AI, The Next Frontier for Fashion Transformation

Introduction

The fashion industry is undergoing a rapid transformation as it adapts to the changing demands and expectations of consumers and stakeholders. One of the emerging technologies that could reshape the sector is generative artificial intelligence (AI), which consists of algorithms that can create new content from various forms of data, such as audio, code, images, text, simulations, and videos.

?AI has the potential to revolutionize many aspects of the fashion value chain, from product innovation to marketing to sales and customer experience. It can enhance the creativity and efficiency of fashion professionals by enabling them to codesign with AI, generate realistic 3-D models and virtual campaigns, and personalize products and services for customers. It can also help brands bring their collections to market faster, optimize their inventory and pricing, and improve their sustainability performance. However, AI is still a nascent technology that poses significant challenges and risks for the fashion industry. It requires large amounts of data, computing power, robust governance, and ethical frameworks to ensure responsible and fair use. It also raises questions about the ownership and protection of intellectual property rights and the impact on human creativity and agency. Therefore, fashion executives need to carefully assess the opportunities and trade-offs of AI and develop clear strategies and capabilities to harness its potential while mitigating its pitfalls. This article explores some of the most promising use cases of AIin fashion, focusing on product innovation, marketing, sales, and customer experience. I also provide practical guidance on how executives can start with AI and what they need to consider.

?Development, Innovation, and Marketing

AI can enhance the creativity and efficiency of fashion designers by enabling them to codesign with AI agents that can generate novel and diverse designs from various forms of data. For instance, AIcan analyze real-time trends and consumer preferences from videos on social media or other unstructured data sources and provide insights and inspiration for the next season's collection. Fashion designers can also input their sketches and specifications, such as fabrics, colors, and patterns, into a AIplatform that can produce multiple design options, allowing them to experiment with different styles and looks. This can lead to innovative, limited-edition products involving collaborations between two brands. Moreover, AI can enable personalized product design for individual consumers, such as eyeglasses customized based on facial recognition technology. This scenario is not a distant future but a present reality. In December 2022, a group of Hong Kong-based fashion designers from the Laboratory for Artificial Intelligence in Design (AiDLab) showcased their generative AI-supported designs in a fashion show. Several tech companies, such as Cala, Designovel, and Fashable, offer tools that allow fashion designers to leverage AIto generate new ideas, explore various design variations without creating costly samples, and speed up their processes.

?AI can support marketing executives and agencies to develop and execute effective campaign strategies, produce engaging content, and create realistic virtual models for various marketing channels—and do it quickly. Creating viral marketing content can often require having a large volume of content. For example, on TikTok, there is no single formula for success, but the more content you create, the higher your chances are of becoming a trending topic and increasing brand awareness and sales. Marketers can generate short-form videos for TikTok or other social media platforms using a generative AI-powered video platform, saving time and costs for creating social-media content. AIcan also analyze patterns and trends in viral content and create new content that matches the marketer's specifications. These applications can help in-house marketing teams manage their workloads while reducing their dependence on external creative agencies. However, marketers need to be careful with this approach, as replicating what other brands have done can undermine the unique identity and value proposition that a brand has built over the years. Several start-ups, such as CopyAI, Jasper AI, and Writesonic, are leading the way in personalized marketing at scale through generative AI. Using these tools, a marketer's daily tasks could look like this: they could select the type of content they want to create, whether it is an email, a blog post, or something else; provide a prompt describing their objective; and specify the target audience and other parameters, such as tone, that ensure the marketing communications are consistent with the brand. The AI tool then generates several options for the marketer to choose from. These tools are helpful for lower-funnel marketing channels rather than more prestigious brand-building communications. Marketers still need to prompt and edit the work.

