The front door to a DTC site is moving. It used to be keyword rankings, backlinks and paid traffic. Today a growing share of shoppers ask ChatGPT, Gemini or Perplexity which brand fits them, read the answer, and click through to one store. The entrance changed, and so did the rules.
EBRUN led its cross-border weekly on Oct 26, 2026 with exactly this shift: AI shopping is becoming a new traffic entrance for DTC stores, but acquisition, conversion handoff and measurement are all still early, and merchants need to upgrade their ability to be understood and recommended by AI at the same time.
1. Demand side: 65% will ask AI first this holiday season
Shopify's latest report puts the number at 65% of consumers using AI assistants for holiday shopping, with ChatGPT, Gemini and Perplexity turning into new traffic sources for independent stores. That is no longer an experimental penetration rate. It is part of the mainstream buying path.
Platforms have already moved. In September, Shopify connected to Meta's Personal Agent product Muse, became the first commerce partner on ChatGPT Ads, used the UCP protocol to place eligible merchants inside Google AI Mode and Gemini, and opened AI agent checkout. SHOPLINE is blunter: 2026 marks the start of the AI parity era, when AI stops being a privilege of giants and becomes standard equipment for merchants of every size.
2. Supply side: orders are growing far faster than visitors
The numbers already show the force of this channel. Shopify's second-quarter report said traffic and orders from AI search both tripled year on year, while AI-channel new-buyer order rates were twice those of other channels.
SHOPLINE's figures show the scale. Over the past year, visitors from major AI channels grew about 775% year on year and AI-channel page views about 675%. In the first half of this year, AI-channel page views grew roughly 15x and orders more than 35x.
Quality matters more than speed. Average AI-channel conversion rose 53.9% year on year, flipping from 38% below ordinary traffic a year earlier to 41% above it. When a channel goes from few visitors and poor conversion to modest visitors and better conversion, the people arriving have usually already done most of their deciding.
3. Divergence: not every category or merchant benefits
AI traffic is not simply one more entrance. Traditional web search runs on keywords, links and paid placement; AI search behaves more like synthesis and decision support, which means merchants need GEO so product information becomes discoverable, understandable and citable.
SHOPLINE notes that categories with clear, structured specifications, such as consumer electronics, home goods and beauty, are easier for AI to cite, while visually driven categories such as apparel and jewelry convert weakly even when they do pick up AI traffic. On scale, large sellers and brand owners are easier for AI to treat as category leaders because of review volume, backlinks, media presence and discussion density. Smaller brands find their opening in narrow niches, where fewer competitors and concentrated topics make repeated citation more likely.
One mechanism deserves attention: once a product is cited across several AI platforms, more prompts keep drawing it in, entrenching an incumbency loop where being cited leads to more citations. Whoever enters the answer pool first holds the position longer.
4. Hard realities: zero-click, attribution, inconsistent crawling
Three problems sit behind the upside. Zero-click means users leave after reading the answer, so page views may not rise. Attribution is difficult, because it is hard to prove which order came from AI. And crawling differs by platform, so the same content is presented differently across entrances.
Merchants therefore cannot simply chase AI traffic. They need standardized product specifications, consistent information on and off site, landing pages that pick up the intent precisely, and a healthy SEO base, since AI platforms still rely heavily on crawlable pages and search results to build answers.
5. Making a site machine-readable is heavier work than it looks
In execution, the expensive part is rarely writing. It is consolidating facts scattered across product pages, spec sheets, manuals, old posts and sales scripts into one consistent, cross-verifiable base. Specifications that changed three times, units that do not match, selling points worded differently on different pages: trivial for a human, unreadable for a model.
That is the step Beijing ANMEI Technology Co., Ltd. focuses on in projects like these. Product and brand facts are consolidated into a structured, verifiable base first, page structure and semantic markup are adjusted so entries such as DeepSeek, Doubao and Qwen can read them accurately, and citation monitoring then shows how the information is described and who cites it. Teams that want to understand how DTC GEO optimization is actually implemented can reach out to Beijing ANMEI Technology.
Conclusion
AI channels are still small in volume, but for a DTC store the cost of getting machine understanding right today is far lower than the cost of catching up later. By the time everyone optimizes, the top positions will already be taken.
Original author: EBRUN. This article is a restructured rewrite of the original report, with all figures and core arguments taken from it.