The early stage of AI in the fashion industry 

The integration of Artificial Intelligence (AI) first entered the fashion industry in the early 2000s. Initially, such data was crucial for improving supply chains and managing inventory. These early applications of AI were implemented in forecasting demand, optimizing inventory levels, and speeding up production planning. Below are notable examples from that period, including the type of AI used, the departments involved, and how AI helps optimize operations. 

Case studies 

1. Inditex (Zara) – Real-Time Analytics for Fast Fashion 

Type of AI Used: Inditex’s flagship brand Zara leveraged advanced data analytics and proprietary algorithms (an early form of AI) to enable its “fast fashion” model. While not labeled “AI” at the time, Zara’s IT systems automatically analyzed point-of-sale data, tracking each item’s sales in real time​. This can be seen as a data-driven decision-support system for forecasting and inventory.  

Applied Departments: Zara integrated data analytics into inventory management, design, and distribution. Store managers used handheld devices to transmit sales trends and customer feedback to headquarters daily. Designers and product managers then used this data to decide which styles to produce, while the central distribution center used IT algorithms to allocate stock to stores based on local demand​. 

Role in Optimization: The AI-driven analytics at Zara enabled a pull-based supply chain that responded rapidly to actual customer demand. By creating new designs based on current sales and trend data, Zara minimized overstock and markdowns. It produced garments in small batches and restocked stores twice a week, which reduced inventory holding costs and ensured styles that didn’t sell were quickly replaced​. 

This data-centric agility allowed Zara to achieve very short lead times and high inventory turnover, giving it a competitive advantage in speed and responsiveness​ (EPCS, n.d.).

2. Nike – Machine Learning for Demand Forecasting 

Type of AI Used: Nike invested in an AI-driven demand forecasting and supply chain planning system (provided by i2 Technologies) around 2000–2001. This system used advanced machine learning and optimization algorithms to forecast demand and schedule production​. In essence, it was an early application of predictive analytics (neural-network-based and rule-based models) for inventory optimization. 

Applied Departments: The technology was applied in supply chain and inventory management departments – specifically in production planning, demand forecasting, and distribution. Nike’s footwear division fed sales and market data into the i2 software, which automatically generated production and inventory plans across factories and warehouses​

Role in Optimization: The goal was to optimize inventory levels by aligning production with predicted demand, thereby reducing excess stock and avoiding stockouts. Initially, Nike’s implementation faced challenges – in 2001 the new AI-driven system produced skewed results, leading to excess inventory of some models and shortages of others​. (Nike missed sales targets and blamed the analytics software for “led to excess inventory and order delays”​.) However, after recalibrating data and business processes, Nike continued using these AI-powered tools. By 2004, the system was reportedly stabilized, helping Nike streamline its supply chain and respond faster to trend changes  (CNET, 2001).

In sum, Nike’s early use of AI in forecasting highlighted both the potential and the need for careful implementation – ultimately improving inventory accuracy and service levels once the kinks were worked out. 

3. Under Armour – Demand Forecasting Algorithms 

Type of AI Used: Under Armour, then an up-and-coming sports apparel brand, adopted a demand forecasting solution in 2004–2005 that utilized AI-driven analytics. The company implemented Demand Solutions forecasting software, which uses multiple statistical and machine-learning algorithms (e.g., neural networks, time-series models) and automatically selects the best-fit model for each product’s sales pattern​. This approach “mimics the way people make purchasing decisions” by applying the most appropriate forecasting formula item by item​. 

Applied Departments: The forecasting system was used by planning and supply chain teams, specifically in order planning, demand capture, and inventory management. It replaced Under Armour’s manual Excel-based forecasts with an automated, AI-enhanced process​. The sales, planning, and production departments all used the improved forecasts to guide production orders and inventory stock levels. 

