March 12, 2025
Managing Partner
Generative AI can unlock new avenues for creativity, personalization, and operational efficiency in fashion, positioning companies to compete more effectively and serve customers innovatively.
Generative AI can boost productivity across the fashion industry, accelerate time to market, and elevate the customer experience. Although this technology remains in its early days, now is the ideal moment for fashion businesses to begin exploring its possibilities.
This season's fashion weeks have concluded in London, Milan, New York, Kansas City and Paris. Immediately following runway shows, brands focus on production and sales while simultaneously planning the following season. Shortly, designs could easily blend a director's creative vision with the power of generative artificial intelligence (AI). That combination might bring clothes and accessories to market at incredible speed, fuel more efficient sales, and deliver a better overall customer experience.
By now, you have likely used ChatGPT, the AI chatbot that captured global attention and launched a digital race to build competing platforms. ChatGPT is one high-profile example of generative AI, a broader technology that can create new content such as images, audio, code, text, simulations, and videos. Instead of merely identifying and sorting information, generative AI produces new outputs by leveraging foundation models, which are deep learning models capable of simultaneously managing several complex tasks. GPT-4.5, 4o, and DALL-E are examples of such models.
Although the fashion industry has experimented with basic AI and other frontier innovations—think metaverse initiatives, nonfungible tokens (NFTs), digital IDs, and augmented or virtual reality—it has not yet delved deeply into generative AI. Admittedly, the technology became widely accessible only in recent months, and it still has issues such as bugs and occasional inaccuracies. Yet indicators suggest it will improve rapidly, potentially changing many aspects of business. According to Santiago & Company analysis, Gen AI has the potential to contribute between $150 and $275 billion to the apparel, fashion, and luxury sectors' profits over the next three to five years.
The technology is already revealing fresh space for creativity, from codesigning garments to speeding up content creation. By processing various forms of "unstructured" data—raw text, videos, and images—generative AI can produce new media ranging from entirely written scripts to 3D concepts and virtual models. While it remains early in its development, certain high-impact use cases for generative AI are emerging, particularly in product innovation, marketing, and sales and customer experience. You can read more about this in our last article titled: "The Future of Luxury: How AI Technology is Revolutionizing Premium Brands". Compared with other areas of the fashion value chain, these functions may be more feasible places to begin adopting the technology in the short term.
This article highlights the most promising use cases and suggests the next steps for executives ready to experiment while also considering potential risks. Generative AI represents more than mere automation—it offers augmentation and acceleration. It empowers designers, marketers, and other professionals to quickly complete tasks, freeing them to focus on what only people can do. It can also enhance systems that cater to customer needs. The question is: where should leaders begin?
Fashion businesses can deploy foundation models and generative AI throughout their value chain, though certain areas may show quicker gains. For instance, merchandising and product teams can transform sketches, mood boards, or written descriptions into detailed, high-resolution visuals. Designers can collaborate with AI agents that draw from past product lines and an ever-expanding collection of imagery to spark new ideas or create variations on existing designs. Companies may also customize products at scale, such as tailoring eyewear to suit an individual's facial structure.
In supply chain and logistics, generative AI can compile data to support supplier negotiations or power robotic automation in warehouses. It can track inventory through real-time analytics and personalize return options based on each shopper's preferences. For marketing, the technology can interpret unstructured data (including in-store consumer behavior or social media discussions) to predict trends, then help teams generate personalized content and accelerate campaign development.
Digital commerce and consumer experience also stand to benefit. Teams can produce compelling sales descriptions by examining what worked in past top-performing posts, then tailor product pages and promotions to each visitor's profile. Brands can refine virtual try-on tools so customers can visualize how clothing or accessories fit. Meanwhile, AI-enabled bots or assistants may address complex inquiries, possibly in multiple languages, boosting service quality and response times.
For stores, generative AI can simulate a variety of layout options and staffing models using parameters like foot traffic and local demographics. It can also assist store associates through augmented reality by relaying product details, inventory insights, and real-time recommendations. Lastly, generative AI can offer sales associates guidance for clienteling and create custom employee training resources in organizational and support functions. Self-service systems can become more advanced, automating human resources, finance, or legal department tasks. Altogether, these capabilities can reshape how apparel brands create products, market them, and interact with consumers.
Historically, fashion houses drew inspiration from trend reports and market analysis when planning for the next season. Now, generative AI can collect and analyze unstructured data—such as social media videos—to identify emerging styles and inform design. Directors and their teams could upload sketches and detail preferences, like fabrics or color palettes, into a generative AI platform that automatically produces a range of designs, allowing them to explore countless looks with ease. The team then refined the resulting ideas, preserving the brand's signature sensibility.
Such technology also supports unique, limited-edition releases and cross-brand collaborations. Certain products, for instance, eyeglasses, might be tailored to each individual's dimensions and tastes by scanning a customer's face via AI. This approach is already moving from concept to reality. In December 2022, a group of Hong Kong-based designers at the Laboratory for Artificial Intelligence in Design (AiDLab) introduced a runway show that integrated generative AI capabilities. Tech firms like Mercer (Formerly: Cala), Designovel, and Fashable offer platforms that allow designers to experiment with endless variations before producing a single physical sample. For beauty brands, generative AI may speed product formulation and reduce the need for multiple lab tests.
