Excess inventory in fashion is one of the industry’s most expensive secrets: clothes that are designed, sewn and shipped, only to sit on shelves or in warehouses until they are discounted or destroyed. The Business of Fashion estimated that the industry’s excess stock in 2023 was worth between $70 billion and $140 billion in sales. Today, global brands are betting on artificial intelligence to tackle the problem at its root, rather than after the damage is done.

Why does excess inventory in fashion build up?

Poor planning is only part of the story. Brands are afraid of missing a sale, so they would rather produce too much than too little. Add the pressure to launch new pieces constantly, factory minimum order quantities, and trends that may live only a few weeks on TikTok. Estimates reported by The Guardian suggest that around 40% of clothing produced globally goes unsold.

The result is that excess inventory in fashion causes a double loss: for the environment, and for the brand’s margins as it is forced into repeated markdowns. That is why experts agree the real cure is to stop excess inventory in fashion before it is ever produced.

What does this mean for you as a designer? Every unsold piece is wasted fabric, effort and time. When you start your own small label, producing limited quantities and testing a design before scaling up is not a weakness — it is exactly the business intelligence that big companies are now chasing with technology.

AI at the design table

Some designers have already invited AI into the studio. Alice + Olivia founder Stacey Bendet revealed that she used AI at the beginning of the design process for her Spring 2026 collection. But the technology has not won everyone over: some executives feel its results still fall short of what their designers produce, and that it is currently better suited to back-office work than to creativity.

This is where trend-forecasting platform WGSN comes in, used by brands such as Coach and Adidas. Its “Opportunity Calculator” estimates the financial opportunity in a buy, while its “Assortment Builder” helps optimise the product mix, drawing on e-commerce data, catwalk data, and search and social trends.

What does this mean for you? Here, AI does not draw for you — it answers every buyer’s question: how many of this design will I actually sell? A designer who understands the language of data alongside the language of cut and cloth will be closer to production decisions in any brand she works with.

The store that arranges itself

Start-up Flagship analyses product, inventory and sales data to suggest how each store should be laid out for the tastes of local customers, working with brands such as Vuori, Mejuri and Madhappy. Its founder Simon Molnar says some brands have just three visual merchandisers for nearly 400 stores, so following every branch by hand is almost impossible. When the right piece is shown in the right place, it sells at full price instead of ending up in the sale.

When the search bar understands your customer

Stockholm-based Depict AI works on a point many overlook: the search bar in an online store. According to the company’s Axel Larsson, 10% to 15% of traditional searches return nothing at all — a customer ready to buy finds nothing, even though the piece may be in stock. The company aims to bring that below 2%.

The answer is natural-language search: instead of typing “green dress”, the customer types “a dress for a September wedding”. In a test with Joseph, around half of the site’s users were searching this way within about three months. Esme Lampard of Joseph says the real value is understanding why customers buy and where they will wear the piece, not just the item itself.

What does this mean for you? Think about your designs through the lens of the occasion: who is this piece for, and when will it be worn? That is the question big brands are now answering with AI — and you can build it into your product descriptions and photos from day one.

Is AI a magic fix?

Not quite. Fast-fashion brands use the same technology to speed up production and chase every trend, making the problem worse rather than better. The difference lies in how it is used: does AI help produce what people actually want — no more, no less — or does it become a tool to produce more, faster?

What do future designers gain?

The battle against excess inventory in fashion will not be won in the warehouse, but at the design table and at the first buying decision. The designer who combines creative instinct, an understanding of pattern and fabric, and the ability to read data and use AI tools is the one brands will need in the years ahead. At Anna Stella Academy, we believe smart design starts with a simple question: does anyone really need this piece?

Main source: Emma Rayder, “Can AI Solve Fashion’s Excess Inventory Problems?”, Fashionista (October 2025).