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How AI Can Revolutionize Sustainability of Fashion

In recent years, the fashion industry has undergone a profound shift towards sustainability, driven by increasing consumer awareness and environmental concerns. As brands strive to minimize their ecological footprint and accelerate sustainability of fashion, one technology has emerged as a powerful ally in this endeavour: Artificial Intelligence (AI). 


While we have already seen brands like Zegna leveraging AI-powered recommendations to suggest products and outfits to their high-end clientele.

“What we did with Microsoft is just the beginning. We are just scratching the surface. We are a master of the art…But in order to elevate the entire value chain, you need science,” says Edoardo Zegna, chief marketing, digital and sustainability officer

From design to production and beyond, AI is now revolutionizing the sustainability of fashion, offering innovative solutions to complex sustainability challenges.


In this post, we'll explore how AI is reshaping the landscape of sustainable fashion and how brands and retailers can supercharge their teams with advanced AI to drive momentum in sustainability.


1. Sustainable Design and Product Development

 

AI is transforming the design process by leveraging data analytics and machine learning algorithms to drive sustainable innovation. By analysing trends, consumer preferences, and historical sales data, AI can generate insights that inform the creation of eco-conscious collections. It can rapidly prototype and iterate on design concepts, reducing the time and resources traditionally required for product development. Additionally, AI-driven tools can streamline the creation of detailed tech packs and mock-up boards, empowering designers to bring their visions to life while minimizing waste.

 

Carbon Trail’s AI Copilot offers product designers and product development teams to embed sustainability data in their day-to-day decision-making, allowing teams to design, simulate, and compare the environmental impact of their next collection. Here is a short demo of the Copilot in action:


 

2. Sustainable Sourcing

 

One of the key challenges in sustainable fashion is sourcing materials that minimize environmental impact. Carbon Trail’s proprietary AI algorithms can analyse vast databases of material properties, supplier information, and sustainability certifications to recommend the most eco-friendly options. By considering factors such as biodiversity, water usage, chemical toxicity, and carbon footprint, AI can help brands make informed decisions about sourcing materials that align with their sustainability goals.

 

Further, Carbon Trail’s AI platform can help brands compare and benchmark suppliers on sustainability, allowing them to source yarn or fabrics from those who are rapidly investing in sustainability transformation. We covered this topic in detail on how brands can boost supplier engagement to decarbonize their supply chain.

 

3. Streamline Supply Chain Data

 

The fashion supply chain is complex, comprising multiple stakeholders and processes across various geographies. Managing and analysing supply chain data is crucial for identifying inefficiencies, reducing waste, and improving overall sustainability performance. AI streamlines this process by aggregating and analysing vast amounts of supply chain data from third party tools in real-time. If you are struggling to leverage Higg FEM data, you may be interested in reading our article on how to power carbon accounting and LCAs with primary Higg FEM data.

 

By integrating with AI-powered analytics platforms like Carbon Trail, fashion brands can build traceability into supply chain, from raw material sourcing to manufacturing and distribution. This visibility and transparency enable proactive decision-making, risk management, and optimization of supply chain operations, ultimately enhancing sustainability outcomes.

 

4. Process Unstructured Data at Scale

 

Brands and Retailers have been struggling with handling unstructured data related to products, such as text, images, and supply chain details. This presents a significant challenge for fashion brands seeking to extract actionable insights. New AI technologies, particularly natural language processing (NLP), excel at processing unstructured data at scale. NLP algorithms analyse textual data from sources like sustainability reports, extracting valuable insights related to consumer preferences, sentiment analysis, and emerging trends.

 

Carbon Trail’s AI platform can handle data of millions of products, enabling brands to identify sustainable materials, track product authenticity, and enhance supply chain visualization. Further, by harnessing the power of AI to process unstructured data, fashion brands gain deeper insights into consumer behaviour, market trends, and sustainability opportunities, driving informed decision-making and innovation.

 

5. Automate Carbon Accounting and Decarbonization

 

Corporate carbon accounting is essential for assessing and mitigating the environmental impact of fashion products throughout their lifecycle. AI offers a solution by automating the carbon accounting process, which involves measuring, monitoring, and reporting greenhouse gas emissions. By integrating AI algorithms with data from various sources such as manufacturing processes, transportation, and energy consumption, fashion brands can accurately quantify their carbon footprint. AI-driven carbon accounting systems like Carbon Trail provide real-time insights into emissions, enabling brands to identify areas for improvement and implement targeted sustainability initiatives.

 

Scenario planning and decarbonization in fashion
AI powered decarbonization simulation

6. Automate Sustainability Reporting

 

Meeting regulatory requirements and industry standards for sustainability reporting can be a daunting task for fashion brands. AI-powered software can automate the process of collecting, analysing, and reporting sustainability data, saving valuable time and resources. By aggregating data from various sources, AI algorithms can generate comprehensive reports that comply with regulations such as the Corporate Sustainability Reporting Directive (CSRD) and the Carbon Disclosure Project (CDP). This enables brands to track their progress, highlight areas for further improvement, and demonstrate their sustainability commitment to stakeholders.

 

7. Custom Charts and Analytics

 

AI algorithms are adept at processing large volumes of data and extracting actionable insights. In the context of sustainability, AI can generate custom charts, tables, and analytics that enable brands to visualize their environmental impact and track key performance indicators. By identifying patterns and trends, AI-driven analytics empower sustainability teams to make data-driven decisions that drive positive change. Moreover, generative AI can help optimize textile production processes and inventory management.

 

Sankey chart of fashion product LCA
Material level impact visualization via AI Copilot

8. Empower Circularity and Sustainability of Fashion

 

The concept of circular fashion, which aims to minimize waste and maximize the lifespan of garments, is gaining traction within the industry. AI plays a crucial role in achieving circularity by optimizing garment fit and reducing return rates. By analysing consumer data and preferences, AI algorithms can customize clothing to fit distinct body types, reducing the need for alterations and minimizing the likelihood of returns. This not only enhances the customer experience but also promotes sustainable practices by reducing textile waste.

 

In conclusion, AI represents a game-changing technology that is driving the fashion industry towards a more sustainable future. By leveraging data analytics, machine learning, and automation, AI enables brands to design products, source materials, report sustainability metrics, and optimize operations in ways that were previously unimaginable. As fashion continues to evolve, embracing AI as a catalyst for sustainability will be essential for brands looking to thrive in a rapidly changing landscape.


 

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