Generative AI advantages marketing teams by helping them develop campaign concepts, produce digital content, and generate virtual influencers or avatars for diverse channels. This agile creativity can be particularly valuable on platforms like TikTok, where quantity sometimes matters as much as quality in driving viral success. With a text prompt, generative AI may generate short-form videos at scale, making it more cost-effective to produce social content in-house, although marketers should guard against diluting brand identity by over-relying on AI-driven replications of trending posts.
Another opportunity lies in individualized communication. Recent Santiago & Company research shows that companies that excel at personalization can boost revenues by 40 percent compared with those that do not. Several start-ups—CopyAI, Jasper AI, and Writesonic, among others—lead the way in creating personalized marketing through generative AI. A marketer might specify their desired content type (email, blog post, or other), outline the target audience, and define a suitable tone. The tool then delivers a variety of draft options, which the marketer can refine further. In most cases, these tools work best for direct-response campaigns that encourage immediate action rather than for prestige branding, which often requires a more subtle approach.
Generative AI-powered chat tools have progressed beyond traditional methods of scripted automation. These new systems can interpret queries more accurately and respond more naturally, which bodes well for customer support in the future. Most current AI chatbots still make mistakes and require checks to ensure correct information. In the long run, however, generative AI could handle complex requests, including those in multiple languages, and help reduce wait times. Several services now offer generative AI "representatives" that manage client questions across emails, text messages, and brand websites.
Luxury brands may also find generative AI helpful for "clienteling," building relationships with loyal high-end customers who are open to spending more. Traditionally, sales associates nurtured these relationships through personal messages, but the strategy depends heavily on each associate's availability and memory. Generative AI can bridge these gaps by providing continuous dialogue and personalized suggestions, alerting associates to new items a customer might like, and even analyzing shopper preferences in real-time.
Stitch Fix offers one example. In mid-2022, the apparel retailer experimented with GPT-3 and DALL-E 2 to boost customer satisfaction and sales. The AI can sift through vast quantities of written feedback—from comments and emails to social media posts—and propose items that match each client's positive reactions. A stylist then searches the inventory for similar pieces, potentially raising conversion rates. Virtual try-ons provide another route to improving the shopping experience. Paris-based Veesual, for instance, gives e-commerce customers the option to pick a model and see how different garments might look on that model's body.
Businesses should be cautious about deploying generative AI for crucial tasks until they thoroughly understand the technology's capabilities and limitations. Nevertheless, staying on the sidelines may prove risky as generative AI advances and accumulates users at remarkable speed. Company leaders can consider a few steps right away.
Executives must clarify how and where generative AI might add the most value. Creative teams want to accelerate collection design, or marketers want to generate social content at scale. Specific goals—improving customer satisfaction scores or reducing response times—can help measure success. Organizations should then decide which use cases to prioritize based on each project's potential impact and practicality, building a short-term plan to test and refine these efforts. At the same time, it is wise to envision longer-term goals. Some might involve a recurring use of generative design platforms, which could be updated season by season.
It is tempting to experiment randomly with innovative tools, but that can lead to wasted time if the technology is not deployed with a clear purpose. Fashion executives should deliberately select generative AI tools that bring genuine improvements.
A previous article discussed some challenges associated with generative AI. Legal uncertainties are among the biggest. Copyright questions may arise if AI-generated designs include elements drawn from other brands' work. Without binding legal precedent, debates over creative rights will likely surface repeatedly, especially given the highly public disputes that sometimes occur in fashion. Another issue is bias embedded in AI algorithms, which can cause problematic or offensive outputs and damage brand reputation. Simply blaming "the AI" will not soothe public criticism.
Additionally, users of generative AI must remain aware of possible inaccuracies, which means having processes in place for human verification. Training employees to understand both the power and pitfalls of generative AI is essential. Regular reviews, ethical guidelines, and a consistent approach to risk management can also help minimize harm.
Companies must develop teams that integrate new tools into their daily workflows to realize generative AI's potential. That includes designers, marketers, sales staff, and customer service personnel. Some fashion businesses have already begun offering AI-focused development. Levi Strauss, for example, launched a machine learning boot camp in 2021, aiming to train employees from nontechnical fields on how to use machine learning in design. Once they complete the program, employees create AI applications specific to their roles. This strategy boosts the diversity of technical know-how, enabling the company to identify blind spots that a strictly engineering-focused team might miss. It also helps cross-functional teams collaborate more effectively and supports employee retention.
Collaboration can become more creative and efficient when an entire workforce gains AI literacy. Leaders should consider structuring responsibilities to foster synergy between data engineering teams and other departments. Weekly leadership meetings or joint sessions can help teams shape quarterly road maps, coordinate priorities, and maintain open communication.
Fashion companies do not need to build foundation models or applications from scratch. Instead, they can partner with generative AI providers or experts to accelerate implementation. These partnerships might revolve around tools from significant technology players—such as cloud-based platforms or APIs—or smaller specialists that offer unique generative AI solutions.
Although the technology for generative AI in apparel and luxury is still evolving, starting early can unlock tremendous opportunities. Exploring new tools now, even in pilot programs, can ultimately create breakthroughs that reimagine how fashion brands design, market, and engage with customers. Experimentation today can pave the way to boundless innovations tomorrow.
AiDLab project, December 2022.
Personalized marketing statistic originally cited in Santiago & Company research.
High-end brand sales conversion data from Santiago & Company analysis.
Levi Strauss machine learning boot camp, 2021.
Levi Strauss retention data, 2021.
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