Small and midsize e-commerce brands face growing challenges as AI-driven discovery, social commerce, and conversational search change how shoppers find products.
Traditional search is giving way to AI-driven recommendations, social-first browsing, and increasingly visual, conversational product discovery. AI and social commerce are rewriting the rules of e-commerce, affecting visibility, rankings, and ultimately revenue.
According to Adobe Analytics, traffic from AI sources to U.S. retail websites grew 393% year over year during the first quarter of 2026. By March, shoppers arriving via AI referrals were converting 42% better than visitors from traditional digital channels, highlighting the growing influence of AI-assisted product discovery.
As AI-driven answer engine optimization (AEO) changes product search, SMB brands should rethink their product information management (PIM) and metadata so AI models can accurately recommend their products. Unlike traditional SEO, which emphasizes keywords, AEO depends on structured, descriptive product data that AI can understand.
“AI discovery tools don’t crawl for search terms. They pull structured meaning out of your product data and hand it to a shopper as an answer,” Hilary Smith, CMO of connected commerce operations platform Linnworks, told the E-Commerce Times.
“If your material composition, use case, compatibility, and sizing are buried in a spec table nobody parses, the model cannot surface them,” she added.
How AI Is Changing Product Discovery
Today’s conversational AI models and interactive search engines aggregate product data to answer queries directly. If a consumer asks about the best eco-friendly hiking boot for wide feet under $150, the AI often provides a definitive recommendation. Marketers risk losing direct site visits as AI-generated answers increasingly become the first stop in the shopping journey, making optimized product data more important than ever.
The discovery, consideration, and conversion stages have merged into a single moment inside social feeds. Consumers no longer find a product on social media and go to Google or an e-commerce storefront to buy it. They check out in-app.
Marketers who cannot maintain real-time inventory and pricing consistency across these highly volatile channels are rapidly losing visibility.
According to Smith, that means enriched, attribute-dense product records. Clean categorization, consistent naming, and sentence-level descriptions help AI understand shopper intent rather than simply describing the product.
“Brands that have always treated PIM as data housekeeping rather than content strategy are the ones most exposed right now,” she observed. “It’s not enough to fix this on your website.”
She explained that the average mid-market retailer now sells across four or more channels. If a brand enriches its product data centrally but fails to distribute it consistently across those channels, the effort accomplishes little.
“An AI model pulling from your Amazon or Walmart Marketplace feed will surface whatever’s actually sitting in that feed, thin listing and all,” Smith said.
AI Discovery Breaks the Traditional Customer Journey
AI-powered search engines now answer shoppers’ product questions directly. They can complete the purchase in-app without sending traffic back to the merchant.
That breaks the feedback loop most retailers have built their operations around, Smith noted. When someone buys based on an AI recommendation, retailers lose session data, the click path, and the attribution touchpoint.
More concerning, only 37% of U.S. mid-market retailers rate their cross-channel, cross-warehouse inventory visibility as excellent, Smith noted. Inventory visibility was already a weak spot.
“Now imagine a single AI recommendation triggering a demand spike across three channels at once, and you’re making replenishment calls with partial visibility,” she added.
Smith suggested the retailers riding this out cleanly are not the ones who saw AI discovery coming. They are the ones who have already built centralized, real-time inventory because they had to solve the complexity of multichannel operations for other reasons.
Retailers Need New Ways to Measure Performance
Smith agreed that sales attribution has been eroding for years due to cookie deprecation, dark social, and app journeys that never touch the open web.
“AI-mediated discovery just accelerates it. What’s changing is where operationally mature retailers look instead,” she said. “They are moving off last-click attribution and onto operational metrics.”
The new metrics include sell-through by channel, inventory turnover, fulfillment accuracy, customer retention, and net margin by SKU. Those are less exciting but more honest.
“They tell you whether demand actually converted and whether your operations kept up, regardless of where the discovery happened,” Smith said.
Social Virality Can Sell Out Products in Hours
Retailers must adapt their operations when demand is driven by discovery rather than seasonal demand. That viral demand is just the sharpest version of a problem multichannel retailers already face. Stock moves in unexpected volumes from unexpected places.
For example, TikTok compresses the sales timeline to almost nothing. It can blow past anything a seasonal model would have predicted. Retailers need the infrastructure in place before demand spikes, Smith advised.
Real-time inventory must sync across every channel, so a sale anywhere updates stock everywhere immediately. Also, automated rules are needed to throttle a listing or trigger a reorder without requiring someone to notice and act manually within a 5-minute window.
According to Smith, 64.8% of U.K. retailers and 60% of U.S. retailers already describe their operations as mostly or highly automated. Automation is now the baseline, not the differentiator.
However, much of that automation only covers routine tasks. The edge cases — with viral moments among the most demanding — are still handled manually at most brands.
“The ones who come through a viral spike without stockouts or oversells are the ones who built exception handling into the automation itself instead of escalating those decisions to a human,” she reasoned.
Backend Automation Keeps Orders Flowing
Discovery and checkout now happen in the same social feed, increasing the risk of inventory synchronization lag and complicating order routing.
According to Smith, the fix is a centralized order management layer that ingests orders from these checkouts through the API and treats them exactly like any other channel order — routing, fulfillment, inventory update, no exceptions. It is not glamorous, but it is the difference between the channel being a growth driver or a liability.
Linnworks recently rolled out Spotlight AI to surface gaps in backend automation. It analyzes marketplace health scores using backend operational metrics, including fulfillment speed, order defect rate, cancellation rate, return rate, and late shipment rate.
“Amazon, eBay, and Walmart all use some version of this to decide who gets algorithm visibility and who gets suppressed,” Smith said.
Spotlight AI finds the automation gaps that create algorithm drag by identifying manual handoffs that introduce errors, delays, and inconsistencies. Fix those, and the metrics marketplace algorithms start moving in the right direction.
These capabilities include automated carrier selection optimized for fast-moving SKUs, routing rules that meet the platform’s shipping window, and real-time inventory sync that prevents oversells from turning into cancellations, which is one of the most damaging line items for health scores.
Smith clarified that the retailers seeing real improvement here are not approaching it as a marketing problem. Rather, they track fulfillment accuracy, late-shipment rate, and cancellation rate as core key performance indicators (KPIs) and use automation to reduce those numbers.
“The health score improvement is the byproduct, not the target,” Smith concluded.
