GENERATIVE AI IS TRANSFORMING THE FASHION INDUSTRY AT A NEW LEVEL

?GENERATIVE AI IS TRANSFORMING THE FASHION INDUSTRY?AT A NEW LEVEL

Global Apparel Market Outlook - An Overview

The global apparel industry was valued at $1,449.0 billion in 2018, registering a growth of 4.6 percent in comparison to the last year. Growth of the global apparel market is expected to remain moderately strong, owing to increasing disposable income in middle-class people globally to shorten the fashion cycles. During the forecast period 2020 and 2025, the market is estimated to grow by 4.8 percent to reach $ 2,006.4 billion, driven by strong growth in the Asia-Pacific market. The other significant factors driving the growth of the global apparel market are - the growing population, rapid urbanization, and a shift in the global economic power base.?We estimate that revenues for the global fashion industry (apparel and footwear sectors) will contract by –27 to –30 percent in 2020 year-on-year, although the industry could regain positive growth of 2 to 4 percent in 2021 (compared with the 2019 baseline figure. According to a report,?the Indian fashion market is expected to grow at a compound annual growth rate (CAGR) of 11-12% to reach $115-125 billion by 2025. The global apparel market is expected to progress at a healthy rate in the coming years. The apparel and fashion industry holds a wide variety of garments and uses almost all the textiles manufactured. Broadly, the industry is divided into - clothing for men and boys, clothing for women and girls, and clothing for children. The global apparel consumption market has arrived at using a bottom-up approach to estimate and validate the market size and related sub-markets.

MARKET SCOPE

The fashion and apparel industry includes all the companies that design and sell clothes, footwear, and accessories. The scope of products varies from market to market, and the product category, from basics to luxury products - everything is defined in a particular category.?Previously, the apparel business belongs to wholesalers, selling the bulk of goods to retailers. Later on, then items will be marked-up and sold to consumers at a profit. However, at times, it becomes quite challenging to draw a line between wholesalers and retailers. The most common types of operation in textile and apparel businesses are wholesalers and retailers. However, the report is highly focused on the retail operation.?

RESEARCH METHODOLOGY

The apparel market research has been prepared by focusing on two kinds of research - primary and secondary to arrive at the global consumption of apparel. The primary sources are apparel manufacturers, raw material suppliers, association representatives, and industrial users. The secondary sources used in the research are investor relationship presentations, annual reports & other company pages, paid databases, news agencies, and interviews with industry experts on the supply side of the value chain.?The secondary research objective was to gather key information about the industry’s supply chain, the market’s monetary chain, market segments, the total pool of global players, and regional and country-level markets.?Primary research was aimed to validate the information gather during secondary research and collect other insightful information from the industry experts.?

The market landscape of the global apparel market is discussed in the form of key drivers, restraints, and opportunities pertaining to this industry was identified. Let’s discuss more on market drivers as these are responsible for promoting and pushing the growth of the apparel market industry. They play a vital role in designing the business strategy to increase revenue with the overall growth of the industry. The major market drivers in the apparel industry are:

?A Rise in Millennial and Gen Z Population

?The spending power forces to underwrite the global economy are Generations Z and Y. According to Forbes, millennials in the United States spend $ 600 billion annually and are projected to make up 35 percent of spending by 2030. Millennials are young spenders. As soon as they enter their 30s, they will be an increasingly important consumer group. Growing disposable income in emerging economies will result in increasing wallet size, which indeed will result in consumers with more money to spend and a greater passion for lifestyle consumption. The per capita apparel expenditure in India and China has increased by 31 percent and 27 percent in 2019 as compared to 206. The trend is estimated to continue in the near future.?Social media channels have turned into a lucrative marketing tool where shoppers are scrolling through social feeds, and bloggers are discovering the latest trends and covering must-have fashion items. Shoppers are not limiting social media channels to inspiration; they are also making purchases via social media channels.?Hence, retailers are exploring social media marketing to leverage their shopping capabilities and boot sales volume. The fashion world is moving online; an estimated 1.8 billion people across the globe purchased goods online. E-retail sales across the world summed$2.8 trillion U.S. dollars, and the projected growth of e-retails is 4.8 trillion U.S. dollars by the end of the year 2021. By the year 2020, consumers are predicted to spend $1 trillion on cross-border e-commerce, and more than 900 million consumers have international social media connections. Hence, fashion tycoons are getting the opportunity to grow their potential consumers, and boost revenues on global digital platforms.?

?MARKET RESTRAINTS

?Market Restraints are anticipated to hinder the overall growth of the apparel and fashion industry. The factors pose a hurdle to industry growth and keep a check on the significant development of the industry like Increasing awareness and demand for sustainability.

??Rising fast fashion consumer demand

?Gone are the days when fashion brands used to spoon-feed trends to consumers. For many decades, this “push model” has worked for fashion players. Today’s fashion world is changing this push model to a pull model. In many segments of the mass market, trends are more likely to pop up from the street.?The changing era of fast-changing preferences and shifting from push to pull model, responding to demand shifts, and tailoring production, create a lot of challenges for stakeholders. Fashion brands are under continuous pressure of bringing new styles more frequently, switch out lines mid-season, smaller batch sizes, and increased on-demand replenishment due to a rise in fast fashion.?

