Artificial intelligence (AI) has swept across various industries, potentially disrupting businesses via creative technologies, more effective operational procedures, and access to consumer and industry insights that provide a prospective competitive edge.
Initially, AI automation did not seem appealing for fashion executives to utilize in an industry founded on creative ability and expression. However, as we enter the hyper-digital age, these applications can transform businesses and generate significant industry growth and revenues compared to competitors using traditional methods.
Despite the fashion industry’s established nature, AI fundamentally alters the sector, from how fashion businesses make their items to promoted and sold. AI technologies are revolutionizing the fashion industry across the board, including design, production, shipping, marketing, and sales.
The usage of AI in the fashion business of 2020 has grown so entrenched that a high percentage of fashion stores that have not implemented AI are now risking insolvency. Consequently, the fashion and retail industries’ worldwide investment in AI technology is predicted to reach $7.3 billion per year by 2022.
Let’s look at some of these AI applications and how firms adopt unique methods to enhance their business models.
AI solutions for fashion design have been somehow neglected. Still, they have enormous potential for an industry that is rapidly automating its design and presentation processes during the pandemic and, most likely, after that. What are the new opportunities for designers and businesses, and why is creative AI so underutilized?
Initial AI implementations have focused on measurable business demands. Creativity is significantly more challenging to quantify and far more prone to lag. But as more research scientists develop new models for creativity, the potentials of the technology become more evident. AI models could become fashion designers’ best friends very soon.
The AI models that have been employed to generate novel apparel design are generative adversarial networks (GANs), a type of Machine Learning (ML) in which two adversarial models are trained concurrently: a generator (“the designer”) that learns to create images that look real and a discriminator (“the design critic”) that learns to distinguish between real and fake photos.
The generator becomes better at creating legitimate images during training, while the discriminator becomes better at detecting fakes. Computer-generated images and movements can appear believable (and potentially aesthetically pleasing) to the spectator due to the imaginative use of technology.
But AI can aid with more than just generating new designs. By gathering more sophisticated data, fashion manufacturers use technology to understand consumer desires better and make better garments. Zalando, a German fashion marketplace, was one of the pioneers of AI-powered fashion design based on the customer’s favorite colors, textures, and other style preferences in collaboration with Google.
Synflux is another company blending fashion and AI. They’re collaborating on a project called Algorithmic Couture. Synflux uses ML to create optimal fashion pattern modules, modeled using computer-aided design tools.
Many of us have a closet full of items we never wear because they are uncomfortable, look cheap, don’t suit our body type, or match the rest of our wardrobe. It’s unavoidable when so many online businesses utilize photographs that entice us to buy yet don’t necessarily provide a true-to-life depiction of a garment.
AI-enabled technologies like augmented reality (AR) and virtual reality (VR) are now trying to solve the problem above by bridging the gap between online and in-store purchase experiences.
According to some analysts, the pandemic will hasten the shift to online shopping by five years. Even again, it is doubtful that fashion will become completely touch-free: many people still want to go to actual malls where they can explore and try on real clothes. Retailers may appeal to this kinesthetic demographic by utilizing in-store AR and VR solutions. These technologies enhance the shopping experience by making it more immersive and pleasant.
Fashion businesses use AR technology to provide new features to traditional and online purchasing. As a consumer, you may experiment with different styles, textures, and colors, and different shoes, purses, and jewelry items to complete your appearance.
An example of this technology is the Wanna app, which employs augmented reality to allow you to try on several pairs of sneakers. Simply select a pair of shoes from the collection of 3D models, point your camera at your feet, and presto — you’re virtually wearing your selected footwear.
Consider the following example: You’re out for a walk when you notice someone wearing the most beautiful dress you’ve ever seen. You want the same, but you don’t know what brand they’re wearing or where you can get it. You can search online, but you’ll just get a few, primarily irrelevant, results, and you’ll be no closer to finding your new favorite dress.
