According to McKinsey, generative AI could add up to $275 billion to operating profits in the fashion, apparel, and luxury sectors over the next 5 years.1 AI in fashion promises creativity and efficiency by transforming unstructured data like text, images, videos into innovative outputs like 3D designs and virtual models.
Check out top 6 use cases of AI in fashion industry to learn about real-life examples and ways to integrating AI in fashion.
Improving design process with artificial intelligence
AI is offering innovative solutions to fashion industry to enhance the design process while maintaining a symbiotic relationship between human creativity and AI tools. The integration of generative AI in fashion provides extensive opportunities for fashion brands to innovate and optimize.
Most companies in the fashion sector rely on clothing designs made manually. However, creative AI can be an effective way to take over in situations like the pandemic when people can not work.
AI-enabled tools can create clothing designs by using data such as images from the brand’s previous offerings or from other designers, data regarding customers’ tastes (color and style choices), and current fashion trends.
Check out below video to see how the London College of Fashion is researching to find new ways to use AI for fashion design and production:
While extensive research is being done in this area, limited real-world applications of AI-enabled fashion designing can be observed, and all of the ones that exist are based on human-in-the-loop (HITL) models.
AI algorithms and data analytics in design
The design process traditionally relies heavily on human intelligence, intuition, and historical trends. By leveraging AI algorithms, fashion brands can collect and analyze historical data from a variety of sources, including social media platforms, fashion blogs, and eCommerce platforms. This analysis allows for the identification of emerging trends, while enabling designers to stay ahead in the fashion world.
For example, machine learning models can process vast datasets of past collections, customer preferences, and fashion trends to generate actionable insights. Natural language processing (NLP) can also be employed to extract key trends from customer feedback, ad campaigns, and product descriptions published in outlets.
Real-life example:
The German fashion platform Zalando and Google created project Muze, which uses machine learning to create fashion designs. The model gathers data regarding customers’ favorite textures, colors, and style preferences by asking a series of questions to create clothing designs. The project created 40,424 fashion designs within the first month.2
Improved production lines
Currently, the apparel manufacturing sector mostly relies on manual production processes with questionable working conditions for the workers.3 However, AI-enabled solutions are changing these trends by enabling automation in the apparel production sector.
AI can help overcome these ethical challenges by enabling automation. For instance, robotics can help automate risky or error-prone tasks in a manufacturing facility to decrease workload and improve worker safety.
Moreover, computer vision enabled with AI also has various applications in fashion production, including efficient quality assurance and predictive maintenance of equipment which reduces the downtime of the machines and ensures operational continuity.
Here are the ways AI can support production:
Demand forecasting and inventory management: With predictive analytics to analyze historical data, social media trends, and consumer preferences, AI powered services allow brands to predict demand more accurately. This helps minimizing overproduction, reducing excess inventory, and aligning production with real-time market needs.
Supply chain optimization: Fashion supply chains are complex as they involve raw material suppliers, manufacturers, logistics, and retailers. AI enhances supply chain management by:
- Tracking materials and inventory in real time to prevent bottlenecks.
- Analyzing logistics data to identify and eliminate inefficiencies.
- Improving supplier collaboration by monitoring compliance with sustainability and quality standards.
Check out supply chain management with AI use cases for more.
Real-life example:
Sewbo, a Seattle-based startup, is reshaping garment manufacturing by automating the sewing process. Their innovative approach involves temporarily stiffening fabrics with a water-soluble polymer, to enable standard industrial robots to handle and sew materials. This method allows off-the-shelf robots to work with various fabrics and sewing machines. The aim is to reduce costs, lead times, and waste in the apparel industry.4
Trend forecasting
Fashion trend forecasting is the process of predicting possible future fashion trends. Traditionally, fashion trend forecasters combine their fashion knowledge, intuition, and historical data to predict possible fashion trends. However, measuring the accuracy of trend forecasts is difficult, and you can not know how accurate they are.
In the current digital era, AI is used to accurately predict fashion trends using different types of data. Trend prediction can also be used to reduce wastage in the fashion and clothing sector by designing clothes people would actually want to wear. More accurate predictions can lead to leaner production and distribution cycles and less waste.
Real-life example:
Heuritech is a Paris-based fashion technology company specializing in AI-driven trend forecasting and demand prediction. The company utilizes advanced artificial intelligence to analyze over 3 million social media images daily, translating real-world visuals into insights for fashion and sportswear brands.
Their platform detects more than 2,000 fashion attributes, including prints, colors, fabrics, and specific product details to quantify and predict consumer demand. This enables brands to optimize their collections, align products with market trends, and reduce overstock by producing items that resonate with consumers.5
Improved fashion retail
AI-enabled technologies are widely used in fashion retail. The applications include:
Intelligent automation
Back-office tasks in retail, such as invoice creation, can be automated through intelligent automation. AI-powered systems can process large volumes of financial and transactional data, generating accurate invoices without the need for manual intervention.
This approach saves valuable time for retail staff, by allowing them to focus on more strategic activities, while also reducing errors and improving operational efficiency. Additionally, the automation of these repetitive tasks can cut costs associated with manual processes, therefore supporting retail operations and boosting productivity.
Inventory management and retail operations
AI-enabled computer vision systems play a key role in automating critical retail operations, including:
Inventory management: AI systems monitor stock levels in real-time, predict replenishment needs, and prevent overstocking or shortages.
