AI-assisted shopping is changing how shoppers discover products. Instead of sorting through pages of search results, shoppers can ask conversational questions and receive a smaller set of recommendations. For marketers, the immediate question is not whether an AI assistant can replace every part of product research. It is whether the product information available to those systems is complete, consistent and clear enough to be understood.

SPS Commerce reports that AI-referred traffic to retail sites increased more than 1,300% year over year during the 2024 holiday season. That figure measures referral traffic, not sales, conversion rate or recommendation quality. Still, it points to a growing role for conversational tools in product research and product discovery.

AI shopping tools are narrowing the product discovery funnel

Traditional ecommerce discovery usually begins with keywords, product result pages and manual comparison. Conversational AI changes that workflow by interpreting a shopper’s request, comparing available attributes and returning a shorter set of options. SPS Commerce says recommendation engines and AI shopping assistants can influence which products shoppers see first and which products never enter consideration.

The product information behind a listing is central to that process. A shopper asking whether a backpack works as lightweight carry-on luggage for a short business trip may expect the assistant to interpret dimensions, weight, materials, intended use, compatibility and category relationships. If those details are missing or inconsistent, the system has less evidence to connect the product to the shopper’s intent.

That creates a different visibility challenge from traditional keyword optimization. SPS Commerce notes that weaker listings could sometimes still gain visibility when advertising spend, search history or brand momentum carried them. In conversational discovery, the recommendation pool is smaller, which increases the importance of structured, interpretable product information.

The supported takeaway is specific rather than sweeping: complete product data can improve the likelihood that an AI-assisted system can evaluate and surface a product. It does not prove that a lesser-known product will outrank a larger brand, or that an AI system will invent brand preference where none exists. The immediate work is making sure products are legible to systems that are comparing them on behalf of shoppers.

PayPal’s guidance on AI product discovery makes a related point. It describes AI assistants as a discovery layer between merchants and customers and says clear product information, external validation and trust signals can influence what gets recommended. PayPal also notes that a product that is not legible to AI may not appear in these conversations.

Structured product data gives AI systems more evidence to compare

Structured product information helps systems compare products consistently across large catalogs. SPS Commerce identifies dimensions, materials, compatibility details, certifications, sizing specifications and category alignment as inputs that can affect how confidently an AI system evaluates, filters and surfaces products.

Product data also moves through multiple systems. A supplier may maintain information in an enterprise resource planning system, distribute it to retailer portals, syndicate it to ecommerce channels and manage marketplace listings separately. When specifications or attributes differ across those environments, systems have a harder time interpreting products reliably.

SPS Commerce describes product onboarding, taxonomy consistency, catalog governance and trading-partner coordination as operational factors that influence how effectively information moves through retail ecosystems. Product information management systems can help centralize that work, but the underlying requirement is simpler: product data must be accurate, organized and consistent wherever it appears.

Smaller marketers face both opportunity and discoverability risk

AI-assisted discovery gives merchants another route to reach shoppers during the research stage. PayPal says shoppers can use AI assistants to find, research, compare and sometimes purchase products through conversational interactions. A clearly described product with complete specifications, transparent pricing and current availability is easier for an assistant to evaluate than a listing that leaves key questions unanswered.

The same shift creates risk when a listing is incomplete. SPS Commerce says products lacking sufficient supporting information may never enter the consideration set. PayPal similarly warns that vague messaging, hidden costs, unclear pricing and outdated stock information can create uncertainty that makes an AI assistant less likely to surface a product.

Trust signals matter as well. PayPal says reviews, ratings and third-party mentions can help AI determine whether a product is reliable and worth recommending, especially when the brand is unfamiliar. That makes accurate product information and credible external validation relevant not only to visibility, but also to how the product is assessed after it appears in a recommendation.

Marketers should treat AI visibility as catalog discipline

The evidence points to catalog discipline rather than a separate, speculative AI tactic. These steps reflect the product-data practices discussed by SPS Commerce and PayPal:

  • Complete core attributes. Maintain dimensions, materials, certifications, sizing, compatibility details and category information so systems have the information needed to compare products.
  • Clarify product context. Explain who the product is for, when it is useful and what problem it solves. Clear context helps conversational systems interpret shopper intent.
  • Keep taxonomy consistent. Use consistent categories and metadata across retailer, marketplace and ecommerce environments where product information is distributed.
  • Maintain accurate pricing and availability. PayPal notes that unclear pricing, hidden costs and outdated inventory information can create uncertainty during recommendation and purchase decisions.
  • Strengthen credible proof. Reviews, ratings and third-party mentions can provide trust signals that help an AI system assess an unfamiliar product.
  • Assign ownership for product information. SPS Commerce emphasizes governance, onboarding and syndication workflows as part of maintaining consistent information across channels.
  • Keep owned acquisition channels active. Email, retailer relationships and customer referrals can complement AI discovery without depending entirely on an assistant’s recommendation logic.

AI-referred traffic, listing accuracy and recommendation testing are the next indicators to watch

Merchants can monitor whether AI-referred traffic becomes a meaningful source of visits for their own sites and whether product listings remain accurate and consistent across the systems where they are published. The reported 1,300% holiday-season increase is one signal of changing behavior, but it does not prove that the growth will persist outside peak shopping periods.

It is also reasonable for marketers to test how product information, reviews, pricing and availability affect how their products appear in AI-assisted shopping experiences. This is a measurement approach, not a finding established by the cited sources. Repeated checks using comparable customer questions can help a business identify listing gaps and inconsistencies that need attention.

The evidence supports a narrow conclusion: AI assistants are becoming part of product discovery, and structured, accurate and consistent product information can affect whether products are surfaced and considered. The evidence does not establish a reproducible test in which a fabricated brand outperformed real products. For marketers, the immediate task is practical: make product data complete, keep it consistent across channels and provide clear information that helps both shoppers and systems understand the product.