In this example, visual search, like text-based search, scans and recognizes user-input photographs and delivers the most relevant search results. Customers can search for what they want without explaining it, making purchasing online more straightforward and gratifying.
Some AI-enabled apps allow users to take screenshots of online clothes, detect shoppable gear and accessories in the image, and then discover the same outfit and shop for similar fashions.
Apps like Pinterest or Google Photos implement a visual search feature. Still, only a few specialize in apparel and help users find more target items. One example is, Lykdat, a reverse image tool to find fashion goods using photographs. Consider it the Shazam of clothing. The customers simply submit a photo of the apparel items. The algorithm will return a list of online retailers that sell these goods.
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Improved customer personalization
Personalization is essential for business success. Because of considerable data innovation, there is an amount of customer data available to be accessed and studied. When integrated with business data, deep learning technologies such as AI and ML enable fashion firms to follow individual client buying behavior.
Marketers increasingly leverage growing technology’s knowledge and computational skills to comprehend shoppers’ expectations and influence their experience based on purchases, favorite colors, textures, and other style preferences.
In a crowded arena, one-of-a-kind, personalized advertisements cut through the clutter, and customers are willing to give personal information for a more customized experience. Already, product suggestions based on such algorithms account for 35% of what people buy on Amazon.
One of the world’s leading athletic footwear and athleisure businesses, Nike is not surpassed in the personalization game. Nike has pushed personalization to the next level by allowing customers to design their sneakers with the Nike By You platform, which they define as a co-creation service.
Computer Vision powered by ML is also used to detect fashion forgeries and counterfeit items. Detecting fakes formerly required professional customs or other law enforcement personnel’s trained eye.
AI algorithms can now monitor counterfeit items becoming more similar to the real thing. Customs and border officials are employing AI technology to help determine the authenticity of high-end products that are frequently counterfeited, such as handbags and sunglasses.
While browsing enormous internet marketplaces, ordinary customers may struggle to recognize counterfeit items from a third-party seller. When a buyer buys a product that appears legitimate but performs poorly, it can leave a bad taste and harm their opinion of the brand.
Some organizations use AI to examine and identify potentially counterfeit products by relying on massive datasets and pictures from numerous online marketplaces.
Entrupy has created artificial intelligence-powered authentication solutions for firms that acquire and sell high-value commodities. Their cutting-edge authentication solutions are powered by a mix of ML and Computer Vision, as well as a proprietary database containing millions of photographs of genuine and counterfeit items collected from across the world.
Its technology is used to authenticate handbags and accessories from several luxury brands such as Louis Vuitton, Chanel, and Hermès by major resellers and professional buyers of luxury goods.
Trend forecasting is a field that focuses on projecting a market’s future. Thus, fashion forecasting is the branch of the fashion business concerned with projecting new fashion trends—colors, styling techniques, fabric textures, and so on—that will pique consumer interest.
Fashion forecasters generate trend forecasts, which product developers utilize to design new garments and accessories for businesses.
But an individual fashion trend forecaster would never be able to examine that quantity of data in time for the following season, so utilizing AI to perform the heavy work frees up forecasters to search for emerging trends in less traditional sectors, such as movies, television, or even politics.
To foresee trend evolutions, Heuritech created an in-house deep-learning technique that detects what is known as early signals. Early indicators include slight shifts in the activity among edgy influencers, who frequently give trends life.
Heuritech helps companies to anticipate demand and trends more precisely, manufacture more sustainably, and gain exceptional competitive advantage by leveraging powerful AI to transform real-world photographs posted on social media into relevant information.
Social media isn’t the only thing that’s changed the way trend forecasters make their forecasts; many businesses also use artificial intelligence to find new trends. Fashion Snoops, for example, employs artificial intelligence to scour the internet for buzzwords and new terminology that have the potential to evolve into something stylish.
For fashion brands, AI and ML can be game-changer. It will allow for faster customer service, better user experiences in the store environment, and more sustainable business practices. The future of fashion is looking bright!