AI in Style: How Beauty and Fashion Brands Are Powering Growth with Technology

What if a single technology investment could reduce product returns by 20 to 30%, boost conversion rates by over 70%, and transform a traditional supply chain into a demand-driven engine operating in near real time?  

According to a 2024 market forecast by IDC, global spending on AI in retail and consumer industries is projected to reach US$40.5 billion by 2028, up from US$13.8 billion in 2023. It is also estimated that generative AI could unlock US$400 billion to US$660 billion in annual retail productivity gains worldwide by improving pricing, merchandising, and customer experience. For brands under cost pressure and facing shifting consumer behaviour, AI's commercial economics are becoming too compelling to ignore. 

As margins compress and consumers demand seamless, personalised experiences, leading organisations are treating AI not as an optional enhancement, but as strategic infrastructure capable of reshaping conversion, inventory, design, and fulfilment. What follows is an evidence-based view of where and how AI drives value, along with examples that demonstrate tangible results. 

Strategic Value Levers: Where AI Reshapes Performance 

The power of AI lies in its ability to affect multiple parts of the value chain simultaneously. Major levers include: 

Personalised Discovery and Demand Conversion 

AI-driven recommendation engines, dynamic merchandising, and customer segmentation enable brands to tailor offers and products to individual preferences. This precision enhances relevance, drives higher conversion rates, encourages repeat purchases, and increases the average order value. As digital-first shopping becomes a default, personalisation shifts from being a differentiator to a baseline expectation. 

In beauty and fashion, where style, fit, shade, and taste vary significantly across customers, this tailored approach reduces acquisition friction and increases customer satisfaction. Retailers leveraging advanced personalisation often outperform their peers by 10 to 20% in revenue from these initiatives, according to a McKinsey analysis. 

Virtual Try-On and Uncertainty Reduction 

One of online retail's most significant challenges has been uncertainty around how a product will look, fit, or feel. For apparel, accessories, and especially cosmetics, these doubts have led to high return rates, cart abandonment, and customer hesitation. 

AI-powered computer vision and augmented reality (AR) change this dynamic. By offering virtual try-on, showing how a garment drapes, how a piece of jewellery looks, or how makeup appears on skin, brands convert ambiguity into confidence. This not only increases conversion rates but also reduces returns, improving overall unit economics and customer satisfaction. 

Inventory Optimisation, Demand Forecasting, and Supply Chain Efficiency 

Behind the storefront, AI's predictive power streamlines supply chain planning, demand forecasting, and inventory management. Traditional retail workflows often struggle with overstock or stockouts, markdowns, and mismatched regional demand. AI models that leverage historical sales data, regional trends, and real-time demand signals enable brands to rebalance stock, optimise replenishment, and improve sell-through rates. 

According to a 2024 report by Accenture, AI-enabled demand planning can reduce forecasting errors by up to 30% and improve inventory productivity by as much as 50%. These operational gains directly protect margins, reduce waste, and free up working capital. 

Accelerated Creation, Merchandising, and Go-to-Market Velocity 

Generative AI and data-driven design tools allow faster prototyping, merchandising content generation, and localised campaign creation. In industries with short trend cycles, like fast fashion, seasonal cosmetics, and accessories, this speed-to-market can make the difference between capturing demand and missing it. 

Rather than relying solely on traditional creative workflows, brands can now iterate concepts, experiment with variants, and respond to market feedback rapidly, with lower creative overhead and higher agility. 

Proof of Value in Action: Multiple Industry Examples of AI Impact 

This section provides documented cases where major brands are already capturing value from AI adoption. 

L'Oréal: Virtual Try-On and AR-Powered Beauty Tech 

The global beauty group L'Oréal acquired AR/AI startup ModiFace to deploy virtual makeup and hair-colour try-on across its brand portfolio. Consumers can preview lipstick, eyeshadow, foundation, and hair colour using a live image or video feed before purchase. 

In 2024, L'Oréal reported a 150 % increase in virtual try-on usage, reflecting growing consumer adoption of AR-based beauty tools. External studies show that AR-enabled shopping experiences of this kind can produce conversion uplifts as high as 189 % compared to non-AR flows. By reducing uncertainty around shade, look, and aesthetic fit, virtual try-on lowers return rates and increases purchase confidence. 