Role in Optimization: By deploying this AI-driven forecasting tool, Under Armour significantly improved forecast accuracy and agility. Better demand predictions allowed the company to fine-tune its product mix and production volumes in response to market changes​. This meant popular styles and sizes were less likely to sell out, and unpopular inventory was minimized. The CIO of Under Armour noted that having quality data and forecasts helped the company keep up with rapid growth in the moisture-wicking apparel market​.  

In summary, AI-based demand sensing helped Under Armour reduce its reliance on guesswork, optimize its inventory levels, and ensure it could meet customer demand without overproducing stock (Supply & Demand Chain Executive, 2005). 

The Rise of AI in Fashion front-end applications

As technology advanced, AI played a larger role in the fashion industry. By the 2010s, the use of AI expanded beyond back-end processes to front-end applications, applied across the fashion value chain from design and customer experience to marketing and sales (Deconstructing AI, 2024). Companies like EDITED, Stitch Fix, and 3DLOOK started with relatively simple AI-driven approaches but have since advanced their capabilities to reshape how fashion brands forecast trends, personalize styling, and optimize fit (Startup Fashion, 2023). 

Case studies:

One of the early companies, EDITED, founded in 2009, AI was primarily focused on tracking sales data, monitoring competitor pricing, and identifying discount strategies. These early analytics helped brands make better inventory and pricing decisions, but they were largely reactive, relying on past sales data rather than predicting future trends. Today, EDITED has evolved into a real-time AI-driven trend forecasting tool, using machine learning, predictive analytics, and computer vision to scan millions of fashion products, runway collections, and social media images. Now, rather than simply tracking pricing, EDITED can predict which colors, fabrics, and silhouettes will dominate future seasons, allowing brands to stay ahead of market trends (EDITED Official website).

Similarly, Stitch Fix, founded in 2011, started with basic customer segmentation, where AI sorted users into broad fashion categories based on surveys, purchase history, and demographic data. While this provided some level of personalization, it was limited in depth, and human stylists still played a major role in selecting clothing. Over time, Stitch Fix integrated deep learning and hybrid AI-human decision-making, significantly enhancing its recommendation engine. Today, its AI system analyzes customer feedback, past purchases, and even external fashion trends to refine outfit suggestions. Unlike its early days, Stitch Fix’s AI doesn’t just categorize customers into broad groups—it continuously learns individual preferences, ensuring that recommendations become more accurate and personalized over time (TIANLIN, 2023), (Stitch Fix official website), (PYMNTS, 2025), (Marr, 2024).

On the other hand, 3DLOOK, founded in 2016, initially worked with basic measurement-matching AI, which helped users find their clothing size based on predefined body measurement datasets. This approach was useful but limited, as it still relied on static size charts rather than personalized data. Now, 3DLOOK uses advanced computer vision, deep learning, and pose estimation AI to create precise 3D body models (Mercaux, 2023). Their YourFit solution, launched in 2021, combines photorealistic virtual try-on functionality with accurate, data-driven size recommendations Instead of just matching users to existing sizes, its AI dynamically analyzes body proportions and posture, improving size recommendations and reducing return rates for online shoppers (3DLOOK official website), (Just Style, 2021).

These advancements highlight how AI has transitioned from being a supporting tool to a key driver of innovation, making fashion more data-driven, efficient, and consumer-centric technology that would later transform the fashion industry. 

In 2025, AI technologies are set to drive significantly greater breakthrough innovations than ever before.

Several fashion brands have already integrated AI tools to enhance their design and business operations. For instance, Tommy Hilfiger collaborated with IBM and the Fashion Institute of Technology to develop AI-assisted trend forecasting and design innovation (Fashion Network, 2018), (FIT Newsroom, 2018). Brands like H&M and Nike use AI to generate unique clothing patterns, predict emerging trends, and optimize material usage, helping to reduce waste while improving production efficiency (Modelia, 2025), (World Fashion Exchange, 2024). A standout example is PUMA’s AI-driven Inverse sneaker, developed through a collaboration between AI and human designers (Design Scene, 2024), (Hypebeast, 2024). The final sneaker, featuring a bold red mesh upper, a structured midsole cage, and ProFoam cushioning, demonstrates how AI can serve as a tool to inspire human creativity (PUMA, 2024). Additionally, Gucci and Prada have incorporated computer vision and generative AI to create virtual fitting rooms (IRJMETS, 2024) and hyper-personalized shopping experiences, reshaping the way customers interact with fashion (Digital Defynd, 2024). 

