Industry Technologies
The fashion industry is familiar with experimenting with technological frontiers. Some of the most significant technological breakthroughs are laser cutting, computer-aided design, and more recently, the use of 3D printing in early 2010.The fashion industry has experimented with basic AI and other cutting-edge technologies. One exaGame-changing technologymple is the Gucci Garden, the label’s collaboration with virtual world platform Roblox in May 2021 to celebrate the brand’s centennial.
Game-changing technology
In 2021, fashion companies invested between 1.6 and 1.8 percent of their revenues in technology. By 2030, that figure is expected to rise to between three and 3.5 percent.
Generative AI could become a game-changer for the fashion industry, adding between US$150 and US$250 billion to operating profits within three to five years. While the fashion sector has only started integrating AI, the opportunities and challenges it presents are evident across all business processes.Generative AI could help fashion companies improve their processes, bring their products to the market faster, sell more efficiently, and improve customer experience. Generative AI could also support product development by analyzing large social media and runway show datasets to identify emerging fashion trends.
Estée Lauder Companies and Microsoft have teamed up to open an in-house AI innovation lab for identifying and responding to trends, informing product development, and improving customer experiences.
Designers could use AI to visualize different materials and patterns based on past consumer preferences. For example, the Tommy Hilfiger Corporation is collaborating with IBM and the Fashion Institute of Technology in New York on the Reimagine Retail project, which uses AI to analyze consumer data and design new fashion collections.
Designers could use AI to visualize different materials and patterns based on past consumer preferences. For example, the Tommy Hilfiger Corporation is collaborating with IBM and the Fashion Institute of Technology in New York on the Reimagine Retail project, which uses AI to analyze consumer data and design new fashion collections.
AI and sustainability
AI helps in creating more sustainable fashion practices by optimizing the use of resources, recycling materials, and reducing waste through more precise manufacturing processes and efficient supply chain and inventory management. For example, H&M uses AI to improve its recycling processes, sort and categorize garments for recycling, and promote a circular fashion economy.
The House debuts the Gucci Garden inside the historic Palazzo della Mercanzia (Bologna, Italy), which housed the Gucci Museo. Conceived by creative director Alessandro Michele, the newly designed space features a store with one-of-a-kind items, the Gucci Garden Galleria exhibition rooms curated by critic Maria Luisa Frisa.
AI can improve operations and supply chain processes by optimizing inventory management, predicting sales based on historical data, and reducing overstock and stock-outs. Brands like Zara and H&M already use AI to control supply chains, promoting sustainability by optimizing stock levels and reducing waste. Zara also introduced AI and robotics into their retail stores to speed up online order pick-ups.
AI pitfalls
Fashion companies should be prepared to manage the associated risks with new technologies, particularly regarding intellectual property, creative rights, and brand reputation. One of the primary issues is the potential infringement of intellectual property related to training data.
GenAI models are trained on vast design datasets, often containing copyrighted works. This can lead to legal disputes over originality and ownership. A related risk is bias and fairness in Generative AI systems, which may present reputational challenges for brands that rely on the technology.
The ambiguity surrounding creative rights in the age of AI is another concern. It’s challenging to determine who holds the creative rights to a design, whether it’s the designer who conceptualized the idea, the developer who built the AI, or the AI itself. This ambiguity can dilute the authenticity of a brand’s creative expression, potentially harming its reputation if consumers perceive the brand as less innovative or authentic.
GenAI models are trained on vast design datasets, often containing copyrighted works. This can lead to legal disputes over originality and ownership. A related risk is bias and fairness in Generative AI systems, which may present reputational challenges for brands that rely on the technology.
The ambiguity surrounding creative rights in the age of AI is another concern. It’s challenging to determine who holds the creative rights to a design, whether it’s the designer who conceptualized the idea, the developer who built the AI, or the AI itself. This ambiguity can dilute the authenticity of a brand’s creative expression, potentially harming its reputation if consumers perceive the brand as less innovative or authentic.
