The Fashion Industry Is Getting More Intelligent With AI

AI in the fashion industry PIXABAY

As long as humans have started to wear clothes, we would have the desire to express our individuality, and one way to achieve that is through fashion. The fashion industry is one of the biggest in the world, estimated at 3 trillion dollars as of 2018, representing 2 percent of global GDP. Much of brick-and-mortar traditional retail as well as online e-commerce is dedicated to the sale of clothing and fashion items. So much so that Amazon acquired shoe retailer Zappos for $1 Billion in 2010, and major retailers such as Walmart, Target, Amazon and others have themselves entered into the fashion retail business through their brands and brand partnerships. Despite the established nature of the fashion industry, AI is fundamentally transforming the industry from the way that fashion companies manufacture their products to the way they are marketed and sold. AI technologies are transforming the fashion industry in every element of its value chain such as designing, manufacturing, logistics, marketing and sales.

AI Helping to Promote and Sell Fashion Goods

The fashion industry is just as much about creating demand and brand awareness as it is about the manufacturing of fashion products. Clothing and apparel brands are constantly looking for new ways to get their goods in front of buyers and create awareness and demand in the market. Increasingly, fashion brands are using AI and machine learning to maximize users’ shopping experience, improve the efficiency of sales systems through intelligent automation, and enhance the sales processes using predictive analytics and guided sales processes.

Fashion brands are also starting to leverage conversational assistants through chatbots and voice assistant devices such as Amazon Alexa, Apple Siri, Google Home and Microsoft Cortana. Using conversational interfaces, fashion brands can gather data by asking customers questions, understanding customer desires and trends, diving deeper into their purchase patterns, and suggesting related and add-on items. For example, when a customer needs new shoes or a dress, instead of interacting with a website or mobile app, they can simply have a conversation with an intelligent conversational agent. Through back and forth dialog, the customer can find the optimal fashion product or accessory item. This interaction provides greater satisfaction for the customer and much more valuable information for the fashion brand.

In addition to conversational systems, AI is making its way into ecommerce and mobile apps. Customers are now able to take pictures of clothing they like or styles they want to imitate, and smart image recognition systems can match the photos to real-life items available for sale. Additionally, AI-enabled shopping apps allow customers to take screenshots of clothes they see online, identify shoppable apparels and accessories in that photo, and then find the same outfit and shop for similar styles.

AI-enhanced Fashion Design and Manufacturing

In the documentary “Minimalism”, they share that there can be up to 52 seasons for clothing. Given the constant changes in fashion and design, retailers need to consistently keep up with the most current trends and predict consumer preferences for next season. Traditionally, retailers base their estimate of current year’s sales on data from the prior year. This is not always accurate because sales can be influenced by many factors that are hard to predict, such as changing trends. AI-based approaches for demand projection, however, can reduce forecasting error by as much as 50 percent. 

Once the clothes are designed, AI technologies can also play a role in textile manufacturing. Fashion manufacturers are innovating the use of AI to help improve the efficiency of manufacturing processes and augment human textile employees. AI systems are being used to spot defects in fabric and ensure that the colors of the finished textile match with the originally designed colors. AI technologies such as computer vision technologies are allowing quality assurance processes to be more streamlined.

Whereas it used to be that only ecommerce giants such as Amazon and Walmart used machine-learning algorithms to figure out sales trends, now small retailers are also leveraging machine learning to understand this dynamic fashion market, which may provide them a better chance to succeed. Intelligent, AI-enabled systems can also help provide greater intelligence for fashion brands by identifying patterns and predictive analytics that can provide insight into fashion trends, purchase patterns and inventory-related guidance. One company at the forefront in innovation with AI applied to fashion is Stitch Fix, an online personal styling service. The company is using machine-learning algorithms to provide better customer experiences for customers and make their supply chain more efficient.

Machine learning technologies are also being applied to expediting logistics and making the supply chain more efficient. AI is being used to manage and optimize supply chains as well as reduce shipping costs and transit time. Machine learning algorithms are being used to make more accurate predictions of inventory demand and therefore reduce wastage or eliminate last-minute purchases to meet unexpected spikes in demand.

Computer vision enabled by machine learning is also being used to help spot fashion fakes and counterfeit products. Previously, spotting fakes required the trained eye of specialized customs or other enforcement officers. Now, AI systems can keep a consistent watchful eye on counterfeit products that look increasingly similar to the real ones. In this area, AI technologies are being applied by customs and border enforcement to help spot the validity of high-end products that are frequently counterfeited such as purses and sunglasses.

We are now seeing that AI technologies can add value in every part of the fashion industry, from the design process and manufacturing processes to sales and marketing of finished goods. The future of fashion is intelligent for sure.


https://www.linkedin.com/in/rschmelzer/

http://www.cognilytica.com

Ronald Schmelzer, columnist, is senior analyst and founder of the Artificial Intelligence-focused analyst and advisory firm Cognilytica, and is also the host of the AI Today podcast, SXSW Innovation Awards Judge, founder and operator of TechBreakfast demo format events, and an expert in AI, Machine Learning, Enterprise Architecture, venture capital, startup and entrepreneurial ecosystems, and more. Prior to founding Cognilytica, Ron founded and ran ZapThink, an industry analyst firm focused on Service-Oriented Architecture (SOA), Cloud Computing, Web Services, XML, & Enterprise Architecture, which was acquired by Dovel Technologies in August 2011.

Ron is a Parallel Entrepreneur, having started and sold a number of successful companies. The companies Ron has started and run have collectively employed hundreds of people, raised over $60M in Venture funding and exits in the millions. Ron was founder and chief organizer of TechBreakfast – the largest monthly morning tech meetup in the nation with over 50,000 members and 3000+ attendees at the monthly events across the US including Baltimore, DC, NY, Boston, Austin, Silicon Valley, Philadelphia, Raleigh and more.

He was also founder and CEO at Bizelo, a SaaS company focused on small business apps, and was Founder and CTO of ChannelWave, an enterprise software company which raised $60M+ in VC funding and subsequently acquired by Click Commerce, a publicly traded company. Ron founded and was CEO of VirtuMall and VirtuFlex from 1994-1998, and hired the CEO before it merged with ChannelWave.

Ron is a well-known expert in IT, Software-as-a-Service (SaaS), XML, Web Services, and Service-Oriented Architecture (SOA). He is well regarded as a startup marketing & sales adviser, and is currently mentor & investor in the TechStars seed stage investment program, where he has been involved since 2009. In addition, he is a judge of SXSW Interactive Awards and served on standards bodies such as RosettaNet, UDDI, and ebXML.

Ron is the lead author of XML And Web Services Unleashed (SAMS 2002) and co-author of Service-Orient or Be Doomed (Wiley 2006) with Jason Bloomberg. Ron received a B.S. degree in Computer Science and Engineering from Massachusetts Institute of Technology (MIT) and MBA from Johns Hopkins University.