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AI, Data Science and Machine Learning

AI in the fashion industry: Use cases for applying machine learning in fashion

USD $4.4 billion.

This is the forecast value of the global artificial intelligence (AI) market in the fashion industry by 2027, according to research firm Statista.

What is driving the rapid integration of AI in fashion retail? In the past two years, the need to migrate to digital services showed how fashion companies had to rely on data and automation to survive. In 2020 alone, established retailers like J.C. Penney, Neiman Marcus, and J.Crew closed shop because they could not anticipate disruptive trends.

The adoption of AI in fashion – from designing to manufacturing to distributing – is expected to become widespread over the next few years as more retailers understand the power of harnessing data for consumer customization and process optimization.

Application of AI in the fashion industry

To better understand how AI is transforming the way fashion is produced and consumed, let’s look at how data science is used in the fashion industry.

Personalized buyer experiences and virtual dressing rooms

As more and more customers switch to the convenience of e-commerce, retail apps will become more necessary than ever. Algorithms can make accurate recommendations based on data captured from customer buying patterns. Not only is it easy to browse through product categories, but clients are also given related items that complement their choices. Additionally, brands such as ASOS and Macy’s use an AI platform that layer clothes on top of potential buyers’ photos, which they upload to the app. The result is a seamless virtual dressing room.

This personalization addresses the limited options that most consumers face when visiting physical stores. Strategic demographic analysis can also help identify potential customers and their spending habits, resulting in better marketing campaigns that target the right audience.

Trends analytics and demand prediction

Through data analysis, algorithms can conduct fashion forecasting, including scanning the web for emerging and future trends. AI collects data from social media, fashion shows, search results, surveys, and consumer sentiments to better predict which clothes will sell to which customers. The algorithm then identifies recurring patterns, demographic preferences, and developing styles.

Data analytics addresses retailers’ main challenge in anticipating business opportunities. Social media platforms like TikTok and Instagram are often hotspots for fashion trends, but many companies fail to properly analyze and incorporate these developments into their business strategies, resulting in opportunity losses.

Supply chain optimization and process automation

An AI-powered supply chain ensures that retailers are on top of their manufacturing process – from sourcing designs to selecting materials to shipping products globally. Using a combination of interconnected devices such as sensors, cameras, and QR codes, software can now identify the most efficient way to cut fabric and deliver materials across factories.

An automated supply chain can lower electricity and labor costs, and reduces shipment delays. Operations are streamlined to efficiently replace fast-selling items, and new styles are rapidly developed and produced.

Challenges of implementing AI in fashion retail

There are three main challenges in integrating machine learning in fashion.

  • Lack of standardized and centralized data collection and processing. While many retailers have an online presence, they don’t have an intuitive application or software to collect data efficiently and create meaningful reports or narratives. While some fashion companies have an existing data- or knowledge base, they can be disorganised and not as updated as they should be.


  • Slow integration of automation in legacy processes. Many factories are still manufacturing clothes traditionally, resulting in inefficient production, unethical practices, and low output. Some companies are also hesitant to invest in automation because the initial capital is expensive, including staff training.


  • Slow adoption of cloud services and online platforms. Even with numerous companies offering software-as-a-service (SaaS) and platform-as-a-service (PaaS), many retailers still don’t know how to build optimized e-commerce sites and use data science to streamline their production processes.

How AI Superior can help

Are you looking to implement AI-based solutions in your fashion retail business but don’t know where to start? We can help identify which process you can automate using machine learning. Our services and solutions can address the challenges of implementing data science in your workflows, supply chains, and business strategies, including data collection and analysis. By taking into consideration all the factors that affect your retail business, we can tailor a customized AI solution that addresses all your needs. Contact us for any query or demo request.

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