?A slowdown in the global economy

?The global economy is going through a synchronized slowdown; the growth of the apparel and fashion industry has downgraded to 3 percent in the last year 2019 - the slowest pace since the global financial crisis. The global economy will continue to be weakened by rising trade barriers ad increasing geopolitical tensions. By the end of the year 2020, the US-China trade tension will collectively reduce the global GDP by 0.8 percent.?The other factors responsible for the growth slowdown are low productivity growth, the aging demographic in advanced economies, and other country-specific factors in several emerging market economies. The overall downturn in the global economy would have a direct percussion on the fashion industry.?Market players need to focus on the opportunities as they bring revenue to their pockets. Additionally, market players should case on these revenue pockets to gain a competitive advantage in this highly competitive market.

?Growing demand in emerging economies

?The lower manufacturing cost of apparel in developing countries such as China, India, Bangladesh, Vietnam, and many others has led to an increase in the production capacities of clothing in the Asia Pacific region. The primary reasons for the lower manufacturing cost are lower labor costs, government support, less stringent environmental regulations, availability of raw materials, and others. Apparel forms a large part of consumption in these emerging economies. Factors driving the growth in developing countries are increasing disposable income, the youth-dominated population, rising buying power, and increasing fashion awareness in rural areas. Also, the growing per capita consumption of apparel and the youth-dominated population will create a significant opportunity for apparel and fashion brands.?

??Increasing demand for secondhand clothing

?Millennials and Gen Z are driving the growth of secondhand. Currently, millennials and boomers are the biggest secondhand consumers, accounting for 33 percent and 31 percent of total resale shoppers.?The secondhand market is currently valued at $24 billion and is predicted to grow to $51 billion in the next 5-years and is expected to reach $64 billion by 2028.?The secondhand market looks so dynamic that retailers are eager to get in on the circular fashion game. For today’s fashion consumers, the line between new and used apparel is getting blurred. And that’s what brings a significant transformation in the retail world.

Porter's Five Force Analysis

The report has been analyzed considering Porter's five forces to identify the competitive forces shaping the apparel and fashion industry. Porter’s model is designed to enhance a company’s profitability by analyzing the competitive level. The five forces of Porter’s model aid businesses to determine their corporate strategy by identifying the industry’s structure. The five forces considered in Porter’s model are:

?The threat of New Entrant

?New industry players are bringing new and unique business ideas to the table. Doubtlessly, new bees are getting smarter with smart business technology; they know the various way to popularize their products. Apparently, social media is their favorite and more effective one.?For new entrants, the apparel industry offers a "high risk, high reward" approach. And it's not too difficult to get a foot into the door and copy others. The penetration of technology and growing e-retail space in the entire value chain has enabled startups to enter the fashion industry with minimum investments and lesser economies of scale.?

?Threat of Substitute?

?This force is almost negligible in the apparel and fashion industry, and there is too little to substitute clothes. The "substitution" in the fashion industry is just competition. Suppliers have little control over the apparel and fashion industry. Most apparel businesses source their products from third-party manufacturers by offering them just a fraction of the profit. Hence, they are dispensable and can be swapped out. As a result, the apparel industry's investments are relatively low and will stay low until the global growth gap reduces significantly.?

??Bargaining Power of Buyer

?In the apparel and fashion industry, buyer power is a relatively larger force. The most critical force is buyers' bargaining ability, as buyers can choose to push prices down, not to buy products, or switch retailers. Buyers have many options to shop for apparel and fashion goods. So, a little incentive to stay with one particular company gives them indirect bargaining power.?

?Degree of Competition

The fashion and apparel industry is highly competitive as large numbers of retailers sell similar products. And brands allow some businesses to sell apparel for significant rates. In the industry, there is little innovation, so the market is rapidly becoming saturated with very similar products. The fashion industry is tough to get into and is almost becoming a "race to the bottom".In the apparel industry, the retail market's growth for apparel would be driven by developing/emerging economies. The fastest-growing apparel market will be India and China, with a CAGR of 8.2 percent and 9.6 percent, respectively, between 2020 and 2025.??

EMERGING TECHNOLOGIES

Generative AI-produced pictures of imaginary Nike collaboration?sneakers?have circled social media for weeks. But that’s just one example of how AI can be used in fashion; its uses are already valuable and vast, spanning marketing, engineering, and immersive experiences.?AI has stirred buzz in the last month, thanks to new and increasingly user-friendly AI tools. Just last week, on March 20, image editor Adobe Firefly and graphic design platform Canva rolled out new AI technology?supporting?text-to-video and?brand design, respectively. These specific launches further prove AI’s usefulness for?visual content creation. And that’s just the start of it. Industry experts predict that AI tools will allow for quicker, more seamless workflows, allowing workers across fields including marketing, operations, and engineering to focus on more creative and problem-solving tasks. “This is one of the fastest moving and potentially most impactful trends that we see on the tech landscape today,” said Roger Roberts, partner at consultancy McKinsey and co-author of December 2022.

