By now, you've probably heard of OpenAI's ChatGPT, the artificial intelligence chatbot that became an overnight sensation and sparked a digital race to build and launch competing products. ChatGPT is just one consumer-friendly example of generative AI, a technology comprised of 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 underlying models, which are deep learning models that can handle multiple complex tasks simultaneously, such as GPT-3.5 and DALL-E.
While the fashion industry has experimented with basic AI and other cutting-edge technologies – the Metaverse, non-fungible tokens (NFTs), digital IDs and augmented reality (AR) or Virtual reality (VR) – but so far it has had little experience with generative AI. Granted, this emerging technology has only recently become widely available and is still fraught with worrying quirks and bugs, but all signs point to it potentially improving at lightning speed and becoming a game-changer in many areas of business Changemaker.
According to analysis by McKinsey, a conservative estimate is that generative AI will increase US$150 billion in the next three to five years, and the operating profits of the apparel, fashion and luxury goods industry will increase by US$275 billion. From collaborative design to accelerating the content development process, generative AI opens up new spaces for creativity. It can input all forms of "unstructured" data - raw text, images and video - and output new media forms, from complete scripts to 3D designs to lifelike virtual models of video events.
While it’s still early days, some clear use cases for generative AI in fashion are already emerging. (Many of these use cases also apply to the beauty and luxury industries) Particularly in product innovation, marketing, sales and customer experience, this technology can yield significant results compared to other areas in the fashion value chain and is likely to More feasible in the short term. In this article, we outline some of the most promising use cases and provide steps executives can take, as well as the risks to be aware of when doing so.
In our view, generative AI is not just about automation, it is also about enhancement and acceleration. That means giving fashion professionals and creatives the technology tools to complete certain tasks faster, giving them more time to do things only humans can do. It also means building better customer service systems.
The underlying model and generative AI can be used across the entire fashion value chain.
Sales and Product:
Supply Chain & Logistics:
Marketing:
Digital Commerce and Consumer Experience:
Store operations:
Organizational and support functions:
Generative AI has the potential to impact the entire fashion ecosystem. Fashion companies can use this technology to help create more marketable designs, reduce marketing costs, make customer communications highly personalized, and speed up processes. It may also reshape supply chain and logistics, store operations, organization and support functions.
Creative directors of mass-market fashion retailers and luxury brands alike can use generative AI to analyze various types of unstructured data in real-time, rather than relying solely on trends. Reports and market analysis to inform collection design for next season. For example, generative AI can 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 can input sketches and required details such as fabrics, color palettes and patterns into a platform powered by generative AI, which automatically creates a series of designs that can Enable designers to design various styles and looks. A team might then design new products based on those outputs, adding the fashion house's signature elements to each look. This opens the door to the creation of innovative limited edition product lines. By using generative AI-driven facial recognition technology, products such as eyewear can be designed for individuals, scanning facial topography and adjusting to the customer's size and style preferences.
This scenario became a reality in December 2022, when a group of Hong Kong fashion designers from the AI Design Lab (AiDLab) held a fashion show with the theme of "Generative AI-Supported Design" . Through tools from tech companies like Cala, Designovel, and Fashable, fashion designers are already harnessing the power of generative AI to inspire new ideas, try out countless design variations without producing expensive samples, and dramatically speed up the process. (For the beauty industry, generative AI also offers brands an opportunity to identify new product matches, which may help reduce lab testing costs.)
Marketing Executives and agencies can use generative AI to brainstorm campaign strategies, product campaign content, and even avatars for each marketing channel—fast.
Finding the golden rules of marketing is often a numbers game. Take TikTok: There is no single winning formula for virality on this platform. Conversely, the more you produce, the more likely you are to become a hit, increasing brand awareness and sales. Creating short videos for TikTok or other social media platforms through an AI-generated video platform helps save time and costs associated with outputting social media content. Generative AI can identify patterns and trends in viral content and create new content based on marketers’ specifications.
These exercises can help in-house marketing teams manage their efforts while reducing their reliance on outsourcing work to creative agencies. However, marketers should be cautious when using this approach: trying to attract consumers by copying what other brands are doing can undermine a brand’s unique identity and value proposition that it has spent years building.
Generative AI can also be applied to personalized customer communications. Research from McKinsey shows that companies that excel at personalization see a 40% increase in revenue compared to companies that don’t utilize personalization.
Several startups—such as copyai, Jasper AI, and Writesonic—are helping pioneer personalized marketing at scale through generative AI. Using these tools, a marketer's daily tasks might look something like this: They can 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're looking for; and include their target audience and other parameters such as tone of voice, which help create marketing communications that are consistent with the brand. The AI tool then provides several options for marketers to choose from.
These tools are most helpful when applied to low-funnel marketing channels (used primarily to encourage sales conversions) rather than more prestigious brand-building communications.
Today’s AI-generated chat, which uses more powerful natural language processing (NLP) to better understand and interact with humans, is already better than existing AI chat has measurable improvements. That said, there is no foolproof AI chatbot yet, and current chatbots and other text-generating tools still occasionally make mistakes that can lead to serious customer service disasters. However, the technology could eventually help customer support agents outsource complex queries, for example, using chatbots to help provide personalized responses in multiple languages.
Today, there are services that assign a brand a generative AI “representative” to handle customer service inquiries via email, chat, text, and the brand’s own platform. These services help reduce customer service wait times and improve response times.
