Last week we covered AI shopping agents, CFDA x OpenAI, and the workforce shift. This week, three more developments show that fashion AI is getting closer to the everyday experience - for shoppers and for brands.
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1. Google Virtual Try-On Goes Frictionless
Google has been expanding its AI-powered virtual try-on tool, and the latest update removes one of the biggest barriers: the need for a full-body photo. Shoppers can now use a simple selfie to see how clothes look on them across billions of apparel listings on Google Shopping.
The earlier version required users to upload or select a full-body image, which added friction. A selfie is something most people already have on their phone. That difference sounds small, but it changes the behavior. When the effort drops, usage goes up.
Google’s approach works by generating a realistic image of the garment on the user’s body type and skin tone, drawing from the product listing data. It currently covers tops, dresses, and outerwear, with more categories in testing. The technology builds on Google’s diffusion-based image generation models, fine-tuned specifically for apparel.
For brands, this raises the bar on product imagery. If a shopper can virtually try on your competitor’s jacket but not yours - because your listing lacks the structured image data Google needs - that is a missed conversion. Google has published guidelines for merchants on image requirements, and brands that follow them get priority placement in try-on results.
This also matters for smaller brands. Virtual try-on used to require custom development or expensive third-party tools. Google embedding it directly in Shopping means any brand with proper product images can benefit, regardless of budget.
The takeaway: Virtual try-on is shifting from a novelty to a standard shopping feature. Make sure your product images meet platform requirements so your listings are not left out.
2. Target and OpenAI Build a Shopping Experience Inside ChatGPT
OpenAI and Target announced a partnership to create a dedicated Target shopping experience within ChatGPT. Users can describe what they need - “a casual outfit for a weekend trip” or “a cozy sweater under $40” - and ChatGPT will pull from Target’s catalog, suggest products, let users build a cart, and complete checkout without leaving the chat.
This is different from the CFDA x OpenAI collaboration we covered last week, which focused on fashion design tools. The Target partnership is squarely about shopping. It treats ChatGPT as a storefront, not a creative tool.
What makes this notable is the depth of integration. Previous AI shopping features mostly linked out to retailer websites. The Target experience keeps users inside ChatGPT for the entire journey: discovery, comparison, cart building, and payment. That is a significant shift in how retail transactions happen.
For Target, the bet is that conversational shopping converts better than traditional browse-and-filter. Early data from similar integrations (like Shopify’s ChatGPT plugin) suggests that AI-assisted shopping sessions have higher average order values, because the AI can cross-sell and bundle naturally during the conversation.
For other fashion brands, this is a signal. If a major retailer like Target sees enough value to build a native experience inside an AI platform, the channel is real. The question is no longer whether AI shopping matters, but how quickly your brand needs to be present there.
The takeaway: Major retailers are treating AI chat platforms as a new sales channel. Even if you are not building a ChatGPT integration today, your product data should be structured for the AI platforms that will come knocking.
3. Small Brands Are Using AI to Compete With the Big Players
One of the quieter trends in fashion AI is how smaller brands are using accessible tools to close the gap with large retailers. A recent Crescendo AI industry report highlights how AI-powered platforms are enabling personalized recommendations, automated styling suggestions, and dynamic product descriptions - capabilities that used to require enterprise budgets.
The economics are straightforward. A large retailer can afford a team of data scientists to build custom recommendation engines. A five-person brand cannot. But now, off-the-shelf AI tools can analyze a brand’s catalog, match products to customer preferences, and generate personalized suggestions at a fraction of the cost.
Personalization is where this matters most. Studies consistently show that personalized shopping experiences increase conversion rates by 10-30%. Large retailers have been doing this for years. What is new is that a brand running a Shopify store with 200 SKUs can now offer a similar level of personalization using AI plugins that cost under $100 per month.
Beyond recommendations, small brands are using AI for:
- Product photography and lookbooks - generating styled images without booking studios or models for every SKU
- Product descriptions - creating detailed, SEO-optimized copy at scale
- Customer service - AI chatbots that handle sizing questions, returns, and styling advice
- Trend forecasting - spotting emerging patterns in social media data before committing to production
The common thread is speed. Small brands can now test ideas, launch products, and respond to trends faster than before. AI does not replace the creative vision - it handles the repetitive work so the team can focus on what makes the brand distinctive.
The takeaway: You do not need an enterprise budget to use AI effectively. Start with one high-impact area - like generating lookbook images for new arrivals - and expand from there.
What This Means for Your Brand
This week’s stories point to the same shift: AI in fashion is becoming less about experimentation and more about execution.
- If you sell on Google Shopping, check whether your product images qualify for virtual try-on. The brands that show up in try-on results will have a conversion advantage over those that do not.
- If you are watching the AI shopping channel, note that Target is not testing - they are building. The window to prepare your product data for AI platforms is now, not next year.
- If you are a smaller brand, the tools to compete are more accessible than ever. AI-powered personalization, lookbook generation, and automated descriptions are no longer luxuries - they are practical tools that pay for themselves.
The practical step this week: pick one of these three areas and take a small action. Upload a product image to an AI try-on tool. Check if your product descriptions have the structured data AI platforms need. Or try generating a lookbook for your newest item - it takes minutes, not hours.
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Sources: Business of Fashion - Tech Trends Changing Fashion in 2026, Retail Brew - Fashion Retail Braces for 2026, Crescendo AI - AI in Fashion Retail, Google Shopping Help - Virtual Try-On
