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How Businesses Are Using AI to Predict Fashion Trends

How Businesses Are Using AI to Predict Fashion Trends


  • Artificial intelligence helps fashion brands understand consumer behaviors and demands.
  • Startups Spate and Fashable use AI insights to identify style trends for customers.
  • The Fashable co-founder told BI that he envisions brands and customers using AI to co-create in the future.
  • This article is part of “Build IT,” a series on digital technology and innovation trends that are disrupting industries.

Artificial intelligence seems poised to change modes at lightning speed. A 2023 report from management consultant McKinsey & Company estimates that generative AI could add as much as $275 billion to the industry's operating profits over the next three to five years.

Industry executives seem equally optimistic. The Business of Fashion and McKinsey said that in a survey they conducted, about 73% of fashion executives said they plan to prioritize generative AI in 2024.

Although the potential is clear, the path forward is still being mapped out. The opportunity and challenge of AI lies in its multifaceted nature. AI can be used to improve efficiency and sustainability, both to identify trends and create new ones, but it requires careful implementation.

“Fashion is by nature extremely creativity-driven, and AI will completely transform the creative process,” said Benjamin Bond, director at business consultancy Kearney. “When we talk about transformation, we talk about creating speed, creating efficiency, creating relevance.”

What is the trend?

Yarden Horwitz, a former fashion industry analyst at Google and co-founder of data science startup Spate, saw the opportunity to introduce AI to the industry more than a decade ago.

“Back then, fashion brands were still going store to store on Fifth Avenue, comparing what was in the windows and what was in the displays,” Horwitz said. “It was a very manual and very imperfect process.”

Horwitz, alongside his colleague Oliver Zimmer, built the first Google Fashion Trends Report, which used machine learning to identify trends in Google search data. Sweatpants, rompers and midi skirts were on the rise; string bikinis and skinny jeans were in decline.

Portrait of Yarden Horwitz

Yarden Horwitz, co-founder of Spate.
Courtesy of Yarden Horwitz

Other reports followed, covering more topics and industries. Horwitz and Zimmer left Google to found Spate in 2018. Today, the company uses generative AI to augment and expand its analytics.

“We’ve already used AI to identify trends,” Horwitz said. “But using generative AI, you can also ask it to review those models. The AI ​​can generate its own models and offer recommendations.”

Spate launched a AI-powered consumer trends report in 2023. The company used GPT4 to analyze more than 2 billion search signals across 10 industries, including fashion. It found that shoppers were looking for authenticity and the feeling that a product would elevate them, but that, perhaps paradoxically, celebrities remained a force in shaping consumer trends.

A trend analysis of popular fashion styles and aesthetics

Spate's trends report highlighted popular aesthetics such as “Barbiecore” and “old money.”

Finding Fashion's Secret Sauce

Using AI for automated, personalized and targeted reporting holds promise for many industries, but could prove particularly useful for fashion. Bond said fashion's emphasis on creativity can lead to catalogs containing thousands or even tens of thousands of products, the details of which can be difficult to quantify.

Portrait of Benjamin Bond

Benjamin Bond, the director of Kearney.
Courtesy of Benjamin Bond

“One of the dropped hem or even a hem?” » said Bond. “It is impossible to manually identify these attributes across tens of thousands of products.”

He added that AI would enable this “attribution at scale”. A fashion brand could use computer vision to identify product attributes in its catalog. It could then use this data to identify trending attributes and find correlations in its catalog.

AI can also help with the more technical aspects of large-scale fashion production, such as choosing a supplier who can offer the best yarn for a particular garment or finding changes to a garment's design that reduce its cost. cost. Bond said these attributes are often invisible to customers but can make or break a garment in the marketplace.

“From the very beginning of design, we're going to see a ton of generative AI,” Bond said. He expects brands with a history of designing and launching digital products, like Nike, to harness the power of Generative AI for fashion design.

Is co-creation the future?

Fashion startup Fashable is among those creating this new generation of AI-based tools. Born in 2021 from XNFY Lab, a research organization that has worked with Microsoft to drive innovation in retail, Fashable creates AI-generated images for designers, brands, marketplaces and more.

Fashable says its team of 12 trains AI models to generate realistic photos of clothing for customers, based on their information. The goal is to use these images to create styles that are more likely to sell, thereby reducing overproduction and unsold inventory.

Fashable's AI approach also addresses a creativity challenge. While it is possible to ask ChatGPT to create a design, the reality is not that simple. Fashion companies want generative AI to be trained on their data, not their competitors', to ensure the results are true to their style. Likewise, they don't want their data to end up in the hands of competitors.

“Part of our value is that we form a model for a brand, and that model will only be for that brand,” said Orlando Ribas Fernandes, co-founder of Fashable. “We train on their data, and that data, and the results of that data, will only be for them.” This method, Fernandes added, does not “contaminate other AI models,” helping to prevent trademark copyright infringement.

Fashable co-founder Orlando Ribas Fernandes speaks on stage at an event highlighting Fashable.

Fashable's Orlando Ribas Fernandes demonstrates an AI-generated dress at a retail event.

Fernandes argued that the goal of using AI in fashion is not necessarily to reduce design costs. “Everyone thinks AI will be very cheap, and it’s not cheap,” he said. “It is very expensive.” Fernandes expects the industry to instead adopt AI to address its inefficiencies.

Many fashion products are sold well below their retail value, while some end up in landfills without ever being worn. This is a huge problem, one that Fernandes knows is difficult to solve. Still, he hopes that generative AI will lead to fashion that responds to people's specific desires, reducing the need to spawn endless varieties in the hope that one will be a success.

“My vision is that brands will start co-creating with the customer,” Fernandes said. “In the future, I might prefer to have a blazer tailor-made for me, rather than something fast fashion. I can start having my own digital wardrobe, and it will be produced just for me.”




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