Generative AI agents can also serve luxury brands, particularly on the “client” side, a retail strategy in which sales assistants build long-term relationships with a brand’s top spenders to encourage purchases and increase brand loyalty . (For example, through appointment shopping, high-end brands can achieve a sales conversion rate of 60% to 70% in luxury boutiques.) The process is still somewhat analog and manual, relying on the brand's sales assistants to communicate through various messages. Platform or SMS to contact customers, and only while these sales associates are working. AI-powered tools can continue conversations or make styling suggestions after shoppers leave the store, coach sales associates on how to interact with customers, personalize communications for specific customers, and analyze consumer profiles and real-time interactions online.
In July 2022, clothing retailer Stitch Fix said that they were experimenting with GPT-3 and DALL-E 2 (text-to-image AI generator) to boost sales and increase sales through better styling services. customer satisfaction. These generative models are being tested to help stylists quickly and accurately interpret large amounts of customer feedback and curate products that customers are more likely to buy. For example, AI tools can analyze all customer feedback, which may include hundreds of text comments, email requests, product ratings, and online posts. If a customer frequently comments on how a particular pair of pants “fits really well” and comes in a “fun color,” DALL-E can generate images of similar pants that the customer might want to buy. The stylist can then find similar products in Stitch Fix's inventory and recommend them to that customer.
Virtual tryouts are another example of generative AI improving sales and consumer experience. Paris-based Veesual provides e-commerce fashion brands with virtual try-on integration, meaning customers can choose their own models and clothes to try on.
As exciting as generative AI technology may be, businesses still want to err on the side of caution before fully delegating any core tasks to generative AI. However, given the speed at which this technology is developing and the explosive growth of its user base, neglecting to explore the possibilities this technology offers can be just as risky. Executives can start thinking about how their businesses can use generative AI now. Leaders can start with a few steps.
Fashion leaders should outline where generative AI can provide the most value to their business. Start by paying attention to which areas—creative design, merchandising, fashion show promotion, or customers—could benefit the most from generative AI. Leaders can then prioritize which generative AI use cases they should pursue based on how impactful the use cases will be to the business. Some impact measures include improving customer satisfaction scores and reducing customer service wait times.
Once the value is determined, use cases should also be prioritized based on their feasibility of implementation; deciding how to seamlessly use generative AI will depend on factors such as the technical skills of the team. Afterwards, the team should build a short-term roadmap 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 designers can update and use season after season.
It may be tempting to have some fun with generative AI, but harnessing its power requires extra effort. Fashion industry executives must intentionally build tools that deliver value rather than indiscriminately experimenting with existing tools.
In a previous article, we listed some of the risks of using generative AI. One is that the legal parameters surrounding the use of generative AI are still being resolved. Designers are sometimes criticized for creating derivative works and copycat designs. Who owns the intellectual property and creative rights to AI-generated works? These works may be based on multi-modal data sources, such as other designers’ past collections, and will be determined on a case-by-case basis until strong legal precedent emerges. (While not involving generative AI, the high-profile battle between Hermès and artist Mason Rothschild over Meta Birkin NFTs, in which a judge ruled that the NFTs infringed on Hermès’ trademark, illustrates how when new technology emerges, Fashion brands may face legal difficulties.)
Another risk is bias and fairness in generated AI systems, especially around biased data sets, which could bring reputational consequences to brands that rely on the technology challenge. For example, a brand's reputation could be harmed if an image generation tool creates an ad campaign using inappropriate or offensive images that are then shared globally. Blaming AI companies for damage control may not help quell consumer anger.
There is also a risk that employees using generative AI are not fully aware of its shortcomings and may not be able to check for errors introduced by the technology. In this case, companies must regularly train employees and provide them with the resources they need to understand how to use technology.
While risk is unavoidable, managers can mitigate its potential impact by establishing processes to address risk, ethics and quality assurance.
Generative AI tools can add value to many different areas of a business, so educate and train employees, including designers, marketers, sales associates, and customer service representatives, to use This technology will be very important.
Some companies have launched AI-focused training. For example, Levi Strauss launched a machine learning boot camp in 2021 to train non-technical employees how to use machine learning in the company's design process. Employees who completed the project created new AI tools relevant to their jobs. One of Levi's goals in setting up the program was to increase the diversity of employees with technical knowledge so the company could spot issues that employees with traditional technical backgrounds might miss. The project also helps teams with different disciplines—such as design and engineering teams—communicate better and find common ground. In addition, Levi's has found that the program helps improve employee retention.
With AI-savvy employees, collaboration will take on new meaning. Leaders should consider: How do we define responsibilities and work together between technical and non-technical roles? Design and software engineering teams can establish weekly leadership meetings to develop quarterly roadmaps and work sessions between teams. Design leads can share their needs for certain insights and tools (perhaps tools to generate design changes from sketches), and the engineering team delivers those tools.
There’s no question that fashion businesses will have to invest in their workforce when it comes to leveraging generative AI, but they won’t have to build the applications or foundation themselves Model. Instead, fashion leaders can work with AI companies and experts to take action quickly. Fashion executives may work with businesses that offer new technology, such as Microsoft or OpenAI, or with partners that provide enabling capabilities, such as cloud computing or APIs.
While potential use cases for generative AI are quickly emerging, the technology’s future in the apparel and luxury goods industries is still being explored. But trying new tools today means endless possibilities tomorrow.
The above is the detailed content of Generative AI: Unlocking the future of the fashion industry. For more information, please follow other related articles on the PHP Chinese website!
![[Web front-end] Node.js quick start](https://img.php.cn/upload/course/000/000/067/662b5d34ba7c0227.png)
