Back in 2023, McKinsey’s Holger Harreis and Roger Roberts already estimated that AI might generate an additional $275 billion in profits for the fashion, apparel, and luxury sectors, over the next three to five years. From product development to data-driven supply chain optimisation, there are many operational tasks that AI can improve fundamentally. In this article, we will outline the areas in which AI is already developing into a game changer for the fashion industry, particularly where sustainability is concerned.
Waste reduction by accurate trends prediction
If you are already vegan (or thinking about becoming one), chances are you are as concerned about reducing waste as you are about animal welfare. Clothing alone makes up to 7% of global landfill waste each year. The textile industry generates 92 million tons of textile waste each year. According to ICMGLT, the fashion industry produces more than 100 billion apparel items each year. Approximately 30% of these are never sold. Sold items are worn less, and sometimes not even once, before they end up in a landfill. According to the Ellen MacArthur Foundation, over the last two decades, the average number an item is worn before it’s thrown away has plummeted by 36%.
Clothing and textile overproduction is driven by global pressure to sell and profit on a massive scale. Short-term production and investment schemes are favoured over long-term and sustainable ones. As a result, developing countries are flooded with more clothing and textile waste than any landfills were projected to take. Accra’s landfills are filling up in three years instead of the projected 25. Dyes and other chemicals from the waste seep into the soil and wash into local water bodies, perpetuating a circle of health and environmental issues that are impossible to contain unless there is a global and fundamental shift in the way fashion is produced.
As social, cultural, economical, and political issues get increasingly complex and unpredictable, so do fashion and consumer behaviour trends. Overproduction is fashion’s way of making sure that every time a micro- or nano-trend pops up, everything is already available within a click or two. Social media turned the trends prediction business into a debate rather than a forecast, though it is also within this extensive ecosystem that data can be collected and analysed for more accurate predictions. Reliable data analysis has become a key navigation tool on any market. According to the Influencer Marketing Hub, 45% of marketers are already using AI for data analysis to improve the precision of trend forecasting, and keep track of real-time developments in changing market conditions and consumer behaviour. AI advanced algorithms can identify fashion’s highly complex and dynamic demand patterns, and perform predictive analysis within historical and contemporary data (Heuritech, Stylumia). Accurate trends prediction will provide a road map for fashion brands to focus their production on what’s backed up by data. For a brand, selling more by producing less means either more profit, or more funds to invest in long-term sustainability solutions. For the planet, it might finally turn out to be the solution to reduce future waste, since the one that’s currently piling up is already more than any developing country could ever re-use or effectively manage.
Further down the road, AI tools can provide solutions for reducing waste and enhancing Product Lifecycle Management, both at the production (Logility, Techpacker) as well as the consumption stage (Green Story). Another set of AI tools can be used to collect and analyse data on specific dynamics between local inventory and demand (Modern Technologies, Fishbowl, SkuVault). The findings, if integrated vertically, will enhance order accuracy against production plans. If integrated horizontally across different locations, these will contribute significantly to optimised inventory distribution, where one region’s potential waste is another’s sought-after treasure.
Increasing conversion with data-driven shopping experiences
AI algorithms are particularly useful for focused tasks where data is pulled out of a diverse data pool and analysed to provide insights on a specific product or activity. For a fashion brand, an efficient AI tool should include media monitoring, social listening and consumer insight tools (Brandwatch, Meltwater, TalkWalker, IBM Cognos Analytics). Media monitoring tools identify and analyse patterns and abnormalities around a brand’s general market activity. The more advanced a tool, the more data it can track for critical brand mentions within a specified timeframe. Social listening and consumer insights tools monitor and process real-time traffic and content on social media, providing a data-driven insight on trends, patterns and shifts that are crucial for planning and optimising both production as well as communication strategies. Specific tools (Anyword, Copy AI, Braze, Japer AI, Pulsar, Writesonic) have been developed for brands to create content for communication and marketing campaigns. These tools are also equipped with analytic functionalities that provide detailed bullet points should a brand wish to employ some tailored solutions to address specific customer targets.
