By Nathan Reiff
9 min read
Generative artificial intelligence (AI), a subgroup of AI that produces content ranging from text to music to images and more, has grabbed hold of public attention in recent years thanks to high-profile tools like OpenAI’s ChatGPT. The famous chatbot is known for writing screenplays and bedtime stories as much as it is for helping students to cheat at writing papers, but it has myriad other applications as well. While ChatGPT is among the best-known generative AI tools, it is far from the only one. What many of these systems have in common is their use of deep learning models to analyze vast amounts of data and then use that information to “create” new content based on a variety of user prompts.
Generative AI is upending a host of different industries, and the world of fashion is no exception. As we’ve seen in the introduction to this course, fashion and technology have been closely linked for centuries already. In recent years, fashion designers and brands have utilized the latest tech to continue to revolutionize—including incorporating non-generative AI and blockchain capabilities. Digital clothing available in the metaverse or sold as non-fungible tokens (NFTs) are prime examples, as are fashion items linked to augmented or virtual reality tech in some way.
Many experts believe that generative AI could be the most powerful tool yet to transform the fashion world. McKinsey & Co. analysts estimate that the apparel, fashion, and luxury sectors’ operating profits could see a boost of as much as $275 billion in the next three to five years alone thanks to this technology. But how does the fashion world use generative AI—and how might it continue to as this powerful tool continues to develop?
AI tools across industries are already being employed as partners in creation. Rather than turn over the design of new items entirely to an AI system, many enterprising designers are finding that teaming up with this tool is even more fruitful. AI can be used as a sounding board for ideas, capable of generating a wide array of creative options based on data ranging from past products, themes, inspirational images, and more.
AI can also help to take the unfinished work of a designer and realize it. A designer who has produced sketches of a new item, for example, can provide those sketches and additional prompts to a generative AI tool to have the AI offer a variety of 3-D realizations of a possible finished design. The designer can select one of those options for testing and production or can use the work that AI produced to help inform his or her continued progress on the design.
Another benefit to the use of generative AI in fashion is that it allows companies to customize products for individual consumers on a much larger scale than ever before. This has allowed firms like Germany’s Styleriser to work with customers to recommend particular clothing colors and styles based on analysis of customer images. Each buyer’s product can be personalized, helping to increase buyer confidence and minimize returns.
Tools to accomplish each of the above tasks already exist, developed by tech firms including Designovel and Fashable. With these AI offerings, designers—both major brands and up-and-coming individuals with relatively limited resources—are able to try mocking up a host of design variations without needing to dedicate the time and money to creating them in real life. This speeds up the design process while also drastically reducing costs.
A huge part of the $3 trillion fashion industry is marketing. This includes how manufacturers promote their products, surely, but it also involves the use of past purchasing trends, style developments, and consumer behaviors to help designers to make informed decisions. Fortunately, when it comes to analyzing and processing huge troves of data, AI is exceptionally well equipped.
AI may be used to comb through sales and purchasing behavior data to find past trends and also to attempt to predict how those trends will continue to evolve going forward. This data may include in-store behaviors, omnichannel information, broad consumer sentiment, and much more. Armed with a good idea of what their customers want, designers may be better able to craft products that will sell well. AI can even be used to automatically segment consumers by groups to help clothing makers to better tailor their marketing campaigns, and to personalize marketing content to individuals based on their specific buying trends and behaviors.
From the customer side, AI may play a role in shaping interaction with fashion companies and their products even when the consumer isn’t aware. Text-generating AI systems are capable of generating and modifying product descriptions and other ad copy in order to specifically target an individual customer. Similarly, buyers should anticipate that their experience of digital shopping should be increasingly governed by AI tools that add or remove steps from the consumer journey in a bid to maximize sales potential. But perhaps one of the most exciting innovations to the customer experience that AI can provide is the ability to try on products virtually and make personal style recommendations. Google announced in June 2023 that it had developed a generative AI tool to aid in virtual clothing fittings. The tool shows customers what particular pieces or outfits might look like based on dozens of models with a variety of body types.
From the business perspective, helping the customer to find colors, designs, and sizes that best suit him or her with the use of AI could mean a massive dent to expensive returns, which currently cost retailers up to 38% of an item’s original cost. Bodify is a startup aiming to aid customers in finding the right fit for a clothing item the first time around. The company uses user-submitted photographs, computer vision, and machine learning algorithms to estimate measurements and make recommendations of particular sizes and fits. Fit for Everybody is another company with similar goals, although its focus is on helping customers to measure themselves accurately. The data gathered through that process is fed back through AI analysis to help designers to better measure and assemble their products.
One of the biggest issues plaguing the fashion industry, and high fashion in particular, is counterfeiting. Across all industries, counterfeit product sales may account for about $600 billion per year, of which an estimated $50 billion might have otherwise gone to fashion companies in lost sales. The persistence of counterfeits of luxury items despite many years of attempts to dissuade or crack down on counterfeiters reveals just how profitable this criminal enterprise can be.
Fortunately, AI provides a new set of tools in the battle against counterfeit clothing and accessories. Accounting firm Deloitte has launched an AI tool called Dupe Killer for this express purpose. The AI system analyzes millions of photographs of clothing and accessories with the goal of being better able to recognize very subtle design elements that are unique to fraudulent reproductions. The tool can alert designers and brands when a company is unlawfully using its trademarked designs or logos, ideally helping them to pursue action against counterfeiters.
Wearable tech has seen a surge in popularity in the last several years and will likely top $42 billion in annual sales in 2023, according to analysis by eInfochips. This cutting-edge segment of the fashion industry already brings together technology with the clothing and accessories themselves, literally incorporating tech into items that customers can wear. Expect AI to expand the realm of what’s possible in this arena in the years to come. AI may even be able to help power smart fabrics that can react to body data and adjust things like airflow, temperature, stiffness, and more.
Non-generative AI has numerous applications in the fashion space as well. For instance, AI platforms can assist fashion brands and retailers in optimizing the layout of stores based on foot traffic, space available, and customer purchasing trends, among other factors. The same can be said for optimizing labor in stores to ensure that companies are best prepared for issues like potential theft. The possibilities for AI and fashion retailing are only beginning to emerge.
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