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How artificial intelligence is transforming sustainability, traceability, and circularity in fashion through compliance, efficiency, and innovation
Sustainability
02 December, 2025
Table of contents
Between 2023 and 2025, artificial intelligence (AI) has emerged as a central enabler in the fashion industry’s sustainability agenda. The convergence of heightened regulation - led by the EU Green Deal and its associated measures - and intensifying consumer and investor pressure has forced fashion companies to rethink business-as-usual. The incorporation of AI technologies now plays a dual role: as a compliance engine (being able to track and report in line with evolving standards such as the Corporate Sustainability Reporting Directive [CSRD] and the emerging Ecodesign for Sustainable Products Regulation/Digital Product Passport regime) and as an efficiency multiplier across supply-chain, materials, forecasting and circularity processes.
Post-2023, sustainability pressure in fashion has accelerated: public-policy timelines were shortened, disclosure demands ramped up, and brands faced increased scrutiny around waste, emissions and traceability. In this environment, AI is no longer a nice-to-have digital tool but a strategic necessity to manage complex value chains, optimise resource flows and satisfy regulatory expectations. AI becomes the “systems backbone” enabling SKU-level impact tracking, real-time data analysis, emissions measurement, digital product passports and smarter circular loops.
The remainder of this article analyses: key statistics (2023-25); real case studies of AI applications across inventory forecasting, traceability/DPP, circularity/recycling, sustainable materials and returns; the regulatory acceleration; challenges and risks; and a forward outlook to 2030.
Here are credible, recent figures illustrating how AI and sustainability intersect in fashion:
According to The Business Research Company, the “AI in fashion” market grew from $1,26 billion in 2024 to a projected $1,77 billion in 2025 (≈40% year-on-year) - demonstrating rapid investment into AI tools in fashion.
A survey reported in the McKinsey & Company “State of Fashion” (2025 edition) noted that 75% of fashion executives were prioritising AI for demand forecasting, inventory optimisation and cost control in 2025.
According to a Gartner, Inc. survey released 30 October 2024, AI (including machine-learning) and generative AI were the top digital supply-chain investment priorities for supply-chain leaders.
According to the United Nations Environment Programme (UNEP), the fashion industry is responsible for “up to 8% of global greenhouse-gas emissions”, making it one of the most resource-intensive consumer sectors. UNEP also highlights that the industry produces significant waste, with fast-fashion consumption accelerating disposal rates worldwide - an estimated 85% of all textiles produced end up in landfills or incinerated each year, according to data cited by the Geneva Environment Network.
The AI-powered Smart Garment Sorting System, developed by the H&M Foundation and HKRITA, uses artificial intelligence, image analysis and hyperspectral spectroscopy to identify textile materials with high precision. With around 100 million tonnes of textile waste generated each year and very little recycled back into new garments, the system aims to automate sorting, reduce contamination, and make textile-to-textile recycling more viable.
These data points illustrate that both market investment and executive focus in fashion are shifting decisively into AI-enabled sustainability, reflecting and feeding into regulatory and commercial pressures.
Here we present eight or more concrete case studies, grouped by application area.
Inditex SA: Its 2024 annual report describes how its logistics-expansion programme (2024-25) incorporates technology for stock-management (including RFID and demand-forecasting algorithms) to increase responsiveness and reduce over-production.
H&M Group (with Google Cloud): While the initial partnership was announced earlier, in 2023-24 H&M ramped machine-learning/AI in its supply-chain data backbone and inventory planning (H&M’s 2024 Annual & Sustainability Report references a “demand-driven supply chain” enabled by digital development).
PVH Corp.: In 2024 and beyond the company’s “PVH+” strategy includes inventory-management improvements driven by data analytics and AI-based planning.
These cases show how brands are leveraging AI to reduce forecasting errors, optimise inventory, reduce wasted stock, and thereby lower environmental impact (via less over-production and fewer markdowns).
LVMH Moët Hennessy - Louis Vuitton: The company states that in 2024 it “continued to deploy the Digital Product Passport (DPP) as part of its LIFE 360 environmental strategy.” Meanwhile, its membership in the Aura Blockchain Consortium (with others) has logged more than 40 million products by 2024.
Several LVMH maisons have already implemented digital passports or blockchain-based product IDs in recent years:
Louis Vuitton (Louis Vuitton Malletier) - one of the earliest adopters, using Aura Blockchain for product authentication and traceability across selected leather goods and luxury items.
Hublot - provides blockchain-secured digital certificates for its watches, replacing physical warranty cards.
Bulgari - has implemented blockchain-backed product IDs for jewellery and high-end accessories, ensuring provenance and authenticity.
Kering Group: Kering’s 2024 EP&L results report 1.305.429 tCO₂e from the product life cycle and commercialisation stage (around 60% of total emissions). What makes this relevant to AI-enabled traceability is that Kering Group uses advanced data-modelling systems, including machine-learning and automated impact-calculation tools, to quantify environmental impacts across raw materials, manufacturing, logistics, retail and end-of-life.