?HYPER-PERSONALIZATION

?Next-level personalization of products and experiences for consumers — through e-commerce sites, targeted ads, and in-store experiences — is among the use cases set to benefit retailers. To date, personalization has largely been done through micro-segmentation by grouping customers according to their interests, age group, or location. “You’re going to see a lot more automation, which you have to have if you want to deliver hyper-personalization,”?said Brian Long, CEO of personalized mobile messaging platform Attentive. On Monday, the company announced Attentive AI, a tool allowing brands to create complete, multi-channel campaigns using AI and insights on effective content from 1.4 trillion Attentive data points. So far, a major retail brand using the tool in beta is reporting a 148% revenue increase.

“A lot of brands are going to our generative image-making platform to generate a marketing image that leverages products in their product catalog, paired with background and foreground imagery based on text prompts they enter. They’re then personalizing the image’s setting, lighting, and other elements,” said Long. The AI uses a database of images paired with text prompts to select fitting setting elements. “In some cases, that results in a production-ready image that they can send out. In other cases, it can help them figure out what they’re looking for in the final shoot.” Long said the tool allows brands to customize marketing imagery for different geographical regions at almost no cost.?Attentive AI develops copy for the campaigns, as well. “Our biggest challenge has always been getting our marketing copy completed in a timely manner,” said Jason Edwards, director of e-commerce at streetwear brand Hat Club. “The main surprise is just how close to final Attentive AI allows us to get our copy before we step in and finalize. We will be able to significantly cut down our content creation time, which in turn will allow us to build out better campaigns and better segment our customers.”

Visual merchandising and collection creation

AI could also solve problems with visual merchandising. As brands open more store locations, they need visual merchandising that expresses the brand identity globally but is also tailored to the context of each store. Head visual merchandisers rarely lay out every store in a brand’s fleet due to costs and time constraints. “If you were able to train learning programs, based on the instincts and intuitions of your best virtual visual merchandisers, using AI, then you could bring their voice, style, and capabilities to each shop door each season,” said Roberts.?He also noted the creative applications of AI for businesses. “Brands will be able to use generative AI technology to synthesize notes, sketches, and ideas for a collection. […] With AI, these can then be combined with operational and financial perspectives, as well as merchandising and marketing, to create a collection at a price and a margin that is sustainable for the brand and good for the customer.”?For it apart, Levi Strauss & Co is using AI to show a wider range of diverse?models?on its website and other channels.?“We’re also using AI to enhance and differentiate our loyalty program by offering personalized benefits to members, which is helping us achieve meaningful growth in enrollments, revenues, and app registrations,”?said Dr. Amy Rushkoff Bolles, global head of digital and emerging technology strategy at Levi Strauss & Co. For example, personalized benefits include localized discounts based on popular products in the area. Since expanding the loyalty program in Europe last year, the brand has reached 5 million members worldwide.

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“AI helps us provide personalized product recommendations on our website and mobile app,” based on consumer data from specific markets, said Gershkoff Bolles.?“We’re also leveraging consumer mobility data to customize our stores to the unique needs and interests of local consumers, and open new stores where we have the greatest demand.” Finally, she said that AI is powering Levi’s promotions by analyzing stock for which categories and products are most likely to benefit from being on sale. She noted examples including mid-season and end-of-season sales, as well as Black Friday sales in the U.S. and Europe.

WEB3 BRANDS

?Some web3 brands are already implementing AI into the creative processes. Charli Cohen, the founder of 2-year-old web3 brand RTLSS, said her studio has used generative AI to speed up coding development work by allowing the AI to create code, which has optimized workflow. She is also using it to create user-generated content. “We are progressively integrating more AI through this year,” said Cohen.?“Our next drop will use AI to gamify both the minting and post-mint experience. Our UGC toolkit, which we’re currently building, will also include back-end processes like validating assets?and managing IP protection; they’ll be automated and enhanced by AI.” AI can be used to analyze?an NFT’s blockchain transaction history to ensure the NFT is the original and not a duplicate. It?can also analyze the content of NFT art to ensure that its original and does not violate copyright laws. One physical fashion brand active in web3, Tommy Hilfiger, is experimenting with AI to engage customers in?co-creation. Specifically, during Metaverse Fashion Week. it’s giving consumers the opportunity to design items in the brand’s signature preppy style using generative AI.?

Fashion shows

?Even fashion shows, which have already undergone somewhat of a?transformation, are set to undergo change due to AI. Matthew Drinkwater, head of the emerging technology company Fashion Innovation Agency, has been experimenting with AI uses for the catwalks since leading an AI-focused course at the London College of Fashion during the pandemic. With students not being able to showcase their final work in 2020, Drinkwater worked with the FIA using archival show footage and skeletal data from the moving models to create a virtual runway show. The project was reimagined this year to include photorealistic models and AI and was released on?March 21 via LinkedIn. “The earlier catwalk had a huge amount of manual labor work involved,” said Drinkwater. “While that’s not to say that this wasn’t the case this time, as there were very specific skill sets required to deliver this with the tools, creating this kind of experience is much more [manageable]. That’s especially with the availability of AI tools like text prompt-to-video, which AI platform Runway AI launched this week.” “The video component is going to take it to the next level of engagement. The images are obviously very cool, but when it’s a video, people stop and look and take their time,” said Long. For the runway project this year, the FIA used AI prompt tools Mid journey and Stable Diffusion.?They took images from luxury brands that they were inspired by, trained the AI model to understand what those looks were, and then applied the looks to one particular male model to create a photorealistic video. Drinkwater agreed with Roberts that the human element is even more necessary within fashion’s experimentation with creativity and AI. However, he said, the new photorealistic opportunities will help to facilitate the mass adoption of digital fashion.

