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Here, things pinch; hangs out there; the drape is wrong. These are just a few examples of the feedback a new generation of artificial intelligence applications could give to a potential customer trying on clothes before a purchase, reducing the chances of a product being returned to a store.
Fashion retailers are increasingly turning to AI to solve the problem of increasing product returns, a persistent drag on profitability and what many in the industry call the industry’s “silent killer.”
A growing number of AI startups have emerged to offer virtual try-on technology, allowing potential customers to visualize fit and style before purchasing.
While tech companies have been trying to solve online adaptation challenges since the 2010s, the rapid development of generative AI has finally made these applications powerful enough to significantly impact retailers’ bottom lines.
The US National Retail Federation estimated late last year that 15.8% of the annual amount retail sales were returned in 2025, totaling $849.9 billion. For online sales, this figure increased to 19.3%. Generation Z is driving this trend, with shoppers ages 18 to 30 making an average of nearly eight online returns per person last year, according to the NRF.
Most returned items never return to the shelves and often cost the retailer more than the value of the refund itself. This is a multi-billion dollar problem for the industry that directly eats into company margins.
“Figuring out how to proactively use returns and then how to minimize them can be a significant driver of business and profitability,” Simeon Siegel, senior managing director at Guggenheim, told CNBC.
While fitting technology will never be as effective as trying something on in person, it’s a great way to bridge the gap, Siegel said. “The situation will continue to improve, I think it will continue to reduce yields.”
Mirror realism?The biggest reason for returns and abandoned carts is uncertainty about fit, Ed Voyce, founder and CEO of AI startup Catches, told CNBC.
Catches has developed a platform that allows users to create a “digital twin” to virtually try on clothes with what it calls “mirror-like realism.” The app went live last month on luxury brand Amiri’s website for a select range of clothing.
Unlike other designs that Voyce says “just look pretty,” the Catches platform incorporates the physics of fabric texture and how the material interacts with a moving body.
“The reason we created Catches was to take advantage of a sort of confluence of technologies that is happening right now to solve this problem in an efficient way,” says Voyce, founder of the startup backed by LVMH Antoine Arnault and built on from Nvidia CUDA platform.
“The reason it’s now possible to solve this problem in terms of timing is because you need to be able to run visuals for end users on bare metal in the cloud, at a low enough cost to build a project. [return on investment] for brands,” says Voyce.
“This technology has the potential to impact the entire industry and truly usher in the next wave of what end users have come to expect.”
Protect the marginThese AI tools are not only intended to reduce returns but also improve purchases.
While e-commerce has grown rapidly in recent years, with online shopping driving retail sales growth, current U.S. trade policy under President Donald Trump has put a brake on the sector which relies heavily on manufacturing in Southeast Asia. Across the price spectrum, retailers are struggling to maintain margins as costs rise and consumers become increasingly price sensitive amid inflationary pressures.
Although yields weigh heavily on profit margins, they are also a critical factor in consumers’ purchasing decisions. NRF data shows that 82% of consumers view free returns as essential, but the cost of providing them is becoming unsustainable for many brands.
Retailers are now testing a combination of technologies and policies to protect their margins.
Strategies to reduce returns range from charging return shipping costs to providing more detailed sizing information and encouraging exchanges rather than refunds.
Zara, owned by Inditexwas one of the first to introduce a return fee for online orders and, although it was a controversial change for some customers, it helped the Spanish retailer protect its gross margin and discourage “bracketing” – the practice of buying multiple sizes to try on at home.
The retailer also rolled out a virtual try-on tool, “Zara try-on,” in December.
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In the meantime, ASOS recently highlighted a marked improvement in its profitability, partly due to a 160 basis point reduction in its return rate.
The online fast fashion player was experience the virtual trials in partnership with deep tech startup AIUTA, allowing potential customers to view a garment on a range of body types, sizes and skin tones. ASOS warns, however, that the tool is designed to give general advice and that customers should always consult size guides before purchasing.
Shopifymeanwhile, has integrated startup Genlook’s AI virtual try-on app into its commerce platform, which it says “removes doubts about sizing, builds buyer confidence, and drives higher conversion rates while reducing costly returns.”
Tech giants like Amazon, AdobeAnd Google have also created virtual trials in various forms, partnering with major brands to roll out the technology.
Starting April 30, Google’s virtual try-on technology will be accessible directly in product search results on Google platforms, according to the Google Labs website.
As for Catches, it predicts that its app can generate a 10% increase in conversions and 20 to 30 times greater ROI for partner brands. It focuses on luxury brands because of their higher prices. The startup has not yet quantified the extent of the drop in returns linked to the use of its platform, but is aiming for “massive reductions”.
Not a universal solution“There are certainly companies that have absolutely seen benefits — it’s harder to figure out how to quantify them,” Siegel said.
Although the benefits are obvious, the analyst cautions that AI is not a magic wand. Beyond suitability, retailers are turning to AI for inventory management, customer targeting and fraud prevention.
“All of these use cases are really interesting, as long as companies don’t abandon what they are,” Siegel says.
“What you sell will always be more important than how you sell, and so I think remembering that will help dictate who gains, benefits from, and amplifies AI versus who is consumed by it.”
