In the ever-evolving landscape of the fashion industry, the convergence of artificial intelligence (AI) and sustainable fashion stands out as a pivotal and transformative force. As the trillion-dollar global fashion sector grapples with its environmental footprint, AI technologies have become instrumental in reshaping the way garments are designed, produced, and consumed. Cutting-edge algorithms are addressing longstanding challenges, from supply chain inefficiencies to textile waste. From predictive analytics optimizing production to AI-driven design innovations, join us on a journey into the evolving realm where technology and sustainability converge, offering a glimpse into the future of fashion industry striving to redefine its ecological impact through intelligent solutions.
The Environmental Toll of Fashion – From Landfills to Global Carbon Emissions
The fashion industry, with a global valuation of approximately $2.4 trillion and employing over 300 million individuals across its value chain, faces substantial challenges contributing to economic losses and environmental impacts. Annually, an estimated $500 billion in value is lost due to insufficient recycling, resulting in garments being discarded in landfills prior to sale. This industry is a significant contributor to global carbon emissions, accounting for 2 to 8 percent. Textile dyeing is the second-largest water polluter globally. The fashion sector is responsible for 24 percent of insecticides and 11 percent of pesticides. Shockingly, every second sees the equivalent of one garbage truck of textiles being landfilled or incinerated. Without intervention, projections indicate that by 2050, the fashion industry could consume a quarter of the world’s carbon budget.
The UN Alliance for Sustainable Fashion highlights alarming statistics, including the industry’s annual consumption of approximately 215 trillion liters of water. Material losses amounting to $100 billion occur each year due to underutilization. Additionally, textiles contribute to about 9% of the annual microplastic losses to the oceans.
Consumer Sentiment and Sustainable Fashion
The Harris Poll research commissioned by Google Cloud highlights a notable change in sentiment across sectors like apparel, electronics, beauty, and food. 82% of shoppers want brands aligning with their values; three-quarters would part ways if values clash. More than half (52%) show a keen interest in supporting sustainable brands, and 66% actively seek eco-friendly options. Moreover, 55% are willing to pay extra for products committed to sustainability. As per research released in this publication, the worldwide market for green technology and sustainable development is anticipated to experience substantial growth, expanding from $11.2 billion in 2020 to a projected $36.6 billion by the year 2025.
The sustainable fashion industry has surpassed $6.5 billion and is set to grow. Projections suggest $10.1 billion by 2025 and a remarkable $15 billion by 2030. Ethical fashion shows an 8.3% annual growth, potentially reaching 9.7% by 2030. Sustainable products are expected to grow 5.6 times faster. Notably, the Asia Pacific region holds 36% of the global ethical fashion market. Insights predict the eco-friendly clothing segment to reach $1,574.2 million by 2025. Additionally, organic clothing, growing at 16.2% annually, is the fastest-growing sector. In 2022, the organic baby clothing market hit $1.79 billion, projected to reach $2.81 billion by 2029. Furthermore, sustainability in textiles could create 18 million jobs by 2030. Solving fast fashion challenges might boost the global economy by $192 billion by 2030.
AI and Sustainable Fashion
McKinsey’s analysis suggests that within the next three to five years, generative AI has the potential to contribute a conservative estimate of $150 billion, with the possibility of reaching up to $275 billion, to the operating profits of the apparel, fashion, and luxury sectors. The impact of generative AI in the fashion industry extends across various aspects, from co-designing to expediting content development processes, carving out new realms for sustainable fashion and creativity. Capable of processing diverse “unstructured” data such as raw text, images, and video, generative AI can produce a spectrum of new media forms, including fully-written scripts, 3-D designs, and lifelike virtual models tailored for video campaigns.
Here are several ways in which AI contributes towards sustainable fashion:
1. Supply Chain Optimization:
AI helps optimize the fashion supply chain by providing better forecasting and demand planning. By employing appropriate tools, fashion houses and owners of apparel stores can simplify the entire operational chain, starting from procuring materials for the latest fashion trends to overseeing inventory, planning production, and fulfilling orders, all through a user-friendly central hub. The implementation of automated workflows not only saves time and minimizes errors linked to manual data input but also enhances precision and expedites processes. This reduces overproduction, minimizes waste, and lowers the environmental impact associated with excess inventory. In conjunction with AI-driven enhancements in supply chain optimization, the integration of augmented robotic automation for warehouse operations and inventory management is also being pursued. Real-time analytics, incorporating insights from augmented reality (AR), are being employed to elevate the efficiency and precision of the overall supply chain processes.
An example of this is Recloseted. They conduct analyses, including supply chain assessments, life cycle assessments (LCAs), carbon emission evaluations, and more. Their services encompass developing sustainability strategies and Science-Based Targets initiatives (SBTi), setting up reporting mechanisms, communicating sustainability endeavors, and identifying sustainable materials and ethical manufacturers.
2. Predictive Analytics:
The accumulation of excess inventory in the fast fashion sector leads to substantial waste, given the rapid shifts in fashion trends. A prominent application of Artificial Intelligence in addressing this issue involves leveraging advanced data analytics and machine learning for sales forecasting. This approach aims to enhance predictions related to sales volumes, clothing trends, and customer behavior, ultimately mitigating the surplus of unsold clothing each season. This dual benefit of cost savings and environmental friendliness has spurred extensive research in this domain, with the goal of refining and perfecting the process. According to CB Insights, in 2018, a Highstreet brand reported unsold clothes worth $4B globally. Fashion brands, leveraging Ai and ML technologies, can strategically determine the optimal quantity of clothes to produce, thereby avoiding overstocking.