?AI IS FASTER TO IMPLEMENT THAN THE METAVERSE

With the metaverse, “a combination of many things has to come together in the right way to create a really great experience. That’s not the case with AI.” said Roberts, explaining, “Your current software can add features that will just make it better. It doesn’t require everyone to show up with headsets or create entirely new platforms. That’s why there is a shorter hype cycle, from everyone talking about AI to people using it and it having CFO-relevant impact.” With companies aiming to cut costs due to recession-related concerns, AI use could be leveraged to cut labor costs. “Its bottom-line impact suggests that this is something that needs to be top of mind for the CTOs and the CFOs. In many cases, it will replace repetitive parts of human work, allowing staff to carry out projects more quickly — but it cannot be a replacement.”

Drinkwater added, “It’s the combination of things that we can put together, like our use of machine learning, artificial intelligence, and immersive experiences, which will start to deliver those next-generation immersive experiences.”

?GENERATIVE AI: UNLOCKING THE FUTURE OF FASHION

?While still nascent, generative AI has the potential to help fashion businesses become more productive, get to market faster, and serve customers better. The time to explore the technology is now. As this season’s fashion weeks?wrap up in London, Milan, New York, and Paris, brands are working to produce and sell the designs they’ve just showcased on runways—and they’re starting next season’s collections. In the future, it’s entirely possible that those designs will blend the prowess of a creative director with the power of generative artificial intelligence (AI), helping to bring clothes and accessories to market faster, selling them more efficiently, and improving the customer experience.

By now, you’ve likely heard of OpenAI’s ChatGPT, the AI chatbot that became an overnight sensation and sparked a digital race to build and release competitors. ChatGPT is only one consumer-friendly example of generative AI, a technology comprising algorithms that can be used to create new content, including audio, code, images, text, simulations, and videos. Rather than simply identifying and classifying information, generative AI creates new information by leveraging foundation models, which are deep learning models that can handle multiple complex tasks at the same time. Examples include GPT-3.5 and DALL-E. While the fashion industry has experimented with basic AI and other frontier technologies—the metaverse, nonfungible tokens (NFTs), digital IDs, and augmented or virtual reality come to mind—it has so far had little experience with generative AI. True, this nascent technology became broadly available only recently and is still rife with worrisome kinks and bugs, but all indications are that it could improve at lightning speed and become a game changer in many aspects of the business. In the next three to five years, generative AI could add $150 billion, conservatively, and up to?$275 billion to the apparel, fashion, and luxury sectors’ operating profits,?according to McKinsey analysis.?From codesigning to speeding content development processes, generative AI creates new space for creativity. It can input all forms of “unstructured” data—raw text, images, and video—and output new forms of media, ranging from fully-written scripts to 3-D designs and realistic virtual models for video campaigns.

These are still early days, but some clear use cases for generative AI in fashion have already emerged. (Many of these use cases also apply to the adjacent beauty and luxury sectors.) Within product innovation, marketing, and sales and the customer experience in particular, the technology can have significant outcomes and may be more feasible to implement in the short term compared with other areas in the fashion value chain. In this article, we outline some of the most promising use cases and offer steps executives can take to get started, as well as risks to keep in mind when doing so. In our view, generative AI is not just automation—it’s about augmentation and acceleration. That means giving fashion professionals and creatives the technological tools to do certain tasks dramatically faster, freeing them up to spend more of their time doing things that only humans can do. It also means creating systems to serve customers better. Here’s where to begin.

UNDERSTANDING THE USE CASES: GENERATIVE AI USE CASES IN FASHION

Foundation models?and generative AI can be used across the fashion value chain.

Merchandising and product:

Convert sketches, mood boards, and descriptions into high-fidelity designs (for example, 3-D models of furniture and jewelry). Enrich product ideation by collaborating with AI agents that generate creative options (for example, new ideas, and variations) from data (for example, past product lines, inspirational imagery, and style). Customize products for individual consumers at scale (for example, eyeglasses based on facial topography).

Supply chain and logistics:

Support negotiations with suppliers by compiling research. Augment robotic automation for warehouse operations and inventory management through real-time analytics (for example, insights enabled by augmented reality, or AR). Tailor product return offers are based on individual consumers.

Marketing:

Identify and predict trends to improve targeted marketing from unstructured data (for example, consumer sentiment, in-store consumer behavior, and omnichannel data). Automate consumer segmentation at scale to tailor marketing initiatives. generate personalized marketing content based on unstructured data from consumer profiles and community insights. Collaborate with AI agents to accelerate content development and reduce creative blocks for in-house marketing teams.

Digital commerce and consumer experience:

Structure and generate sales descriptions based on past successful sales posts. Personalize online consumer journeys and offers (for example, web pages, and product descriptions) based on individual consumer profiles. Tailor virtual product try-on and demos to individual consumers (for example, clothing try-on, and styling recommendations). Enhance intelligent AI agents (for example, conversational chatbots, and virtual assistants) and self-service to address advanced consumer inquiries (for example, multilingual support).

