AEO, trust signals, and digital assistants: Adapting fashion brands to the new rules of AI shopping

AI commerce is quickly becoming a norm in fashion. As consumers increasingly turn to AI assistants for product discovery and recommendations, brands are being encouraged to rethink their approach to digital shopping journeys. Traditional search and e-commerce funnels are shifting towards AI-led experiences that prioritise speed, relevance and trust.

According to 2025 research by Adobe Express, 60 percent of 5,000 surveyed consumers said they used AI as part of their shopping journeys. Retail visits driven by AI grew 4,700 percent year-over-year between last year and 2024. The data points to an unprecedented speed of adoption driven by the growing scale of AI platforms themselves.

“In just one year, agentic commerce has gone from something most consumers had never heard of to something used by roughly half of GenAI users – representing about 16 percent of Americans,” Kimberly Shenk, CEO of AI shopping optimisation platform Novi, told FashionUnited in an interview. “In the 30-year history of e-commerce, we’ve never seen a new shopping behaviour adopted this quickly, or with this much innovation compressed into such a short window.”

AI assistants dismantle ‘messy middle’ of commerce, condensing shopping journeys

According to Shenk, AI shopping dismantles what she calls the “messy middle”, condensing shopping into a single step. Rather than opening multiple tabs to compare products, reviews and prices, AI assistants are able to do all this research on the consumer’s behalf. “That efficiency has fundamentally changed shopping behaviour,” she continued. “Instead of keyword searching, consumers are asking conversational, highly specific questions – and trusting the answer they receive. As a result, AI assistants are increasingly becoming the first stop for product discovery, not just a supplementary tool.”

As shopping moves in this direction, brands must adapt to a new set of rules. One of the biggest shifts is the move from SEO to AEO, or Answer Engine Optimisation. “SEO was about helping search engines find your content. AEO is about helping AI systems understand your information well enough to explain it accurately in their own words,” Shenk explained. “That shift is massive. You are no longer optimising for keywords. You are optimising for answers.”

For fashion brands, this means placing more emphasis on SKU-level accuracy and structured product data. “AI assistants recommend individual products, not narratives,” she said. “Products with verified, structured SKU data are over 60 times more likely to be selected in ChatGPT than those relying on marketing claims or promotional language.”

Verified trust signals drive forward visibility

Trust signals are also becoming increasingly important. While traditional e-commerce relied heavily on promotions, paid placements and storytelling, AI-led shopping platforms instead prioritise credibility and verification. “Certifications and badges outperform promotional tags by 10 to 58 times, and sponsored tags can actually reduce selection rates,” Shenk noted. “When brands pair visible badges with clear, explanatory text, selection rates increase seven times compared to text alone.”

According to data cited by Novi, products with verified trust signals are selected by AI models between 230 and 259 percent more often than random choice, while products without such signals are selected between 86 and 96 percent less often. In addition, not all signals perform equally. Named certifications such as Oeko-Tex, FSC and Made Safe typically outperform generic claims, as do retailer-backed programmes that add further layers of verification. Products that also then combine visible badges with explanatory text are selected more than those relying on text alone, underlining the importance of credible information in AI commerce.

Retailers vie to meet consumers in AI-led environments

The shift towards AI-driven shopping is already being reflected at industry-level. Retailers such as JD Sports and Walmart have begun investing in AI-based commerce partnerships designed to intertwine discovery and transaction. JD Sports’ investment into AI Commerce Systems (ACS), for example, aims to make its product catalogue easier to find and possible to buy directly via AI platforms. This intends to both protect brand visibility in the wake of declining traditional search formats, and to meet consumers directly in AI-led environments.

Similar evolution is defining the future of commerce at a platform level. At NRF earlier this year, Shopify unveiled its Universal Commerce Protocol (UCP), an open standard co-developed with Google and endorsed by over 20 retailers, allowing AI agents on platforms like OpenAI to connect with merchants and handle transactions. Shopify also launched its native checkout in Google AI and the Gemini app, and opened its catalogue to non-Shopify merchants, allowing them to sell across AI channels via its own infrastructure.

The debate of overconsumption

While dismantling entry barriers by turning AI assistants into points of sale, the shift towards this style of commerce is raising questions about whether change in this area could encourage overconsumption among consumers, particularly in a market that is already overly saturated. Shenk believes this is not the case. “Consumers no longer see a long list of search results. They get one synthesised answer,” she said. “AI platforms typically surface only three to eight product recommendations, and those recommendations are confident and authoritative.”

In that perspective, AI commerce then narrows choice, rather than expanding it. “If your product data is not clear, consistent and citable enough for the AI to use, your product simply does not exist at that moment,” she added. “Visibility in AI is earned through clarity and credibility, not volume.”

As such, with AI shopping environments becoming more selective, brands with verified claims and detailed product attributes are already well ahead. According to Shenk, these include large heritage brands tied to recognised certifications, as well as companies that have invested in detailed product metadata and personalisation over a long period of time. “Across both groups, the common denominator is a mindset shift,” she said. “These brands treat product data as infrastructure, not copy.”

Of course, keeping up with this fast-moving space remains a challenge, particularly as AI models evolve quickly and change how recommendations are generated. Shenk warns that brands can lose AI visibility without knowing it, particularly as models continuously evolve and signals differ over time. Brands must therefore ensure their product data is consistent across every touchpoint.

“AI models make inferences based on the totality of a product’s digital footprint,” she explained. “When your product data is clear, verified and repeated across the ecosystem, models gain confidence. That confidence is what ultimately gets a product chosen.” As such, in an increasingly agent-driven retail landscape, that confidence may prove to be one of fashion’s most valuable assets.