?Sales and consumer experience

AI can enhance sales and consumer experience by enabling intelligent AI agents to interact with customers and provide personalized services and recommendations. For instance, generative AI-powered chatbots can use natural-language processing to understand and respond to complex customer inquiries in multiple languages, reducing customer service wait times and improving response quality. AIcan also support luxury brands in "clienteling," a retail strategy that builds long-term relationships with high-value customers to increase purchases and loyalty. (For example, luxury boutiques can achieve conversion rates of 60 to 70 percent through appointment-only shopping. AI-powered tools can help sales associates engage with customers through various messaging platforms or texts, even when not working. They can also provide coaching, personalization, and analysis for sales associates based on consumer profiles and real-time online interactions.

?In July 2022, Stitch Fix announced that it was experimenting with GPT-3 and DALL-E 2, the text-to-image AI generator, to boost sales and customer satisfaction by providing better styling services. These generative models are being tested to help stylists quickly and accurately interpret large amounts of customer feedback and select products that customers would be more likely to buy. For example, the AI tool could analyze hundreds of text comments, email requests, product ratings, and online posts from a customer. If the customer frequently mentions the "great fit" and "fun color" of a particular type of pants, DALL-E could generate images of similar pants that the customer would probably like. The stylist could then find matching items in Stitch Fix's inventory and recommend them to the customer. Virtual try-ons are another example of how AIcan improve sales and consumer experience. Veesual, a Paris-based company, enables virtual try-on integration for e-commerce fashion brands, allowing customers to choose their models and pick clothes to try on.

?Getting started

AI is an exciting technology but comes with significant challenges and risks. Fashion leaders should not rush to adopt it without careful consideration but should also not ignore the opportunities it offers. Here are some steps leaders can take to start exploring generative AI.

Fashion leaders should identify where AI can create the most value for their businesses. They should start by mapping out the areas—such as creative design, merchandising, runway campaigns, or clienteling—that could benefit from generative AI. They should prioritize the AIuse cases they want to pursue based on their impact and feasibility. Some impact measures include improving customer satisfaction scores and reducing customer service wait times. Once the value is defined, use cases should also be evaluated based on how easy they are to implement; this will depend on factors such as the team's technical skills. Then, teams should build a short-term road map to test and validate these use cases. They should also think about their long-term goals, such as how to build a generative-design platform that can be updated and used by designers for every season. AI is not a toy but a powerful tool. Fashion executives should intentionally build tools that can deliver value rather than experimenting with existing tools randomly.

In a previous article, we discussed some risks of using generative AI. One is the legal uncertainty around generative AI's use. Designers may face accusations of creating derivative works and copycat designs. The ownership and protection of intellectual property rights and creative rights for AI-generated results, which may be based on multimodal data sources such as other designers' past collections, still need to be clarified and may vary depending on each case. Another risk is bias and fairness in AI systems, especially around limited data sets, which may pose reputational challenges for brands that rely on the technology. For example, if an image-generating tool produces an advertising campaign with inappropriate or offensive images that are then shared globally, a brand's reputation could be damaged. And blaming the company's AI for the mistake may not appease consumer anger. There is also the risk that AI employees must be fully aware of its limitations and may need to check for errors introduced by the technology. In this case, businesses must train employees regularly and provide them with the necessary resources to understand technology use. While risks are inevitable, executives can mitigate their potential impact by establishing a process to address risk, ethics, and quality assurance.

?AI tools could add value to various business areas, so educating and training employees—including designers, marketers, sales associates, and customer service representatives—on how to use the technology is essential. Some businesses have already introduced AI-focused training. For example, Levi Strauss launched a machine learning boot camp in 2021 to train non-tech employees on how to use machine learning in the company's design process. Employees who complete the program create new AI tools relevant to their work. One of Levi's goals with the program is to increase the diversity of employees with tech knowledge so that the company can uncover problems that employees from traditional technology backgrounds might need to be aware of. Fashion businesses will need to invest in their workforce when it comes to using generative AI, but they do not have to build applications or foundation models themselves. Instead, fashion leaders can partner with AI businesses and experts to move fast. A fashion executive might partner with a company (such as Microsoft or OpenAI) that provides new technology or a partner that offers support capabilities (such as cloud computing or APIs).

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