Store operations:

Optimize store layout planning by generating and testing layout plans under different parameters (for example, foot traffic, local consumer audience, and size). Optimize in-store labor to avoid bottlenecks such as gaps in staff allocation and theft detection through real-time monitoring of video data. Support AR-assisted devices to better inform the workforce in real-time on products (for example, condition, assortment, inventory, and recommendations).

?Organization and support functions:

Coach sales associates to sustain successful “client ling” relationships via real-time recommendations, feedback reports, and high-value consumer profiles. Develop individualized training content for employees based on role and performance. Enable self-serve and automate support tasks (for example, HR tickets, accounting for large documents, review of legal documents). Generative AI has the potential to affect the entire fashion ecosystem. Fashion companies can use the technology to help create better-selling designs, reduce marketing costs, hyper-personalize customer communications, and speed up processes. It may also reshape the supply chain and logistics, store operations, and organization and support functions (see sidebar, “Generative AI use cases in fashion”).

Product development and innovation

Instead of relying on trend reports and market analysis alone to inform designs for next season’s collection, both mass-market fashion retailers and luxury brands’ creative directors can use generative AI to analyze in real-time various types of unstructured data. Generative AI can, for example, quickly aggregate and perform sentiment analysis from videos on social media or model trends from multiple sources of consumer data. Creative directors and their teams could input sketches and desired details—such as fabrics, color palettes, and patterns—into a platform powered by generative AI that automatically creates an array of designs, thus allowing designers to play with an enormous variety of styles and looks. A team might then design new items based on these outputs, putting a fashion house’s signature touch on each of the looks. This opens the door to creating innovative, limited-edition product drops that may also be collaborations between two brands. Products such as eyeglasses could be designed for individuals by using facial-recognition technology powered by generative AI to scan facial topography and adjust for a customer’s size and style preferences. This scenario became a reality in December 2022, when a group of Hong Kong–based fashion designers from the Laboratory for Artificial Intelligence in Design (AiDLab) held a fashion show featuring generative-AI-supported designs. Using tools from tech companies such as Cala, Desi Novel, and Fishable, fashion designers are already tapping into the power of generative AI to spark new ideas, try myriad design variations without having to produce expensive samples, and vastly accelerate their processes. (For beauty businesses, generative AI also provides an opportunity for brands to identify new product formulations, potentially helping to reduce lab testing costs.)

Marketing

Marketing executives and agencies can use generative AI to brainstorm campaign strategies, product campaign content, and even virtual avatars for every marketing channel—and do it fast. Striking marketing gold can often be a numbers game. Consider TikTok: there’s no single winning formula for going viral on the platform. Instead, the more you produce, the higher your chances are of becoming a trending topic and boosting brand awareness and sales. Prompting a generative AI-powered video platform to create short-form videos for TikTok or other social media platforms can help save time and costs associated with pumping out social-media content. Generative AI can recognize patterns and trends in viral content and create new content that also follows specifications from the marketer. These exercises can help in-house marketing teams manage their workloads while reducing their reliance on outsourcing work to creative agencies. Marketers will want to be careful with this approach, however: trying to reach consumers by replicating what other brands have done can counteract the unique identity and value proposition that a brand spends years building.

Generative AI could also be applied to personalized customer communications. Companies that excel at personalization increase revenues by?40 percent compared with companies that don’t leverage personalization, according to McKinsey research. Several start-ups—CopyAI, Jasper AI, and Writesonic, to name just a few—are helping pioneer personalized marketing at scale through generative AI. Using these tools, a marketer’s daily tasks might start to look like this: they could choose the type of content they want to create, whether it’s an email, a long-form blog post, or something else; add a prompt describing what they are looking for; and include the targeted audience and other parameters, such as tone, that help create marketing communications that are in line with the brand. The AI tool then offers several options from which the marketer can choose. These tools are most helpful when applied to lower-funnel marketing channels (those that are mostly used to encourage sales conversions) as opposed to more prestigious brand-building communications. Marketers are still required to prompt and edit the work.

Sales and consumer experience

Today’s generative AI-powered chats, which use stronger natural-language processing to better understand and interact with humans, are already a measurable improvement over existing AI chats. That said, there isn’t (yet) a foolproof generative AI chatbot for businesses—current chatbots and other text-generating tools still occasionally make errors that could cause serious customer service disasters. Eventually, though, this technology could help customer support agents outsource complex inquiries—for example, using chatbots to help provide personalized responses in numerous languages. Today, there are services that assign a generative AI “representative” to a brand to handle customer service queries across email, chat, text, and a brand’s own platforms. These services help to reduce customer service wait times and improve response times. Generative AI agents can also serve luxury brands, particularly when it comes to “clienteling,” a retail strategy whereby sales associates develop long-term relationships with a brand’s highest-spending customers to encourage purchases and improve brand loyalty. (High-end brands can hit a sales conversion rate of 60 to 70 percent in luxury boutiques, through appointment-only shopping. That process has remained somewhat analog and manual, relying on brands’ sales associates to reach out to customers through a variety of messaging platforms or texts, and is limited to only when those associates are working. Generative-AI-powered tools can keep the conversation going or make styling recommendations after a shopper leaves the store, coach sales associates on how to engage with customers, personalize communications for specific customers, and analyze consumer profiles and online real-time interaction.

