AI Shopping Assistants: What Every Fashion Brand Needs to Know in 2025
- Molly Sishton
- Sep 10
- 4 min read
Updated: Sep 23
AI shopping assistants are no longer a test feature - they’re shaping how consumers discover, compare, and buy. The question for fashion brands is simple: will your products show up, or be invisible?
The Rise of AI Shopping Assistants
For two decades, ecommerce has been built around search engines and marketplaces. But the landscape is changing quickly: consumers are shifting from typing into Google to conversing with AI-powered shopping agents like ChatGPT, Perplexity, and Amazon Rufus.
Instead of scrolling through endless product grids, a shopper might simply ask:“Show me a sustainable burgundy midi dress under £150 for a rooftop wedding.”
The AI takes care of the filtering, the recommendations, and in some cases, even the checkout.
43% of U.S. adults are now aware of AI shopping assistants—though only 14% have used one so far (Digital Commerce 360).
Adoption is accelerating. ARK Invest projects agentic AI could drive $9 trillion in ecommerce sales by 2030, representing a quarter of global retail transactions.
Amazon’s Rufus is already handling millions of product searches each month, and early reports suggest double-digit gains in conversion rates for queries routed through it.
This is not just a shift in technology. It’s a shift in power: from brand websites and ad platforms to AI intermediaries.
Voices from the Ground
On Reddit’s ecommerce forums, store owners and managers are already testing the edges of this shift:
“AI agents can already handle 70–80% of our customer support requests, like ‘where’s my order?’ The scary bit is if they get as good at recommending products—then visibility will be entirely out of our hands.”
“We tried integrating an AI shopping assistant as a test, and the biggest surprise was speed. Customers were checking out in under 3 minutes. But if you’re not in the assistant’s dataset, you’re out.”
“My worry is that smaller brands will get buried. Unless your data is structured and searchable, these systems will just default to Amazon or big retailers.”
These comments highlight the tension: AI can improve efficiency, but also risks centralizing discovery around whoever feeds the machine best.
Where Fashion Brands Win (and Lose)
Let’s be blunt: product data is the new SEO.
Winners will be those with clean, richly attributed feeds. If your dresses are tagged with details like “sustainable,” “linen,” “burgundy,” “midi length,” you stand a chance.
Losers will be those relying on vague product titles like “Women’s Dress 20384”. AI agents don’t guess—they ignore.
Ads won’t guarantee visibility. While ad products for AI agents are coming, discovery in the agentic era starts with structured data, not bidding wars.
Even large retailers aren’t immune. A leaked Amazon internal study noted that 30% of queries through Rufus couldn’t be matched properly because of poor product data. That’s Amazon—imagine the risk for smaller brands.
Why This Matters for Fashion in Particular
Fashion is complex. Unlike electronics or groceries, it relies on style, context, and personal preference—things that don’t always fit into neat categories.
But for AI, if it’s not structured, it doesn’t exist.
A shopper might ask: “Show me a 90s-inspired slip dress in chocolate satin under £120.”
If your data doesn’t explicitly say 90s-inspired, slip dress, chocolate, satin, you won’t appear—no matter how perfect your product is.
This is where fashion brands face the biggest challenge and opportunity: building data that captures the nuance of style.
Checklist: Is Your Brand AI-Ready?
✅ Titles – Do your product titles include descriptive keywords, not just generic SKUs? ✅ Attributes – Are colors, materials, styles, and fits consistently tagged? ✅ Structured Data – Is your feed in a format that AI agents can parse? ✅ Conversational Queries – Would your product show up for a natural-language query? ✅ Trust Layer – Are you clear about sustainability, shipping, and returns (factors shoppers often ask about)?
Human Trust Still Matters
It’s not just data. Consumers are still wary of letting AI do everything.
A TechRadar survey found that 66% of shoppers wouldn’t allow AI to make purchases for them, even if it promised better prices.
Shoppers cite privacy, poor chatbot experiences, and lack of control as barriers.
So while AI may handle discovery, brands must still build trust and loyalty—through quality, authenticity, and service.
The Path Forward
Agentic commerce is still early, but momentum is undeniable. Ecommerce managers and fashion brands should treat 2025 as the year to prepare.
Audit your product data now—don’t assume what worked for Google will work for AI.
Optimize for conversation—think in terms of “what would my shopper actually ask?”
Act before it’s mainstream—being early can secure lasting visibility.
FAQs
What is agentic shopping? Agentic shopping refers to AI-powered agents (like ChatGPT or Amazon Rufus) that actively shop on behalf of consumers—searching, filtering, and even completing purchases.
Why should fashion brands care? Fashion discovery depends on style, fit, and context. If your product data doesn’t reflect those nuances, AI assistants won’t surface your products, even if they’re relevant.
Can ads guarantee visibility?
Not yet. Early signs suggest AI assistants will favor structured data first, with ads as a later layer. Waiting to “just pay for ads” is risky.
What should brands do now? Run an Agentic Shopping Readiness Audit. This evaluates your product feeds, tags, and structure to highlight whether you’d appear in AI-driven queries.
At SO-ME App Ltd, we’ve built one of the first Agentic Shopping Readiness Audits. We help fashion brands and ecommerce managers understand whether they’re discoverable in the era of AI shopping—and what fixes will make them visible.
Because in this new landscape, visibility isn’t optional. It’s survival.
