The Invisible Infrastructure of Circular Fashion
AI is becoming the connective infrastructure that enables companies to design, produce, distribute and recover products in ways that are both economically competitive and environmentally responsible. This is one of the insights drawn from interviews with industry experts included in the SDA Bocconi Monitor for Circular Fashion 2025–2026.
The most effective use of AI emerges when technology is embedded in a company’s strategic core rather than treated as a series of experimental pilots. In fashion, generative AI is increasingly integrated into design processes, allowing for the simulation of materials, the prediction of environmental impacts and the optimization of durability and reparability.
From Traceability to Consumer Engagement
Beyond fashion design, AI is also advancing the traceability agenda: by combining data analytics with blockchain and tagging technologies, fashion companies can now track materials throughout the value chain and automate life-cycle assessments in compliance with emerging European regulations such as the Digital Product Passport. Finally, AI is enhancing consumer engagement by personalizing experiences in repair, resale and rental models, encouraging longer product lifespans and circular fashion consumption patterns.
Specifically, the integration of AI into fashion resale is laying essential groundwork for the future of secondhand retail. AI enhances product discovery and streamlines the listing process for sellers, making resale platforms more efficient and user-friendly. It also boosts consumer engagement by dynamically adapting product displays in real time and delivering personalized recommendations aligned with current trends. Through AI-driven personalization, resale platforms can better understand individual shopping behaviors and offer more relevant suggestions, strengthening connections with users.
AI already plays and will increasingly continue to play a crucial role in tracing and managing complex supply chains by minimizing waste and improving energy efficiency across the entire value chain. However, this transformation comes with both opportunities and challenges.
Obstacles, Competencies and Governance for Sustainable AI
Several barriers continue to limit large-scale adoption. Data quality and fragmentation remain the most significant technical constraints. Many fashion companies lack integrated data systems, reducing the reliability of AI outputs. Cultural resistance is another major obstacle: creative professionals often perceive AI as a threat to artistic identity, while technical teams may struggle to communicate effectively with design and product functions.
Economic factors also play a role. Integration costs remain high, and the return on investment is still uncertain for small and mid-sized enterprises. Ethical and environmental concerns add further complexity, including the high energy consumption of large-scale AI models and fears of job displacement.
The effective implementation of AI for sustainability requires a new combination of capabilities and governance structures. Organizations must develop strong technical expertise in data science and machine learning, but these skills alone are not sufficient. They must be complemented by managerial and organizational competencies such as change management, design thinking and cross-functional coordination, as well as deep industry knowledge in areas including supply chains, materials science and life-cycle assessment.
The diffusion of AI literacy across all organizational levels is also very important. Employees need not only technical training but also a clear understanding of ethical AI use, environmental implications and the alignment of AI initiatives with broader sustainability goals.
When applied responsibly, AI does not replace human intelligence; it amplifies it. Used in this way, AI enables fashion companies to create long-term value while respecting both people and the planet.