This example demonstrates how AI-powered virtual experiences transform what was once an optional novelty into a mainstream commercial lever for beauty retailers. 

H&M: AI-Driven Inventory, Forecasting, and Supply Chain Optimisation 

Global apparel retailer H&M has implemented AI algorithms to analyse customer behaviour, sales patterns, and store-level demand to optimise inventory management and reduce waste. This approach enables more accurate demand forecasting, improved inventory alignment with real demand, and mitigation of excess stock. 

As a result, H&M has realised material gains in operational efficiency and profitability. By optimally aligning supply with demand, the retailer reduces its dependency on markdowns and the working capital tied to unsold inventory. This case illustrates that AI value is not limited to consumer-facing tools, but also manifests in supply-chain and operational levers that are critical for long-term margin protection. 

Zara (Inditex): Trend Forecasting, Demand Sensing, and Agile Merchandising 

Fast-fashion leader Zara (part of group Inditex) leverages AI-supported trend forecasting and demand sensing to anticipate style popularity and consumer preferences. This predictive insight enables Zara to align design, production, and distribution more closely with real-time demand, rather than relying on fixed long-term plans. 

They also utilise AI to optimise inventory distribution across stores, dynamically responding to localised demand signals. This adaptive supply-chain model supports fast-fashion rhythm, reduces overproduction risk, and improves sell-through rates. Zara's approach demonstrates how brands can compress design-to-shelf cycles, improve inventory efficiency, and respond to demand volatility, all while preserving brand agility and relevance. 

Broader Industry Evidence: AI as a Structural Capability Across Fashion and Beauty 

Recent academic and industry-wide analyses underscore that AI and big data are transforming product design, personalisation, supply chain management, and sustainability across multiple global fashion and beauty brands. AI-powered tools are being applied not only in individual pilots but also as enterprise-scale infrastructure to support merchandising, customer experience, inventory management, and product innovation. 

These trends indicate that the transformative potential of AI is reaching far beyond experimental use cases. For many organisations, AI is becoming a fundamental infrastructure that underpins competitive advantage, operational resilience, and long-term growth. 

Strategic Considerations for Scalable Deployment 

To capture value at an enterprise scale, adopting AI requires more than point solutions; it demands strategic discipline and a strong foundation. Leading organisations succeed when they: 

• Prioritise high-impact use cases with measurable outcomes (such as virtual try-on, demand forecasting, and personalisation) rather than pursuing every available AI application. 
  

• Build a modular, interoperable AI infrastructure that integrates data from product, supply chain, customer behaviour, and commerce systems to enable unified analytics and adaptability across workflows. 
  

• Combine AI-driven automation with human expertise, especially in areas requiring creative judgment, brand identity, or nuanced decision-making. 
  

• Establish robust governance frameworks that encompass data quality, privacy compliance, bias mitigation, and performance monitoring to ensure effective management of these key areas. 
  

• Focus measurement on business outcomes rather than vanity metrics, linking AI initiatives to conversion lift, return reduction, inventory efficiency, working capital improvement, and customer lifetime value.   

This disciplined, outcome-centric approach ensures AI investments deliver sustainable competitive advantage rather than temporary novelty gains. 

Conclusion: Building the Intelligent Growth Engines of the Next Decade 

The beauty and fashion industry stands at an inflection point. AI is no longer a niche technology experiment or a marketing gimmick. It is rapidly becoming structural infrastructure that can reshape how brands design, market, distribute, and sell. 

Organisations that embed AI across their commercial engine, aligning product discovery, demand sensing, supply-chain planning, and consumer experience, are building scalable growth engines. These firms will enjoy higher conversion, lower returns, leaner inventory, faster time to market, and greater resilience against demand volatility. 

The next generation of industry leaders will not compete solely based on products. They will succeed in intelligence. The brands that act now and treat AI as a core capability, versus those who view it as a luxury add-on, will capture the advantage with strategic clarity, disciplined governance, and scalable execution.