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How Zara, H&M and Zalando are utilising AI to transform precision in inventory, assortment strategy and product decisions.
Technology in Fashion
23 February, 2026
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Artificial intelligence in fashion is debuting a new phase in a rapid way. For the past decade, AI has been adopted mainly by marketing in chatbots, personalised recommendations, and campaign optimisation, developed to enhance customer acquisition and conversion. But now, the industry’s focus is evolving. According to McKinsey, 75% of fashion executives are now prioritising AI for inventory optimisation, demand forecasting, and controlling cost, marking a change toward merchandising’s core functions instead of customer engagement alone.
AI is becoming embedded in merchandising, influencing which items are designed, how much is produced, where inventory is located, and how assortments are managed in real time. This transformation shows the budding recognition that merchandising decisions determine inventory risk, revenue performance, and margin outcomes. AI-based demand forecasting can decrease the forecasting errors by 20-50%, allowing brands to significantly enhance product availability while minimising excess inventory.
This change reflects a structural shift in how fashion companies work. Merchandising determines commercial results such as margins, revenue, and working capital efficacy, and labels are coming to know that optimising marketing is not enough. As volatility, quick trend cycles and inventory pressures are rising in the industry, AI is climbing to be a foundational layer underpinning modern marketing and merchandising.
The initial wave of AI adoption in fashion was based on customer-facing applications. Dynamic pricing tools, conversational interfaces, and recommendation engines allowed brands to customise the shopping experience at scale. These technologies gave measurable gains in engagement and conversion rates, especially in e-commerce.
Anyways, fashion leaders are now looping in the fact that the greatest financial impact lies even earlier in the value chain. Merchandising decisions like inventory investments, assortment planning, and replenishment show whether the right products exist in the right place. AI allows the brands to move from a reactive approach to predictive merchandising through real-time data signals such as search trends, customer behaviour, regional demand patterns, and product performance.
One of the most immediate impacts of AI is improving the accuracy of on-demand forecasting. Traditional forecasting methods depend heavily on manual adjustments and historical data, which can limit the quick responsiveness to fast-shifting trends. In contrast, AI models can process vast datasets such as social signals, weather, customer browsing history, and regional behavior to forecast demand at minute levels too.
Zalando GmbH loops AI in on assortment planning to analyse customer demand and anticipate it across markets, enabling precise buying decisions and decreasing risk in overstock. This allows inventory decisions beyond seasonal guessing.
Mango employs AI analytics for store and online performance tracking by dynamically allowing the team to adjust product allocation. It helps in responsive inventory shifts based on live data. Nike applies machine learning to weather, sales, and consumer data for demand prediction at SKU/store levels.
This shift enables fashion companies to align their inventory investment with actual demand signals instead of betting on intuition or seasonal assumptions.
AI is also transitioning how inventory is distributed across locations and channels. Allocation decisions were dependent on a static planning approach before, often resulting in stock imbalances.
Fast Retailing Co., Ltd. uses AI to analyse the customer purchasing behaviour and optimise the product distribution. This enables the company to work on any adjustments in the inventory levels in a continuous approach that will improve the availability of the product while reducing markdown exposure. The luxury conglomerate, Kering Group, has invested in AI tools to enhance the demand forecasting and inventory precision across its portfolio of brands like Saint Laurent and Gucci. StyleMax, a fast fashion chain, used AI in dynamic allocation and real-time reorders, which gave it an outcome of 47% markdown reduction and 82% fewer stock-outs.
These systems allow fashion companies to operate more efficiently, improve their sell-through rates, and reduce working capital tied up in unsold stock.
AI enables the merchandising strategy to be more localised and customer-focused. Brands can tailor product selection through the regional demand patterns and customer preferences rather than offering identical assortments across markets.
Farfetch UK Limited appointed AI for demand forecasting, matching stock and real-time inventory synchronised to the customer needs across the global luxury markets. This optimised the utilisation by predicting the trends and adjusting allocations. ASOS has AI as machine learning recommenders, collaborative filterers, and a Fit Assistant for localised suggestions. Stitch Fix has AI styling driven by its client data and generative AI visualisation.
This shift represents the change from mass merchandising to demand-sligned smart merchandising.
AI is also influencing product creation itself. It can identify the future demands and emerging styles by analysing social media, sales trends, and customer behaviour. This, in turn, gives it an upper hand in creating collections that will be liked and purchased by the target customers.
Labels like H&M use AI to analyse customer data and identify budding trends, allowing faster product development and enhanced alignment between demand and supply. This enables the companies to decrease product development risk while improving responsiveness to ever-changing preferences of consumers. Zara uses AI to forecast trends from social media and/or runway data. Victoria’s Secret uses generative AI for designing products from recent trends and customer preferences. Adidas has AI/ML for the creation of performance products via consumer data.
As a result, merchandising is gradually changing from a seasonal planning system into a continuous, data-driven process.
AI is cruelly redefining the merchandising sector, transitioning from an intuition- and guess-based discipline into a well-informed, data-driven strategic function. While marketing applications assisted fashion companies in enhancing customer engagement, merchandising shows the larger opportunities of driving financial and operational performance.
By allowing predictive dynamic inventory allocation, demand forecasting and data-driven assortment planning, AI enables brands to balance the product decisions with customer demand. This reduces risks, improves margins and hikes responsiveness in a volatile market.
Cover Image: Digismoothie for ASOS.