Contemporary e-commerce is all about data and the way it is used. The same mechanism that collects data to a point where it fuels bugging discussions about online privacy is also responsible for providing data that is crucial to analyse customer behaviour and preferences. AI tools are already personalising the shopping experience by recommending products based on one’s search, purchase history and returns, and targeting specific suggestions and advertisement. The rising popularity of the curated shopping experience, where the reseller is meeting the shopper’s need to have the offer curated to their more-or-less specified preferences, is a customer behaviour macro trend that is enabled by the ever improving AI. A fashion brand can benefit tangibly from engaging a curated selling mode as the data collecting and analysing tools provide a hands-on insight of what’s trending, selling and churning.
Tools that provide enhanced online shopping experience such as body measuring, virtual fitting, and clothing simulation on avatars are designed to keep a customer engaged and entertained, which will ultimately result in high conversion rate (Banuba, Google “Try On”). Visual recognition tools serve multiple purposes, from enabling user-friendly visual search to capturing facial expressions and calculating their relevance against actual conversion rates (Canto, Folio 3, Royal Cyber). The growing popularity of these tools is proof that an extremely focused, customer-centred approach in which the customer, while being borderline exploited for personal and behavioural data, has the ultimate say in how much they will contribute to the big picture, even if it involves getting some fluency in navigating multiple layers of privacy settings.
Product development and materials innovation
Generative AI, which is engineered to draw on complicated algorithms to mimic human invention skills, is already being developed into tools that can create and break down multiple prototypes within a few sessions (Artiphoria, Cala, Designovel, Fashable, Repsketch, Vmake). By replacing extensive and tedious pattern making with data-driven research and development, generative AI holds the promise of levelling the playing field between local brands and global players. Prototypes would otherwise take the whole product development teams weeks, if not months, to deliver. Playing God with data input requires virtually no additional cost, giving the product developers full agency over the whole creative and optimisation process. AI tools can apply a wide range of experimental parameters to a prototype that might result in unexpected improvements and innovations. Specific product performance criteria can also be defined for AI to reverse engineer the entire production and optimisation process.
Generative AI can translate sketches to prototypes, and prototypes into real-life cuts and patterns. It can develop correct measurements and size charts out of specific output criteria, such as fabric properties or specific size requirements. With correct prompting, it can modify and optimise either a product or an entire collection for specific market requirements. As it improves, it will also become a precise optimisation tool that will help the decision makers develop the most efficient strategies across the whole vertical chain of product development, production, and distribution, and tailor them to specific objectives and goals.
Following the trend to move away from animal-derived fabrics while reducing the negative impact to the planet, AI data collecting and analytic tools are powering current research on more sustainable ways of recycling cotton, polyester, and blends. Many companies have already used generative AI to develop vegan materials such as spider silk, mycelium leather, or seaweed fiber, and to provide these materials with advanced qualities such as waterproofness, durability, heat resistance, and biodegradability, which would make them more attractive and competitive on the market. Generative AI’s deep learning and complex data processing are leveraged in the development of smart textiles that can monitor human physiology and physical activity. Innovations won’t stop at data collecting and analysis though. The new generation of smart materials is designed to improve the human body’s functions and response to various conditions, both the everyday and the extreme.
AI tools can identify areas for efficiency improvement in quality and quantity management schemes. AI can perform inspections programmed after specific quality and/or quantity control processes, and generate automated documentation and reports with minimal risk of compromising the results (Advantive, Techpacker). Fashion brands can browse from the growing number of AI tools that optimise various aspects and processes in the supply chain. These include tools that source potential suppliers that match transparency and sustainability criteria (Supptrail), help navigate communication and negotiation procedures (Ironclad), and, last but not least, address potential disruptions in the whole supply chain (IBM Sterling® Supply Chain Intelligence Suite). With the tangible benefits that AI is starting to bring to the fashion table, and with its extensive integration and optimisation potential, artificial intelligence might be just what fashion needs to start delivering on its sustainable promises.
Further reading:
www.influencermarketinghub.com/ai-trends-analysis-tools/
www.textiletechsource.com/2024/02/26/ai-and-textiles-its-already-here/
www.mckinsey.com/industries/retail/our-insights/generative-ai-unlocking-the-future-of-fashion