How AI is driving the niche-ification of retail tech

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AI is killing one-size-fits-all retail tech – and fashion is showing us what comes next.

When was the last time you had a unique experience on an online store? Like it or not, most e-commerce sites look remarkably similar – even if one sells shoes and the other school supplies.

But the one-size-fits-all approach to commerce is dying. And AI is killing it.

For the first time ever, retailers can niche down and scale up with AI – creating unique experiences tailored to their industry and customer needs. The next revolution of tech isn’t one global unified change, it’s hyper-specialisation.

Fashion is one perfectly fitting example of this. It’s vastly different to its retail cousins – and these differences add significant complexity. Trends change at breakneck speeds. One shirt can come in ten sizes and 15 colours. Buying multiple garments just to return them is part of a shopper’s purchasing process. And how a retailer styles a piece can make or break sales.

The list goes on. But it’s safe to say retailers selling coffee machines don’t have the same needs or complexity.

So why have brands in high-complexity industries such as fashion continued to rely on generic tech for so many years? It’s simple: there were few alternatives. AI is changing that – and pushing fashion technology toward greater specialisation.

The rise of AI fashion experiences: retailers are creating and adopting new experiences built specifically around the way people shop for fashion.

The growing need for fashion-specific context: shopping on LLMs and through agents is forcing retailers to provide richer product data to ensure their products can be understood and discovered.

Last year, the first serious AI sensation hit fashion. Zara released its virtual try-on avatar. It was just in Spain. It was still in beta. But the reaction was huge – generating massive hype online and hundreds of write-ups in the press. For online stores, Zara became the North Star overnight.

Since then, we’ve seen waves of fashion-specific AI experiences launching and improving at a rapid rate. Zalando’s Size & Fit avatars and 3D product twins are just two more examples of tools that help shoppers better understand how an item of clothing looks and feels.

Meanwhile, Stitch Fix launched its AI style visualisation platform, where shoppers can generate images of themselves in recommended outfits.

Companies specialising in AI fashion experiences aren’t just gaining traction – they’re attracting millions in investment. Daydream, an AI-powered platform for fashion discovery, has already raised $53 million and onboarded 2,000 fashion brands. It sells fashion directly on its own platform, and retailers can also integrate its fashion-specific AI search functionality into their own sites with its Powered by Daydream feature. Brands such as STAUD, Alice + Olivia and Cult Mia are already testing the pilot.

Fashion-specific AI is even going beyond what customers recognise as AI. Content production tools are transforming storefronts across the industry – often without shoppers even realising. From product descriptions to model photoshoots, retailers are able to produce content 95 per cent faster and 90 per cent cheaper.

One of the biggest players in AI content production, SCAYLE STUDIOS, was built in collaboration with online fashion marketplace ABOUT YOU. With just one product photo, it can generate studio-quality images, GIFs and videos. Brands can style pieces on diverse AI-generated models across different body shapes, ages and skin tones. They can choose from a range of environments – from photo studio backgrounds to paparazzi-style images.

The Co-CEO of ABOUT YOU, Tarek Müller, stated the tool has so far “saved us over €8 million per year.”

But the niche-ification of retail tech doesn’t just apply to front-end experiences. The rise of LLM shopping is driving change on an architectural level too.

Retailers are in a race for visibility on AI platforms. 80 per cent of shoppers under 44 now use LLMs as their primary shopping vector.

Platforms such as ChatGPT and Claude are rapidly developing their commerce capabilities. However, integrated conversion is still some way off. ChatGPT has launched Instant Checkout in the US – allowing shoppers to purchase products directly on the app. But Claude is not quite there yet.

With purchasing still limited, the biggest opportunity for retailers lies in discovery. But how do you actually show up?

If a shopper says “I want a burnt-orange dress like Zendaya’s from the Oscars, but in size 12 and not satin,” the LLM needs a huge amount of information from a product description to fulfil that brief. In a discovery-focused search, they may ask, “can you suggest some clothes that fit the ‘office siren’ look?” In that case, the LLMs don’t even have a specific colour, cut or fabric to look for – just a vibe to go off. To solve this, LLMs need context-rich information about products, meaning product data is no longer a hygiene factor, it is a revenue driver.

For enterprise fashion retailers already dealing with high SKU complexity, huge catalogues and hyper-fast trend cycles, providing this context can seem like an insurmountable task.

Fashion-specific AI tools are already filling that niche – able to scan items of clothing and describe them in granular detail.

Fashion retailers are also looking at their core architecture, asking whether it can understand industry-specific context in minute detail. And platforms such as SCAYLE – built specifically for the complexity of fashion – are making significant waves.

Levi’s, the world’s largest denim retailer, announced it was replatforming to SCAYLE. Jason Gowans, Chief Digital & Technology Officer at Levi’s, said the platform’s fashion specialisation was a key motivator behind the move: “We needed a trusted partner that combines best-in-class technology with a deep understanding of fashion and its unique nuances in fit, style and storytelling.”

Fashion retailers now need a tech stack that understands fashion’s complexity, can handle complex industry-specific product data and can react as fast as the market moves. If the platform underpinning that product isn’t built to handle this new level of detail, retailers won’t keep up.

Want to learn more about SCAYLE? Explore the platform here

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