In July 2022, apparel retailer Stitch Fix said it was experimenting with GPT-3 and DALL-E 2, the text-to-image AI generator, to boost sales and improve customer satisfaction with better styling services. These generative models are being tested to help stylists quickly and accurately interpret reams of customer feedback and curate products that customers would be likelier to purchase. For example, the AI tool could analyze all of a customer’s feedback, which could include hundreds of text comments, email requests, product ratings, and online posts. If a customer regularly comments on, say, the “great fit” and “fun color” of a certain style of pants, DALL-E could generate images of similar pants that the customer would likely want to purchase. The stylist could then find similar items in Stitch Fix’s inventory and recommend them to that customer. Virtual try-on are yet another example of how generative AI can improve sales and consumer experience. Paris-based Veesual enables virtual try-on integration for e-commerce fashion brands, meaning customers can choose their model and pick clothes to try on.

How to get started

As exciting as generative-AI technology might be, companies will still want to tread cautiously before entrusting any of their core tasks entirely to generative AI. But neglecting to explore the possibilities that this technology offers could be just as risky, given the pace at which it is evolving and the explosive growth of the user base. Executives can start thinking now about how their businesses could use generative AI. There are a few steps leaders can take to begin.

Make value your North Star

Fashion leaders should outline where generative AI can offer the greatest value to their business. Start by noting which areas—creative design, merchandising, runway campaigns, or clienteling—could benefit the most from generative AI. Leaders can then prioritize the generative AI use cases they should pursue based on the level of impact the use cases may have on their business. Some measures of impact include improving customer satisfaction scores and reducing customer service wait times. Once the value is identified, use cases should also be prioritized according to how feasible they are to implement; determining how seamlessly generative AI can be used will depend on things like a team’s technical skills. Afterward, teams should build a short-term road map to test and validate these use cases. At the same time, they can also consider what long-term goals might include, such as how to build a generative-design platform that can be updated and used by designers for every season. It may be tempting to have a bit of fun with generative AI, but harnessing its power will take extra diligence. Fashion executives must be intentional in building tools that can deliver value rather than experimenting with existing tools indiscriminately.

Know risks and plan to mitigate them

In a previous article, we listed some of the?risks of using generative AI. One is that the legal parameters around generative AI’s use are still being ironed out. Designers are sometimes criticized for creating derivative works and copycat designs. Determining who owns the intellectual property and creative rights to AI-generated works, which could be based on multimodal data sources such as other designers’ past collections, will be decided on a case-by-case basis until there is a strong legal precedent. (Although it doesn’t involve generative AI, the high-profile battle between Hermès and artist Mason Rothschild surrounding?MetaBirkin?NFTs, in which a judge ruled that the NFTs infringed on Hermès’s trademark, shows how fashion brands can become embroiled in legal conundrums when new technologies emerge.) Another risk is bias and fairness in generative AI systems, particularly around biased data sets, which may present 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 hurt. And pointing fingers at the company's AI in an attempt at damage control may do little to calm consumer ire.

There is also the risk that employees who use generative AI are not fully aware of its shortcomings and may fail to check for errors introduced by the technology. In this case, businesses must regularly train employees and provide them with the resources they need to understand how to use the technology. While risks are unavoidable, executives can mitigate their potential impact by establishing a process to address risk, ethics, and quality assurance.

Upskill your current workforce

Generative AI tools could add value to a host of different areas of a business, so it will be important to educate and train employees—including designers, marketers, sales associates, and customer service representatives—on the use of the technology. Some businesses have already introduced AI-focused training. Levi Strauss, for one, 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 that are relevant to their work.?One of Levi’s goals with the program is to increase the diversity of employees who have tech knowledge so that the company can uncover problems that employees who come from traditional technology backgrounds might otherwise miss. The program also helps teams with different specializations—such as design teams and engineering teams—communicate better and find common ground. Furthermore, Levi’s has found that the program helps improves employee retention.

With an AI-savvy workforce, collaboration will take on a new meaning. Leaders should consider: How do we define responsibilities and operate collectively between technical and nontechnical roles? Design and software engineering teams can set up weekly leadership meetings to strategize quarterly road maps and working sessions among teams. Design leads can share their needs for certain insights and tools (a tool that generates design variations from a sketch, perhaps), while engineering teams deliver those tools.

?Partner with the right tech support

Fashion businesses will no doubt have to invest in their workforce when it comes to leveraging generative AI, but they won’t have to build out applications or foundation models themselves. Instead, fashion leaders can partner with generative-AI businesses and experts to move quickly. A fashion executive might partner with a business (such as Microsoft or OpenAI) that provides new technology or a partner that provides support capabilities (such as cloud computing or APIs). While the potential use cases for generative AI are coming to light quickly, the future of this technology in the apparel and luxury industries is still being stitched together. But experimenting with new tools today means opening infinite possibilities tomorrow.89% of all companies across different sectors are switching to digital technologies?and the fashion industry is not an exception. McKinsey reports that in 2021, fashion brands and companies invested approximately 1.7% of their income in emerging technologies. Moreover, they estimate the figure will rise between 3.0% and 3.5% by 2030. Blockchain technology,?non-fungible tokens (NFTs), and?AI technology?are digital technologies that are implemented in the fashion industry. On the other hand,?generative AI?is relatively new; yet it started affecting many elements of the fashion industry.