Case studies:  

Brands like Zara, H&M, and Gucci use AI for trend prediction and consumer insights, while Tommy Hilfiger’s collaboration with IBM AI highlights early experimentation.  

​The fashion industry is increasingly leveraging Artificial Intelligence (AI) to enhance various aspects of design, production, and customer engagement. Two notable examples are H&M's integration of AI for trend forecasting and inventory management, and Tommy Hilfiger's collaboration with IBM and the Fashion Institute of Technology (FIT) to incorporate AI into the creative design process (Arthur, 2018).

1. H&M's Integration of AI 

H&M has strategically implemented AI technologies to optimize operations and better align with consumer preferences. These technologies primarily include machine learning, predictive analytics, and recommendation systems, which collectively enhance various aspects of the business. 

     Demand Prediction and Inventory Management:
 H&M uses supervised machine learning algorithms to forecast demand by analyzing historical sales data, seasonal patterns, market trends, and external variables like weather. These predictive analytics tools help the company tackle issues such as overstocking and understocking by anticipating product demand at a granular level. This not only reduces financial losses tied to excess inventory but also supports sustainability efforts by minimizing waste (DigitalDefynd, 2023). 

     Personalized Shopping Experiences:
 To improve customer engagement, H&M employs recommendation engines based on both collaborative and content-based filtering methods. These AI systems analyze browsing behavior and purchase history to provide personalized product suggestions, which increases customer satisfaction and boosts loyalty. 

     Supply Chain Optimization: 
Using predictive AI models, H&M forecasts regional demand months in advance, enabling precise product planning and efficient distribution. This ensures that the right products are stocked at the right locations, improving supply chain responsiveness, and reducing delays (Medium, 2023), (Digital Defynd, n.d.). 

2. Tommy Hilfiger's Collaboration with IBM and FIT version 2

In 2018, Tommy Hilfiger launched the "Reimagine Retail" project in collaboration with IBM and the Fashion Institute of Technology (FIT). This initiative represented one of the early experiments in applying AI directly to the creative design process. The project integrated computer vision, natural language processing (NLP), and early-stage generative AItools to support design ideation (FIT Newsroom, 2018)

     AI-Driven Design Inspiration:
 FIT students accessed IBM’s advanced AI capabilities, including deep-learning-based computer vision, to analyze over 15,000 Tommy Hilfiger product images and more than 600,000 runway photos. This technology extracted key silhouettes, colors, and patterns from vast visual datasets, offering data-informed inspiration aligned with the brand’s design identity. Simultaneously, IBM’s natural language processing tools scanned trend articles, blogs, and social media content to detect emerging consumer themes and sentiments, feeding back into the concept development phase (FIT Newsroom, 2018; Analytics Vidhya, 2018). 

    Innovative Design Outcomes:
 One standout result was a plaid tech jacket featuring interactive, color-changing fibers, designed to respond to real-time voice and social media inputs. This concept demonstrated how AI can influence not just inspiration but also garment innovation through the application of generative design algorithms (IBM, 2018).

     Educational Advancement: 
Beyond design outputs, the collaboration also served an educational purpose, empowering the next generation of designers with the skills to combine AI technology with traditional fashion design techniques. It illustrated how AI could augment—rather than replace—human creativity by serving as a collaborative design partner (PR Newswire, 2019). 

These case studies from H&M and Tommy Hilfiger illustrate how different types of AI—ranging from machine learning and predictive analytics to computer vision and NLP—are transforming both the operational and creative sides of fashion. While adoption in operational areas like inventory and trend forecasting is growing rapidly, the use of AI in core design remains in early stages, presenting both exciting opportunities and ethical challenges.