The rise of agentic AI in e-commerce is already reshaping how fashion brands are discovered and perceived, as chatbots increasingly take out the guesswork for shoppers.
With the slow death of the search bar, brands can no longer rely on SEO to help how they rank on a search results page, as every interaction, from discovery to purchase, is increasingly filtered through algorithms, making brand story a strategic asset.
The shift reflects “a generational change” in consumer behavior, said John Harmon, senior retail and tech analyst at Coresight Research.
Shoppers are no longer looking to search engines that return lists of links to click, but instead turning to conversations with AI platforms that synthesize information, give the user an explanation of trade-offs and weigh the pros and cons of each recommendation.
Whoever controls the AI agent, the data and the training decides how the brand is seen when a bot, not a person, is doing the buying.
That shift places an immediate premium on detailed product data, from dimensions and materials to sourcing and sustainability attributes, raising the risk that smaller brands will fall behind unless they can meet a new, higher bar in order to be read and understood by chatbots.
“Consumers have really gravitated to these AI chatbots,” Harmon said. High school and college students in particular bypass Google and often start product searches directly in ChatGPT, he noted.
Coresight’s December survey found that around 13 percent of consumers had already used a chatbot to support shopping, even though only a small share completed transactions inside those platforms. That gap highlights both the friction and the opportunity for brands.
Friction at the transaction stage can hurt conversion rates, while the initial burden of feeding AI systems with high-quality data is likely to favor larger, tech-savvy brands with mighty budgets.
Most chatbots cannot yet complete a purchase, directing consumers back to retailers’ own websites for payment, shipping and returns.
“Even if you check out with ChatGPT, the retailer is handling payment and shipping and status and returns,” Harmon said. At the moment, “it’s really just a window into the retailer’s website.”
That window, however, is closing fast. Google’s launch of Gemini Enterprise for Customer Experience in January showed just how quickly AI systems are evolving from search assistants into autonomous agents that can reason, plan and act under human supervision. Unlike traditional chatbots, Gemini agents are designed to handle text, voice and images, proactively build shopping carts, execute pre-approved purchases and manage the full customer lifecycle, from discovery through post-purchase support.
ChatGPT, meanwhile, was closing in on 900 million users by mid-December 2025, with Google’s Gemini following at roughly 650 million, according to a Bernstein report. The scale of those audiences coincides with early commercial experiments, including ChatGPT’s rollout of instant checkout features through partnerships with Etsy, Walmart and select Shopify merchants, enabling third parties to build shopping apps directly within the AI interface.
Currently retail usage is still a small slice of overall activity — OpenAI estimates that around 2 percent of ChatGPT interactions relate to purchasing products — but Bernstein identified it as the fastest-growing category of search on the platform. Analysts believe 2026 will be an “inflection point” for agentic AI.
For fashion brands, the risk is huge. Years of brand building and storytelling built through runway shows, retail environments, and carefully controlled imagery risk being lost.
Evelyn Mora, founder of retail intelligence consultancy Vlge, frames the challenge through the lens of data. Fashion has historically relied on “embodied data” — real people generating insights through physical experiences, store interactions, fittings and cultural context. AI systems, by contrast, operate on “disembodied data,” that is synthetic, coded representations that must be trained deliberately.
“Building and training AI agents is like building and training your team,” she said.
In an agent-led commerce environment, the interface itself becomes automated. A consumer might type a simple request such as, “I’m looking for a rain jacket,” for example, and their personal shopping agent, armed with sizing, preferences and budget constraints, will do the rest. That agent then interacts directly with brand-specific agents that represent labels like Chanel, Gucci or Saint Laurent, each trained to sell on the brand’s behalf.
“This is where the brand’s agent comes in, so it is essential that brands train their agents to sell to you,” Mora said. “Brands like Chanel will have to have an AI agent that knows everything about Chanel, all the products and everything to do that sales work from one personalized agent to a different personalized agent.”
Training those agents will be both expensive and complex. Feeding historical archives into AI systems — digitizing patterns, silhouettes, fabrics, campaigns and craftsmanship — requires years of work and budgets that can stretch into the hundreds of millions for heritage houses. Yet historical data alone is not enough.
“It’s like food to people. The more we eat, the more we grow,” Mora said. “We have to keep feeding our AI agents data for them to become smarter.”
That creates a competitive dynamic where agents effectively become intellectual property. Brand agents will compete not just on price or availability, but on the depth and quality of their knowledge — including awareness of competitors’ collections and even secondhand markets.