.What is generative AI?

Generative AI refers to a class of?machine learning algorithms?designed to generate new, original content based on a set of input data. Generative AI is used for a variety of tasks, including generating text, images, music, codes, and even entire websites. AI-driven?generative adversarial networks (GANs), a type of generative AI, can perform creative tasks that were once thought to be unique to humans. These powerful machine-learning models can create realistic images, videos, and voice outputs.

Why is generative AI important for the fashion industry?

Generative AI is important for the fashion industry as it brings many benefits. It can improve customer satisfaction and allow online retailers to bring generative products to market faster and more cost-effectively by diversifying and personalizing fashion designs, increasing the representation of all body types with generated models, and creating automated digital experiences in online shopping. In the fashion retail industry, where both aesthetics and consumer pleasure are important factors in product design and speed and novelty are crucial, GANs offer an efficient way to generate new product designs at a low cost. Watch the video below to see the generative ability of GANs in use.

Generative AI tools for image & design generation

Before explaining the specific use cases of generative AI in the fashion industry, it is good to know how it generates creative images and other content constitutive of a design.?By utilizing generative algorithms, AI can create unique and interesting images that merge computer-generated styling with human-driven creativity. The artwork created by generative AI in this way offers an entirely new approach to creating visual art. It can tap into generative elements and generate infinite variations of the same image.?With generative AI, the artist’s creativity is no longer limited by limitations such as cost or resources. Rather, it allows various professionals like graphic and fashion designers to craft truly innovative or fusion works of art at the click of a button. In Figure 1 above, you can see how it is able to produce creative, stylistic, and unique outputs from the same input. Since the fashion industry relies on these three elements (creativity, style, uniqueness), generative AI is a perfect match for its purposes.?Most AI-generated images are nearly impossible to differentiate from real ones. When participants in a study were unaware that generative AI technology had been used, they tended to perceive the images generated by GANs as more novel than the original images. Another famous generative AI tool, DALL-E, has the ability to create a wide range of images, including?Photorealistic images, Abstract patterns, and Stylized illustrations.?It has been demonstrated to be capable of generating highly creative and novel images that go beyond what it was explicitly trained on. Some examples from its realistic and artistic generations: We u can see how generative AI is capable of creating surprising and stylistic designs from a basic object.?

5 use cases of generative AI in the fashion industry with example cases

1. Creative Designing for Fashion Designers

With its great ability to generate new images and content, generative AI can assist fashion designers in the creative design process by developing new ideas or helping to refine and optimize existing designs with the latest trends. This can be done through a variety of techniques, including Generative AI can create entirely new fashion designs based on specified constraints and parameters, such as the desired aesthetic, materials, and target market, and?Generative AI can be used to apply the style of one design to another, allowing designers to create variations on existing designs or combine elements from different sources. Besides, we don’t need to be an exclusive fashion designers for creating new designs. An ML engineer specializing in generative arts, Fathy Rashad, created his own generative cloth designer ClothingGAN by using StyleGan and GANSpace.

2. Turning Sketches into Color Images

Generative AI benefits the fashion industry as it can also transform sketches into fully colored images. Generative AI allows designers and artists to experience their vision in real time with minimal effort. With this technology, they can save valuable time and resources while being able to experiment without difficulty. Additionally, generative AI can help limit human error, such as errors in color-matching and patterns. It can also enable fashion brands to become more creative, leveraging the ability to analyze numerous sketch-to-color combinations and generate multiple variations for review. For example,?Chroman?is a tool that allows a trained algorithm to create genuine and personalized color palettes. Similarly,?Colormind?enables preparing creative color palettes based on preferred samples from movies, photographs, artworks, etc. By implementing such tools, generative AI can also help to reduce the need for physical samples, saving time and resources.

3. Generating Representative Fashion Models

Using generative AI to create a diversity of fashion models can help fashion companies to better serve a wide range of customers and showcase their products in a more realistic and accurate way. A Cambridge University research shows that when Dove’s advertising campaign featuring women of various skin tones and body types increased sales by 600% in two months. For this purpose, it can be used to create a diversity of fashion models in several ways: Virtual try-on:?Generative AI can create virtual representations of fashion products that can be superimposed onto images of people, allowing customers to “try on” clothes virtually. These virtual models can be customized to represent a wide range of body types, colors, and sizes, allowing customers to see how the clothes would look on them specifically. 3D rendering:?Generative AI can create 3D models of fashion products that can be rotated and viewed from different angles. These models can be customized to represent a wide range of body types, colors, and sizes, allowing designers to see how the clothes would look on different body models.

Japanese tech company?DataGrid?used GANS technology to create models that can change bodily. You can watch the video released by the company showing a multitude of generated models: Lalaland?is another tech startup that makes hyperrealistic virtual fashion models driven by generative AI for use on e-commerce platforms. It works by creating model avatars, uploading the images of garments, styling the product, and then downloading output images.?

