AI: The New Fashion Guru
The recent news of Adobe introducing its first digital dress that brings fabric to life, Project Primrose, is a stark reminder of the rapidly evolving landscape of the fashion industry. The silver interactive dress, which utilizes generative artificial intelligence (AI), has the ability to dynamically alter its patterns, fabric color, and style while being worn by the individual. This innovative creation, powered by object recognition, algorithms, and machine learning, serves as a testament to the transformative potential of AI in fashion. While introducing the dress, Christine Dierk, the research scientist of the project, stated that “Fashion doesn’t have to be static. It can be dynamic and even interactive” (Adobe MAX, 2023, 03:03). In light of such innovations, the integration of AI in the fashion industry is not only enhancing creativity but also revolutionizing customer experience and sustainability.
In the modern world of fashion, creativity is continually advancing beyond the traditional confines of needle and thread. Through the use of AI, fashion designers can now cultivate ideas without the limitation of fabric, and other substantial choices. For instance, methods of generative AI fashion synthesis, notably generative adversarial network (GAN) based models, have surfaced as potent devices for producing new designs. These models possess the capability to “disentangle and manipulate shape, texture, and style representations,” giving designers the opportunity to swiftly and efficiently explore a diverse spectrum of design options (Guo et al., 2023, p.11). Leveraging these tools, different kinds of generative AI techniques have already been implicated in various areas of fashion design. Zalando and Google’s AI-human design collaboration, Project Muze, serves as one of the examples. Operating as cloud-based software, Project Muze collects information on customers’ preferred texture, color, and style inclinations through a series of inquiries to craft personalized clothing styles. Its core objective is to employ “machine learning algorithms to turn users’ personalities and interests into inspirations for unique designs” (Sharma, 2023, p.15). The platform pursues to bridge the divide between capturing emerging trend demands and the continuously evolving world of style. As the fast-fashion industry constantly evolves and renews itself, platforms like Project Muze save time for designers to see their design vision come to the surface. One illustrative implementation of this is the “Fashion X AI” fashion show, which took place in Hong Kong, on 19th December 2022. The show displayed “more than 80 outfits from 14 designers in the spotlight, all of which were created with the help of the artificial intelligence software AiDA” (Reuters, 2022, paras.2). AiDA is capable of generating twelve fashion designs in just ten seconds using raw sketches and color palettes. Fashion designer Mountain Yam, based in Hong Kong, has incorporated AiDA into his work over the last six months. He attests that “it not only saved his time but inspired him” (Reuters, 2022, paras.5). Considering all these transformative implications, we can draw the conclusion that AI is reshaping the fashion landscape, enhancing creativity, while providing designers with innovative tools to transcend traditional boundaries.
Yet, it is not only designers who are capitalizing on AI technology; customers are also reaping the advantages. Customer experience when it comes to fashion is complicated due to different body types, stylistic preferences, and budget restrictions. Personally, I often find it challenging to put together an outfit that suits both my body type and the specific purpose of the day. However, this problem can be solved, as AI has enabled “the automation of styling assistance, allowing the scaling of the feature that was once performed just by humans and affordable only to a few” (Evangelista, 2020, p.75). The AI fashion assistant can assist users in finding items that suit their style or align with current trends. It offers suggestions for clothing that complements their body type and provides advice on refreshing their wardrobe. For example, in 2017, Amazon launched a new fashion initiative known as Echo Look, with the aim of serving as a private stylist. The gadget is equipped with a built-in camera, microphone, and speakers. Users can use voice commands to capture photos of their outfits and share them with friends online. It also has “fashion recommendations by rating and compares the user’s outfits through a feature called StyleCheck,” which blends machine learning with advice from fashion experts (Evangelista, 2020, p.76). One can scroll through multiple outfit choices on the program and choose the one they prefer the most. Even so, the challenges for customers extend beyond just selecting and styling an outfit. When I search for new clothing items in stores, it is a struggle to discover the ideal piece that fits well and aligns with my aesthetic preferences. In 2019, Sportswear brand Nike took this matter into account and released the Nike Fit feature. This device is a scanner that measures one's feet and recommends the perfect fit. According to the brand, “60% of people at any given time are using the wrong shoe size.” Research indicates that this application is particularly valuable because finding the right fit for footwear can be a complex task (Evangelista, 2020, p.86). From these case studies, we can conclude that the synergy between AI and individual preferences is reshaping the way we navigate style and fit.
This transformative impact of AI is not just limited to enhancing customer experience and personalization. When we shift our focus from the consumer’s perspective to the production side of the fashion industry, we encounter a different set of challenges and opportunities. AI implications in fashion through the lens of designers and customers are indeed compelling. However, as we delve into manufacturing, the issue of sustainability comes to the forefront. Fast fashion brands, frequently criticized for their role in waste and overproduction, exemplify the complexity of this challenge. For instance, known as one of the pioneers of fast fashion, Zara produces “12,000 new designs and manufactures more than 450 million clothing items every day” (Lai, 2022, paras.2). This is where AI can step in to reduce overproduction and optimize supply chain. It can ingest vast amounts of data to create actionable decision models that leverage years of experience but are also capable of processing billions of data points. (Linton, 2023, paras.4) Some brands, such as Ralph Lauren and H&M, are already using AI “to refine their range in line with consumer demand, operate more efficiently, and therefore drive significant value” (Consultancy.uk, 2023, paras.4) In support of this statement, OC&C study predicts that the fashion industry powered by AI can reduce excess production by 15% (Consultancy.uk, 2023, paras.5). However, as it analyzes data and predicts upcoming trends and customer demand to optimize the supply chain, implementing AI project can be complex and costly for fashion companies. Without proper execution, these projects can come with a carbon footprint that undermines sustainability goals. (Rodriguez & Vargas, 2023, paras.4). Nevertheless, the long-term benefits often outweigh these costs. AI tools are helping companies meet their sustainability goals by optimizing resource usage, reducing waste, and improving efficiency (Thomas, 2023). For instance, a fashion company, Sorabel, “leverages AI to maintain low inventory risk while delivering fashionable items at affordable prices” (Simon, 2020, paras.4). Therefore, with AI’s potential to optimize production, reduce waste, and align with consumer demand, it stands as a powerful tool in transforming the fashion industry towards a more sustainable future.
In conclusion, the implication of AI in the realm of style is far from a mere novelty; it signifies a profound evolution in the way fashion is conceived, created, and consumed. AI’s ability to enhance creativity, personalize customer experiences, and address sustainability challenges is undeniable. From dynamic, AI-generated designs to AI-driven styling assistance, the fashion landscape is evolving at an unprecedented pace. Furthermore, AI’s potential to optimize production and reduce waste holds the promise of a more sustainable future for the industry. As AI continues to revolutionize every aspect of fashion, from the runway to the wardrobe, it serves as a testament to the ever-expanding boundaries of innovation and creativity in the fashion industry.
References
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