AI Is Finally Solving The Online Returns Crisis, And It’s Worth Billions To Whoever Gets There First

Fashion e-commerce has a dirty secret: somewhere between a quarter and half of everything sold online comes straight back.

Across Europe, around 26% of all online apparel orders are sent back (shoes are even worse at 27%), and in markets like Germany and Switzerland the figures hit 44% and 45% respectively. Each returned package costs a retailer between €20 and €45 in transport, handling and restocking once all the costs are added up.

McKinsey estimates that up to 30% of fashion items bought online in Europe are returned, most of them because the shopper bought multiple sizes and kept only the best fit. The industry has tried charging for returns, limiting refund windows and improving size guides. None of it has moved the needle meaningfully.

A new wave of AI startups thinks it knows why: every previous fix tried to discourage returns rather than prevent them. If about 70% of fashion returns are driven by sizing mismatches, according to data cited by sizing AI companies, then the real solution is giving shoppers enough confidence before they buy that they don’t need to hedge with multiple orders. That’s the problem virtual try-on technology is now well equipped to address, and the commercial prize for getting it right is enormous.

The technology has been around in various forms for a while, but what’s changed is the quality. Earlier iterations were novelty features that showed a flat image of a garment overlaid on a photo. What’s emerging now incorporates fabric physics, body movement modelling and personalised size prediction, all tools that meaningfully close the gap between seeing something on a screen and knowing how it’ll actually fit.

Why This Isn’t The Same Try-On Tool You Ignored Five Years Ago

The virtual try-on category has split into two distinct approaches, and both are attracting serious investment.

The first is AI-powered sizing: tools that build a personal fit profile based on body measurements, past purchases or photographs and use that profile to predict which size will fit best for a specific garment from a specific brand. French startup Fringle takes this approach, matching a user’s body measurements with garment dimensions directly and claiming to cut returns for brands including Maje. Zalando, the German fashion giant, has rolled out a similar tool where customers take two photos in form-fitting clothes to generate a size profile for future purchases.

The second approach is visual: virtual fitting rooms that let shoppers see how a garment, pair of shoes or accessory looks on their own body or a realistic avatar. Platforms including Virton, WANNA and iAugment are positioning themselves as plug-in solutions for existing e-commerce sites across the UK and Europe. The more sophisticated versions now model how fabric drapes and moves, addressing one of the main complaints about earlier try-on tools, which showed how something looked but not how it behaved.

Early pilots and adopter cases suggest meaningful impact. AI-driven sizing and virtual try-on have cut fashion-related return rates by around 30 to 40% in some implementations, according to retail technology analysts. Retailers also report higher conversion rates and larger basket sizes when try-on tools are available, meaning the technology can function as both a cost reduction and a revenue driver.

For markets with already high baseline return rates, the margin improvement potential here is substantial.

The Free Returns Culture That’s Now A Trap

The UK sits in an interesting position: British shoppers are accustomed to generous return policies, with most major UK and European retailers offering free returns within 30 to 90 days.

That’s been a competitive necessity rather than a strategic choice, and it’s created a customer expectation that’s now very difficult to walk back. Charging for returns risks customer backlash, and tightening refund windows risks losing customers to competitors who haven’t. The only exit from that trap is removing the reason most customers return in the first place.

A 2026 survey of European retailers found that 36% see keeping pace with AI as a major challenge, with legacy system integration and skills gaps cited as the main obstacles, that hesitation is understandable but increasingly expensive. The retailers who move first on virtual try-on and AI sizing will build proprietary fit data on their customers that becomes harder to replicate over time. The ones that wait will face the same return rates they’ve always had, with the added pressure of competing against brands whose unit economics have become structurally better.

For e-commerce startups building in fashion and apparel, what that means in practice is more direct. A startup that embeds strong virtual try-on or AI sizing from the start builds a structural advantage in returns costs that a late adopter will struggle to close. It’s also a meaningful differentiator in fundraising conversations, since investors who understand retail unit economics know that a 30% reduction in return rates changes the entire P&L shape of a fashion e-commerce business.

The Race Nobody Has Won Yet

The commercial prize here is real, and it’s still wide open.

No single company has established itself as the dominant platform for virtual try-on or AI sizing at European scale. Zalando’s internal tool gives it an advantage within its own platform, but the wider market, covering hundreds of independent fashion brands, mid-size retailers and e-commerce platforms that don’t have the resources to build their own solution, is still there for the taking.

The startups most likely to win are the ones that solve the integration problem: making it easy for any retailer to add AI-powered fit tools without a complex implementation, and building enough proprietary data over time to make their size predictions meaningfully more accurate than anything a new entrant could replicate quickly. That’s a data flywheel problem as much as a technology problem, and the window for establishing that flywheel is narrowing.

Online retail returns are a multibillion-euro drag on an industry that can’t afford to keep absorbing it. The tools now exist to address the root cause directly, and what the market is waiting for is the company that makes them easy enough, accurate enough and affordable enough to become standard.

Whoever gets there first won’t just be solving a logistics problem – they’ll own a category.