Cashierless stores: AI-powered checkout solutions enable cashierless shopping, where customers can pick items and leave the store while AI systems automatically bill their purchases.
Robotic process automation in retail
RPA enhances retail efficiency by automating repetitive processes and providing smarter customer interactions. Key applications include:
Customer Relationship Management (CRM): AI chatbots and virtual assistants handle customer queries, process returns, and recommend products based on past interactions.
Marketing operations: RPA in marketing automates campaign management, such as sending personalized offers, segmenting customer data, and tracking engagement metrics.
Watch how H&M, one of the largest fashion retailers, leverages AI to improve its operations:
Real-life example:
Amazon Go’s “Just Walk Out” technology eliminates the traditional checkouts. To shop at an Amazon Go store, customers need an Amazon account and the Amazon Go app installed on a supported smartphone. Upon entering, customers scan a QR code from the app at the entry gate, which grants access and initiates the shopping session.
Inside the store, a network of cameras and sensors, combined with computer vision and deep learning algorithms, tracks the items customers pick up and return to the shelves. This system maintains a virtual cart for each shopper, accurately recording their selections without the need to scan individual products.6
Higher personalization
With AI systems analyzing extensive customer data and enabling customization, brands can now create experiences that cater to individual preferences while fostering customer engagement and loyalty.
Personalized marketing
Personalized marketing is essential to customer-centric strategies in the fashion industry, and AI tools play a pivotal role in its success. By analyzing vast datasets with purchase history, browsing behavior, and demographic information, AI can generate insights to craft highly tailored marketing efforts. Here’s how AI can help with personalized marketing:
Targeted recommendations:
AI algorithms analyze customer behavior to suggest products that align with individual tastes. For example, if a customer frequently browses for summer dresses, the system can recommend similar styles or complementary accessories.
On eCommerce platforms, personalized product suggestions appear on homepages or during checkout, which would increase the chances of purchases.
Email campaigns:
AI-driven systems can craft personalized email recommendations based on a customer’s unique style, past purchases, or seasonal preferences. For instance, a brand might send an email highlighting new arrivals in a color that the customer frequently shops for.
Virtual try ons:
Virtual try-on technology uses augmented reality (AR) to let customers digitally try on clothes, makeup, and other products. It replicates the in-store fitting experience, helping shoppers visualize items, make informed decisions, and enjoy a more engaging shopping experience.
- Improved customer satisfaction: Customers can see how products will suit them, increasing confidence and enhancing their shopping experience.
- Reduced return rates: By visualizing the right size, style, and color, shoppers make better-informed decisions, lowering the likelihood of returns.
- Increased sales and conversions: These customers are more likely to complete purchases.
- Enhanced brand loyalty: Personalized and interactive product exploration sets brands apart and fosters stronger customer connections.
Real-life example:
Warby Parker, an eyewear retail company, elevated eyewear shopping experience by introducing virtual try-on technology through its app.
Customers can virtually try on different frames, while the website lets them order up to five frames to try at home with free return shipping.
The app leverages computer vision to analyze face shape and skin tone and offers personalized fit recommendations for an enhanced shopping experience.7
Figure 1: Virtual try on with Warby Parker.
Increased sustainability
By integrating AI into their operations, fashion brands can achieve sustainability through smarter resource use, optimized supply chains, and waste reduction:
Predictive analytics to reduce overproduction
One of the biggest challenges in sustainable fashion is combating overproduction, which leads to excess inventory and textile waste. AI algorithms use predictive analytics to forecast consumer demand by analyzing historical data, social media trends, and market dynamics.
This reduces uncertainty and minimizes human error and also enables brands to produce only what is likely to sell. By optimizing production, AI helps brands prevent overstocking, thereby reducing waste and mitigating the environmental impact of unsold inventory.
Sustainable material sourcing
AI-driven systems allow selecting sustainable materials by evaluating factors such as environmental impact, ethical sourcing, and cost-effectiveness. These systems can assess raw material options and recommend eco-friendly alternatives, such as natural fibers or suppliers with strong compliance records.
This process can ensure that brands align with responsible sourcing practices and meet the expectations of environmentally conscious consumers.
Waste reduction in manufacturing
AI-driven systems can optimize production processes to minimize fabric waste. By analyzing data on production efficiency, material usage, and quality control, AI can identify areas where waste can be reduced.
This approach decreases the environmental burden of textile waste and also enhances cost-efficiency for fashion brands. With sustainability becoming a core focus, these waste reduction strategies are critical for balancing environmental and economic goals.
Further reading
- AI Use Cases & Applications
- Best Data Collection Services & Companies
- Top 12 Data Crowdsourcing Platform
- 5 AI Training Steps & Best Practices
- Manufacturing AI: Top 15 tools & 13 use cases & case studies
External Links
- 1. Generative AI in fashion | McKinsey. McKinsey & Company
- 2. How Zalando is merging fashion and code – Think with Google. ThinkwithGoogle
- 3. Fashion & Environment — SustainYourStyle.
- 4. https://www.sewbo.com/
- 5. Fashion trend forecasting & prediction with AI - Heuritech. Heuritech
- 6. Amazon Go stores: How the 'Just walk out' cashierless tech works.
- 7. Warby Parker.
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