it through the channels AI increasingly relies on,  Google, merchant feeds and structured web content. This emerging practice is often called Answer Engine Optimization (AEO) or Generative Engine Optimization (GEO).
• Enrich product information with use cases, compatibility and language versions.
• Scale supplier onboarding and enrichment so assortment growth doesn't stall.
• Monitor AI visibility and the external trust signals that shape it.

Where this is heading

Better product data improves not only AI visibility, but also conversion, assortment scalability, supplier collaboration and monetization. This is where many companies need practical support,  from product data enrichment and feed management to bringing supplier data in at scale and measuring AI visibility across channels. At Solteq we see this becoming a growing part of modern commerce enablement, and we help companies build it.

A practical first step is an AI-visibility assessment: understanding whether AI recommends your products today, and why.