Expect the gatekeepers of holiday shopping to include software, not just search engines or social feeds. Adobe forecasts AI-driven traffic to U.S. retail sites will jump 130% year over year this season — more than doubling — and its survey data suggests those AI-referred visits convert at higher rates and produce more revenue per session. For marketing and e-commerce teams, that shift changes where discovery happens and how attribution should work.

What Adobe measured and why the numbers matter

Adobe’s forecast identifies AI-attributable traffic by tracking shoppers’ link engagement. In a companion survey of 5,000 U.S. consumers, Adobe found AI-assisted shoppers engaged with brand offers more often and, in 43% of cases, generated higher revenue per visit than non-AI visitors. Visitors referred by AI tools also added items to carts at a 32% higher rate.

Those figures come with behavioral signals: more than three-quarters of AI-assisted shoppers reported feeling more confident in their purchases, and over two-thirds said they were less likely to return an item bought with an AI assistant’s help. Adobe contrasts that with data from roughly 18 months earlier, when non-AI traffic was worth 128% more than AI-referred traffic — a near-complete reversal that shows fast consumer adoption and improving AI utility.

Why brands need to treat AI as a discovery and referral channel

AI tools sit on top of the channels brands already use to reach shoppers. They can surface listings, summarize product features, and refer shoppers into a retailer’s site or checkout flow. Rebecca Wettemann, CEO and principal analyst at Valoir, put it plainly: brands need to ensure AI can find, accurately explain and differentiate their products. If AI systems can’t represent a product well, that item may be effectively invisible to a growing share of shoppers.

Bill Staikos, founder of Be Customer Led, framed the strategic challenge as one of dual audiences: the human shopper and the shopper’s AI agent. When an agent makes or narrows purchasing decisions, loyalty, personalization and conversion metrics become harder to interpret unless teams adapt measurement and experience design for agent-driven interactions.

Practical steps marketing and e‑commerce teams should take now

  • Make product data machine-ready. Prioritize complete, accurate product descriptions, technical specifications, consistent taxonomy and high-quality images so AI systems can surface and compare your items correctly.
  • Map AI touchpoints into attribution. Update analytics, UTM schemes and tagging to record referrals that originate from AI assistants and agent-driven flows so you can allocate budget and optimize performance.
  • Test for the AI user experience. Ensure product pages answer the specific questions agents rely on—clear specs, prominent shipping/return info and concise value signals—to preserve faster decision paths and higher conversion rates.
  • Track post-purchase signals. Adobe’s survey links AI help to greater purchase confidence and lower return intent; teams should monitor real return rates and customer-feedback trends to verify that effect.

What to watch this season

Adobe flags the arrival of “next-generation” consumer AI as a potential accelerant. Personal AI agents such as Meta’s Muse — which reportedly reached 2.8 million downloads in the first 12 days after launch — and Apple’s revamped Siri create more ways for agents to handle discovery and comparison outside traditional channels. Those changes raise two practical questions for retailers: will platforms standardize access to commerce signals, and how will intermediaries affect margins and customer relationships?

For many teams, the immediate work is straightforward: validate Adobe’s signals against your own traffic and conversion data, make product metadata and content AI-ready, and adapt measurement frameworks to identify and credit agent-driven referrals. The next wave of decisions will focus on whether to optimize directly for specific consumer agents or to build broadly discoverable product representations that work across multiple AI systems.

What to watch after that: whether AI platforms open commerce signals consistently, whether personal agents prefer certain merchants or inventories, and how attribution standards evolve. Those developments will determine whether this season’s surge becomes a durable shift in how customers find and buy products.