“The agents will ultimately be IPs of the brands,” she said. “It’s a competition of who has a smarter sales team, who has the best quality data, who has the best data pipelines.”
Mora’s “HauteTech” framework, which she will launch on Feb. 25, treats all that info as a “data atelier” rather than a factory process. The emphasis is on rarity and quality, not scale alone, mirroring how luxury brands design garments rather than mass-produce them. Brands will increasingly trademark their AI agents, along with the methods used to train them, she asserted.
“That’s the level of granularity the AI agent of a brand should have,” she said. “The success and the way these agents are trained are going to define directly the revenues and engagement of people.”
Large brands are moving quickly. L’Oréal, for example, launched its Beauty Genius AI assistant in 2024 after years of digitizing historical data. Mora said many major fashion houses are already far along with similar efforts.
“It’s not something that it’s going to come in five years — no, it’s coming literally in the next months,” she said. “There are a lot of brands today that are already building their agents and training them.”
At the same time, the infrastructure underpinning agentic commerce is becoming increasingly centralized. According to Luca Morena and Joanna Damaszko of trend forecasting firm Nextatlas, most smaller brands will not own the underlying technology powering AI systems — and shouldn’t try to.
“It’s not about the tech because, of course, we all know that it is a commodity,” said Morena, chief executive officer of Nextatlas. “There’s a lot of things that will not be owned by brands, apart from the intimacy with the client base. That’s why we are talking about reinforcing storytelling and communicating brand values. Those can remain powerful even in an AI-driven shopping ecosystem, because the consumer will have to have in mind what kind of brand they want to shop from before they even begin their search.”
Nextatlas has been tracking what it calls “synthetic intimacy” since 2024 — the idea that consumers are using AI systems not only for efficiency, but as private “safe spaces.”
People use it to write personal messages, share confidential information and rehearse conversations.
“It’s not only about facilitating transactions or making us more efficient,” said Damaszko, senior insights manager at Nextatlas. “It is also about creating this safe space where we play, where we talk, where we put something on the test, because it feels private.”
That intimacy introduces both opportunity and risk for fashion brands. As discovery becomes conversational, labels risk being flattened into generic descriptors unless their identities are deeply encoded.
“What does it mean for a brand in the future when your whole identity is reduced to a single line in a conversation?” said Damaszko. “Are you losing your customer journey that you’ve been working on for so many years when you are reduced to one click?”
Rather than chasing technical optimization alone, Nextatlas’s research shows that brands must focus on how they participate in those conversations.
“AI is not the facilitator, but it’s a catalyst for introspection,” Damaszko said.
That shift could accelerate fashion’s move away from trend-driven cycles toward more individual expression. AI stylists such as Alta, which builds outfits around specific occasions and even integrates a “fragrance wardrobe,” hint at how agentic systems may prioritize personal context over seasonal narratives.
“Agentic shopping is not only great to buy new products,” said Damaszko. “I also want to know what I have and know how to match them.”
As AI automates more of the sales process, physical retail may become even more key to retaining brand loyalty. Mora predicts fewer stores, fewer sales associates and higher intent among those who do visit.
“The future of luxury is physicality,” she said. “It’s about unrepeatable experiences.”
Sales staff, she added, will become scarcer and more highly trained, while stores evolve into immersive environments closer to museums than retail floors with scent and sound playing a larger role. Some physical stores could become invitation-only. The model, she believes, will increasingly resemble Hermès’ Birkin, where even being able to access the product becomes part of the brand value.
Nextatlas shares that view, warning that while checkout can become frictionless, discovery doesn’t necessarily need to be.
“People enjoy mystery, enjoy serendipity, enjoy recommendations from peers,” Nextatlas’ Morena said. “It doesn’t always have to be about solving a problem.”
Agentic commerce may be well suited to comparing utilities or washing machines, but fashion brands will need to remain tied to emotion, aspiration and identity.
The competitive landscape is likely to split. In the near term, larger brands with deep pockets and robust data pipelines will enjoy an advantage in AI-driven discovery. Over the long term, differentiation will hinge on whether brands can translate their cultural cachet into agent system design.
The risk, however, is that efficiency-driven optimization will erode cultural distinction. If brands focus solely on becoming machine-readable, they may sacrifice the very qualities that justify premium pricing, asserted Vlge’s Mora.
“If brands don’t build better stories, and have their agents and IP do the same, we will flatten culture, taste, identity,” she said. “With the revenue-motivated efficiency push, we can destroy the soul of fashion.”
