Beyond Chatbots: AI Reshaping Fashion eCommerce
Jemma Stacey, former founder of resale live-streaming platform FINDS and an expert in AI X Retail, and I decode exactly how technology is not only disrupting but creating opportunities for fashion.
Look up any Vogue Business or Business of Fashion Magazine headline featuring “AI + ___,” and you’ll quickly realize artificial intelligence (AI) is infiltrating every corner of fashion. There’s palpable excitement around its impact on design, search, resale, inspiration, and more. The question isn’t if AI is shaping fashion but how soon and who will win.
Investors are also betting big on the application of AI in fashion and retail. From Daydream’s (AI-powered Fashion Search Engine) $50M seed funding round to Alta’s (AI-powered styling & shopping platform) $11M seed round, and Raspberry AI’s (Generative AI-powered design tool) $24M raise, we’ve entered the “Fashion Tech Boom 2.0.”
In my sit-down with Jemma Stacey, the former Founder & CEO of the resale live-streaming platform FINDS and a consultant at the intersection of AI and Fashion, we discuss how AI is rapidly transforming the shopping experience across new and resale fashion markets. We discuss the evolution of shopping interfaces, the role of AI in Search and Discovery, the importance of “Social Proof” within AI-powered interfaces, and what it takes to stand out in the age of AI-powered fashion.
Now that you understand the context, here are the top takeaways below.
AI’s Superpowers and Reach
AI’s impact on eCommerce will be vast. According to Jemma, AI is impacting “design, supply chain, all the way through to the actual discovery interface, through to resale and streamlining listing processes…[and] agent checkout." AI can exist as your personal assistant along the shopping journey by bringing you personalized, curated collections and by reducing the cognitive load associated with shopping online today.
Right now, shopping for clothes online or on social media presents too many options; I know I am not alone in having countless open tabs. ChatGPT, Google’s AI Mode, and more specialized search engines like Daydream are vying to provide a more seamless experience. Brands and retailers will need to advance their websites’ search and discovery to survive.
We’ll likely see two camps emerge: AI-native interfaces and traditional brand and retailer eCommerce sites, which try to play catch-up and adapt to the new baselines retroactively. We’ll define “AI-native” as an interface that “layers across the top of e-commerce that will…scan the web, bring in products, and…show you products that way where you can actually natively purchase” and are hyper-personalized. These AI-native platforms may evolve to include checkout agents, which automate purchase decisions. Google’s AI Mode announcement earlier this spring seems to indicate that an early version of this checkout agent is on its way.
Yet, even AI-native platforms are far from perfect. AI hasn’t traditionally been able to understand the nuance of fashion shopping, but we’re at a turning point now with the larger players and more specialist fashion-tech startups.
Chatbot turn Shopping Assistant
ChatGPT was the first breakthrough, ushering in the Gen-AI age, and it has slowly shifted the possibilities of search and discovery. From ChatGPT to Google’s AI Mode and Perplexity’s Shop Like a Pro, big-name AI platforms are allowing users to search for and discover new products directly in the conversational search experience.
Ask and you shall receive. Spend Less time scrolling your favorite brand or retailer’s website searching for a product now that you can simply name your brand, your price range, and other preferences. However, today’s AI search platforms are still largely conversational. Goal or context-oriented search in chatbots is an improvement, but today’s user experience has room for improvement.
First, conversational search can feel very easy, but it still needs constant refinement and back-and-forth discussion. You’re not likely to get the exact result from your first prompt. With fashion, some individuals know exactly what they want, down to the tiniest of details; others want to explore their options fully; and lastly, some individuals are in the middle, where they have an idea of what they want, but need help refining their choices or to at least simplify their choices. Individuals who know exactly what they want may be able to prompt their way into finding the exact product they’re looking for with a chatbot. However, all three groups would benefit from other elements that blend in social discovery with a more visual experience.
