Artificial intelligence is shaping how consumers search for, discover, and buy products online. Instead of opening a retailer’s website, selecting a category, and filtering through hundreds of products, consumers can tell an AI system – or even a traditional search engine – what they want: a black wool coat under $1,000, a handbag that will fit a 13-inch laptop, running shoes for marathon training, or a vintage Prada skirt from the late 1990s.
With the help of AI, search and shopping tools can translate a consumer’s preferences into highly specific queries and find and recommend products that appear to match. But the effectiveness of these tools depends on a longstanding feature of online shopping: the quality of the underlying product information. That constraint is not new – what AI changes is how precisely consumers can express what they want, and how much they can rely on the system to determine which products actually meet those criteria.
The Product Data Problem
For all the attention being paid to AI-powered shopping assistants, recommendation engines, and autonomous agents, their outputs still depend on the information available about the products they are searching. Online product listings may lack detailed information about construction, materials, dimensions, fit, provenance, compatibility, and other characteristics that can determine whether a product actually satisfies a consumer’s request. And even when that information is provided, it may be wrong.
Authenticity offers a clear example. A consumer might ask an AI shopping tool to find an authentic Hermès Birkin in a particular size, color, material, and price range. The system may understand exactly what the consumer wants and identify listings that appear to match. But if a seller has incorrectly described a counterfeit bag as authentic, whether the system produces a reliable result may depend on whether it has other information with which to evaluate that claim.
The problem extends to other product details, from dimensions and materials to country of origin and age, and can be particularly acute in the resale market, where listings may rely heavily on seller-supplied information. Product names may be wrong, dates and seasons estimated, measurements taken differently, and details about condition, repairs, or modifications incomplete or missing.
Ultimately, AI may make it easier to find listings that appear to satisfy a highly specific request, but more sophisticated search capabilities do not necessarily produce better results. As consumers rely more heavily on AI-powered platforms to select, compare, and potentially purchase products, the quality of the underlying product information becomes more consequential.
From SEO to Product Data
This may change how brands and retailers think about online product information. Retailers have long structured product information for ecommerce platforms and search engines. AI-driven discovery raises the stakes, however, by enabling consumers to search based on increasingly specific product attributes. That could mean supplying more structured information about products, from materials and measurements to care requirements, manufacturing information, certifications, and other attributes. It could also mean working to keep product descriptions consistent across a brand’s own website, authorized retailers, marketplaces, feeds, and other digital channels.
That becomes more difficult as products move beyond channels a brand controls. Third-party marketplaces, resale platforms, and individual sellers may describe the same product differently, relying on their own information or data gathered from elsewhere. Brands may have little or no control over those descriptions, leaving AI systems to reconcile competing accounts of the same product.
THE BOTTOM LINE: Better AI, alone, will not make online shopping more accurate. Brands, retailers, and platforms will also need better product data – more detailed information about what they are selling, greater consistency across channels, and reliable ways to identify when that information is wrong.
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