Heuritech, for instance, utilizes its Artificial Intelligence platform to systematically collect and analyze data from millions of social media images. This data is then harnessed to predict product trends and behavior patterns. Heuritech’s technology enables the forecasting of product quantities and the optimization of product assortment. This approach assists fashion and sportswear companies in producing only what aligns with consumer preferences, reducing the number of unsold products. The result is increased sell-through, enhanced profitability, and heightened brand relevance.
3. Personalized Design and Production:
The fashion industry’s adoption of fast fashion and mass production models has led to a notable increase in waste and elevated return rates. Recognizing these challenges, creative directors in both mass-market fashion retailers and luxury brands are increasingly exploring the integration of generative AI to mitigate these issues. AI enables personalized design and production, allowing brands to create garments tailored to individual preferences. This reduces the likelihood of garments being discarded and contributes to a more sustainable, consumer-driven approach to fashion. Generative AI empowers designers to analyze various forms of unstructured data in real-time. They input sketches and specific details, such as fabrics, color palettes, and patterns, into a generative AI-driven platform. This platform then autonomously generates a diverse range of designs, providing designers with the opportunity to explore a wide array of styles and aesthetics.
An illustrative example is the Laboratory for Artificial Intelligence in Design (AiDLab), the pioneering research platform focused on the fusion of AI with design. A fashion show organized by a group of fashion designers based in Hong Kong, affiliated with AiDLab, highlighted designs enhanced by generative AI. Additionally, designers are actively leveraging tools from tech companies like Cala, Designovel, and Fashable to harness generative AI’s potential, fostering creativity, experimenting with multiple design variations without the need for costly samples, and significantly expediting their design processes.
4. Ai Chatbots & Sustainable Fashion
In the realm of sustainable fashion, the integration of AI powered chatbots and voice assistants stands out as a transformative force. These intelligent tools not only provide personalized styling advice, recommendations, and customer support but also help retailers optimize inventory and reduce waste by predicting consumer preferences and adjusting stock accordingly. This helps prevent overstock situations, where excess inventory is often marked down or disposed of. Fashion chatbots empower brands to sustain conversations, offer post-store styling suggestions, train sales associates in customer engagement, tailor communications, and analyze real-time consumer profiles. Fashion chatbots seamlessly embed themselves into e-commerce websites and social media platforms, offering personalized shopping experiences aligned with sustainability principles. Notable brands such as Victoria’s Secret, Louis Vuitton, Tommy Hilfiger, and Burberry have already embraced fashion chatbots to enhance customer service efficiency. Furthermore, the utilization of chatbots for basic consulting services aligns with the cost-efficient and eco-conscious ethos of sustainable fashion practices.
5. Virtual Try-Ons:
In McKinsey’s 2019 Apparel CPO Survey, 83% of participants anticipated a decline in the use of physical samples in favor of virtual samples by 2025. This shift, accelerated by the impact of COVID-19, aligns with the projected growth of the Augmented Reality (AR) market, set to reach $198 billion by 2025 according to Statista, up from $3.5 billion in 2017. The synergy of AR and 3D body scanning is fostering innovations like virtual try-on technology, forecasted by 3D Insights to revolutionize the fashion retail experience. Virtual try-ons provide consumers with a transparent understanding of a product’s dimensions and features, reducing the likelihood of returns, which is environmentally impactful, and enhances the overall efficiency of the retail process.
An exemplary application of AI in this context is demonstrated by Bodify, an app that utilizes computer vision to analyze shoppers’ uploaded selfies or photos, determining their measurements and offering personalized recommendations to suit their physique, style, and budget. This not only enhances the shopping experience but also aids retailers and manufacturers by providing accurate garment sizes, minimizing returns. Another noteworthy example is 3DLOOK’s YourFit solution, an integrated app that combines AR clothing try-ons with size and fit recommendations, contributing to a more personalized and efficient shopping process.
6. Waste Reduction in Retail:
AI is playing a pivotal role in waste reduction within the retail and fashion industry. Innovations like AI-driven inventory management and sizing solutions, exemplified by platforms like MySize, streamline operations, leading to fewer overstocks and reduced production waste. Through a simple smartphone camera, MySize enables customers to accurately choose clothing sizes during online shopping. This not only minimizes returns and waste but also aligns with environmental sustainability by reducing the carbon footprint associated with fewer returns and shipping processes. The ripple effect extends to reduced shipping requirements, diminishing the carbon footprint associated with return logistics. By seamlessly integrating AI technologies, the industry is making significant strides towards a more sustainable and environmentally conscious retail landscape.
7. Ai Generated Fashion Models:
AI-generated fashion models are proving to be a game-changer in the journey toward a more sustainable fashion industry. These virtual models, crafted through sophisticated algorithms, offer a sustainable alternative to traditional, resource-intensive photo shoots. By eliminating the need for physical models, clothing samples, and elaborate sets, AI-generated models significantly reduce the environmental impact associated with the production of promotional materials. Moreover, these digital avatars provide a platform for designers to experiment with diverse styles, colors, and patterns virtually, minimizing the need for physical prototypes.
Nigerian visual artist Malik Afegbua garnered attention by sharing images of seniors strutting down a virtual fashion runway on social media. The surprising revelation was that this groundbreaking fashion show never occurred in reality. The entire event, including the images, models, and designs, showcased the remarkable capabilities of Artificial Intelligence, highlighting its influence and expansive possibilities.
Paving the Way to a Sustainable Fashion Revolution with AI
In essence, can we consider AI as the future of sustainable fashion? Undoubtedly, yes. AI emerges as a potent force, equipping us with advanced tools to curtail waste in clothing production. As we step into the next decade, the transformative impact of sustainable fashion technology on materials, products, and the overall consumer experience is set to revolutionize the industry. This holds significant promise for substantial reductions in waste, marking a pivotal stride towards a more sustainable and environmentally conscious future.