Jemma argues, “shopping and for fashion, where it's quite a visual experience, people are very led on aesthetics, [and] that conversational back and forth can also have its own friction.” Some platforms already understand this need. Google is slowly introducing innovative UI, including new virtual try-on and existing visual image search, into Google AI Mode’s product discovery. Other platforms, such as clothing resale search startup Beni, have introduced Beni Image Search, by which users can take a picture or upload an image to discover similar secondhand options. AI-powered fashion search engine Daydream’s beta version also allows users to upload images so that the platform can help users find the closest product.
While AI-powered chatbots and fashion-tech platforms are offering consumers personalized searches, no one has cracked social proof. Fashion is an inherently social experience. There’s a reason we ask our friends for their opinions or look to fashion and style creators on social media. Social proof is highly influential when you’re deciding what to buy. That’s also why publications from the NY Times to Condé Nast Traveler and Forbes have dedicated product review sections.
Social media solves social proof because of the creator economy, built-in shopping, and a treasure trove of data to show relevant results. Yet, the user experience isn’t optimized around shopping. Social media does a great job with discovery, but platforms are still testing how to convert finds into sales. TikTok seems to be the closest to cracking commerce on its platform with TikTok Shop. Fashion needs shopping first, with social layers built on top.
Depth vs. Breadth
What larger players and smaller players will solve for is likely very different. An OpenAI, Google, or Perplexity may be less likely to incorporate social context into their product’s solution set. These “generalists” will “broadly be good at just searching for anything: purchases that have less consideration, that maybe are less important.” Questions that require “a deep understanding of fashion, style, taste, subcultures, social influence” will be answered by fashion specialist startups.
These are your Daydream, Alta, Beni, Phia, Plush, etc., that will be trained on “deeper personalization and training on…fashion style, taste.” These platforms may even build “a deeper understanding of your purchase history, building around like the wardrobe.” A solution set that incorporates the end-to-end fashion shopping experience would close the “full loop from like purchase through to resale.” Imagine your AI fashion assistant performing wardrobe analysis that leads to listing and negotiating resale and rental procedures on your behalf.
End-of-life services from resale to rental are still fragmented, and competition is heating up, as more startups vie for market share. I predict that we’ll see closer integration between resale, rental, and fashion search and discovery shopping platforms over the next couple of years. Competition is already breeding innovation, and the larger players aren’t likely to incorporate deep end-to-end development in fashion. However, with smaller startups leading the way, the features outlined above may not be that far away.
Who will be the Winners?
With the plethora of AI-powered tools, technical teams are able to ship features faster than ever, certainly at earlier stages that involve product validation. Incorporating advanced features such as visual image search, virtual-try-on, and others will still take time, but the barrier to entry is much lower.
We’re not too far away from a future where personalization, visual image search, agentic checkout, and virtual try-on become the new baseline expectation. So how do platforms differentiate themselves on the market if we see a future where features are similar?
Building communities, tapping into social media, and brand positioning are still important. However, startups that master distribution and data will reign supreme. What’s distribution? It’s how people hear about your brand, how easy is it to use, and what markets you’re entering. A startup’s job is then, to take all the data that users provide and craft personalized, curated experiences. Experiences that blend ease and social proof. In Jemma’s experience, no one has cracked this yet, but we’ll soon start seeing clear winners.
Jemma’s favorite AI-first shopping apps? As a builder in the resale space and passionate about sustainability and circularity, Jemma recommends Beni and Phia for secondhand fashion discovery. She even teases startups building in stealth that are working on agentic resale.
Closing Predictions
I predict that we’ll likely see 1) early-stage mergers amongst fashion-tech platforms that are working on siloed solutions, and 2) as a result we’ll see a consolidation of platforms that amount to “super apps.” Given the trajectory of the industry and the competition amongst siloed sections of the shopping journey, I don’t think this is too bold of a take. Who do you think will crack AI shopping first? Comment below or share this post with someone building in fashion-tech.
This is just a taste of our full conversation. Listen to the full breakdown by tuning into the full episode on Spotify. You can follow Jemma on LinkedIn and her Medium page where she regularly posts in-depth insights on fashion and AI.