?4. Marketing & Trend Analysis for Fashion Brands

AI-powered generative models allow companies to speed up and improve their?trend forecasting and marketing analytics?capabilities. As a result, companies stay ahead of trends while meeting the customers’ future needs more effectively. It can help trend analysis by bringing together a variety of techniques, such as machine learning and probabilistic programming. These techniques allow for powerful generative models that consider the customer desires in the fashion business, and generate deeply personalized options for specific consumer desires that go beyond what traditional analytics and customer demand algorithms can do. It also improves marketing capabilities by: Utilizing data analysis, natural language processing, and machine learning to create a highly tailored and personalized product range for the target audience, Designing emails, website pages, captions, and ads that are tailored to a specific person’s interests and preferences in order to engage them, and Plotting creative and authentic marketing and ad content that are likely to storm search results

5. Protecting the Data Privacy of Consumers

The fashion industry can utilize generative AI to improve consumer?data privacy. The generative AI algorithms allow fashion companies to generate new designs while keeping customer data private. With?synthetic datasets?that generative AI produces, companies are able to create unique patterns and automated data analytics while protecting customers’ details, such as: contact information, banking information, purchase history, preferences, and more from third parties.?It safeguards individuals’ financial security and provides organizations with valuable insights into their target market without invading people’s privacy. This way, generative AI offers a way for fashion brands to revolutionize their business strategy in a secure manner.

Challenges of Generative AI for the fashion industry

The biggest challenge of generative AI for creative sectors such as the fashion industry can be the ambiguities around the copyright of AI-generated work. Using generative AI in the fashion industry can lead to some problems such as: disclaiming the uniqueness, originality, or copyright eligibility of the generated designs or other fashion materials. ownership problems about whether the fashion designer or the programmer of the AI deserves the authorship rights of the generated work. Misuse of such tools for unethical marketing strategies, and risk of diminishing human creativity in the fashion industry and leading to unemployment.

CONCLUSION

Instead of waiting for technology vendors to introduce generative capabilities, it will be vital for designers and developers to deepen their skills, expand their toolsets, and prepare for a near-term future where their creative and commercial talents are supported by AI. In the ever-evolving realm of apparel, keeping pace with the latest innovations is vital for competitiveness and success. Our industry, along with others, could well find itself on the brink of a revolution – one powered by the rapidly emerging potential of generative AI. Advancements in this technology have come thick and fast this year with businesses using generative AIs to write code, create art, and author content all at speeds never seen before. ChatGPT pioneered the way at the start of the year with GPT-3.5, and technology giants have clearly shifted their strategies to either compete or adapt. Microsoft is bringing ChatGPT into Office via CoPilot, and Google has just launched Bard alongside integration with G-Suite – and Adobe and Nvidia have shared similar commitments to the technology. As consumers we undoubtedly benefit from these advancements – our creative work should become easier as more tools arrive that try to understand our imaginations and turn this into reality. We use these tools by speaking to them in human language. We prompt the tools to create, by describing our needs as we might do to a colleague or intern. We share little details that might help the AI work and think as we do, then we follow up and iterate until we are satisfied. You can find businesses using these tools today as the barrier to entry is surprisingly low. If you can explain the solution or problem in a short prompt, then they will happily assist you in your creation. But these tools only scratch the surface of business benefits. The next generation of generative AIs will be able to identify improvements to development processes, help create a range plan from previous seasons, and be able to contribute to meetings and output just like a human teammate. To enable this, these tools will to be prompted, and that prompt will need to include your business data. For apparel enterprises to fully capitalize on the immense possibilities offered by generative AI, a critical factor comes into play:?Product Lifecycle Management (PLM). Without a centralized hub of information and a unified source of truth, harnessing the full benefits of generative AI becomes a daunting challenge. In this fast-paced environment, where AI advances are coming rapidly, if apparel businesses fail to embrace PLM, they risk losing their future competitive edge by being unable to adopt further generative AI technologies. Now is the time to leave behind disconnected spreadsheets and email threads, and?now is the time to adopt PLM. Traditional Product Lifecycle Management benefits are well known and studied at this point and there have always been compelling reasons to adopt it. Finding the right provider was always important and the depth of features is a main consideration in finding?the perfect PLM.

Today, however, looking forward to this possible future with Generative AI argue that businesses new to PLM need to look equally (if not more so) at a time to install. How we define success with PLM may be about to change. Generative AIs will need data repositories to draw out new insights from our single source of truth, so the sooner we start building that source the better. Speed of installation could then perhaps be considered over roadmaps and future enhancements. These new AI tools are unlikely to be exclusively embedded in existing software. Many of the Generative AI tools are agnostic and run in web browsers or small executables. Some even find themselves as Discord extensions.

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Reference:

1.????“State of Fashion Technology Report 2022.”?McKinsey, 2 May 2022, https://www.mckinsey.com/industries/retail/our-insights/state-of-fashion-technology-report-2022. Accessed 25 December 2022.

2.???“Unpaired image-to-image translation using cycle-consistent adversarial networks.”?ICCV 2017, Zhu, J.Y., Park, T., Isola, P., Efros, A.A.

3.???“Artificial intelligence in the fashion industry | by Research Features.”?Medium, https://medium.com/@ResearchFeatures/artificial-intelligence-in-the-fashion-industry-2df9e0e42a54. Accessed 25 December 2022.

4.???“How to Build an AI Fashion Designer | by Fathy Rashad.”?Towards Data Science, https://towardsdatascience.com/how-to-build-an-ai-fashion-designer-575b5e67915e. Accessed 25 December 2022.