These AI-supported tools allow Kering Group to:
trace impacts across multiple supply-chain tiers,
model emissions at product/material level,
prepare for future DPP integration,
and comply with tightening EU regulations (CSRD + ESPR).
So while Kering Group has not yet launched full consumer-facing DPPs, its AI-driven impact-measurement system serves as the internal backbone that will feed into Digital Product Passport requirements.
Zalando GmbH: In late 2024, Zalando GmbH publicly announced pilots around digital tagging and traceability (for example via its “Walking the Talk” initiative) and collaborated with Fashion for Good for circular-innovation pilots.
These examples highlight how AI facilitates traceability of each SKU, supports Digital Product Passport rollout, enables impact-measurement across value-chains, and helps brands comply with forthcoming regulation.
AI is rapidly advancing circularity across the fashion sector through systems that automate textile identification, optimise recycling flows and streamline resale operations. Modern sorting technologies now use machine learning, computer vision and hyperspectral spectroscopy to accurately detect fibre composition and garment structure, enabling higher-quality textile-to-textile recycling. In parallel, fibre-to-fibre recycling facilities increasingly rely on ML-driven process optimisation to separate blended materials and maximise the recovery of usable fibres. Circular and resale platforms also deploy AI-classification models that automatically categorise second-hand items, assess condition and standardise product data, reducing return rates and improving operational efficiency. Together, these technologies form the foundation of a digitally enabled circular-fashion ecosystem.
L'Oreal SA (2023-2025): L'Oreal SA uses AI-enhanced Life Cycle Assessment (LCA) models to evaluate product environmental impacts across categories and support eco-design decisions. The company’s Environmental Labelling Programme (2023-2024) integrates these AI-driven assessments to improve packaging, formula sustainability and consumer transparency.
Shiseido Company, Limited (2023-2024): Shiseido applies AI-based digital modelling in its R&D pipeline to optimise skincare and cosmetic formulations. These tools reduce raw material waste, shorten development cycles, and improve precision in active-ingredient performance prediction.
The Estee Lauder Companies Inc.(2024):
The Estee Lauder Companies Inc. is modernising its global supply chain using advanced analytics, automation and machine-learning-enabled systems to enhance inventory planning accuracy. These upgrades help reduce overproduction and cut waste associated with excess stock and inefficient distribution.
Burberry (2024):
Burberry expanded its digital traceability platform in 2024, implementing automated supplier and material-mapping tools to enhance visibility across its production network. While not explicitly labelled as “AI”, these systems support DPP-readiness and more transparent supply-chain reporting.
Prada SPA / LVMH (2023-2025):
Prada SPA and several LVMH maisons have invested in digitalised, automated manufacturing systems to improve production efficiency and reduce material waste. While public documentation does not explicitly state AI usage, these upgrades support future integration of AI-guided planning and waste-reduction technologies.
These examples demonstrate how AI is reinforcing circular-fashion strategies at multiple levels - from smarter sorting and recycling to greener formulation, eco-design, traceability and low-waste manufacturing.
Bolt Threads (2023-2024): After pausing production of its Mylo™ mycelium leather in 2023 due to financing constraints, Bolt Threads shifted its focus toward scaling b-silk™ protein technology for beauty applications in 2024. The company applies data-driven biotechnology and advanced analytics across its R&D processes to optimise material performance and commercialisation.
Renewcell: While the company faced structural challenges, its 2023 production development (818 metric tons of prime-quality pulp in December 2023) underscored the use of digital process controls and AI-assisted pulp-quality enhancements.
Nike: In its FY24 Sustainability Data the company emphasises that “materials with less impact” and advanced manufacturing are pillars, and external commentary highlights Nike’s use of AI and generative-AI in product design and materials innovation.
These indicate that AI is not only used downstream (supply-chain, recycling) but also upstream in materials innovation: optimising composition, processing, quality and circularity.
ASOS plc Fit Assistant update in 2023-24: The company publicly noted enhancements to its AI-driven size-and-fit recommendation engine (press release, 2023) to reduce return rates.
Zalando GmbH’s Sizing AI (2023-24): The platform’s AI-based sizing tools - launched across European markets - aimed to improve first-time-fit accuracy and reduce returns impacting logistics and waste. (Zalando GmbH’s innovation narrative references this). Fashionbi
Shopify Fit Predictor (released 2024): Enables merchants to integrate AI-based “will-it-fit-me” tools, thereby reducing refunds/returns and the associated environmental burden.
By reducing returns, brands minimise wasted packaging, transport emissions, reverse-logistics and residual waste - an important leverage area for “AI sustainability fashion”.
The regulatory environment has tightened sharply, and AI now plays a central role in fulfilling new mandates.
The CSRD (Corporate Sustainability Reporting Directive) in the EU has been coming into force for large companies from 2024 onwards, requiring enhanced non-financial disclosures, double materiality and supply-chain evidence. Brands must demonstrate traceability and impact across their value chain.
The DPP rollout by 2025 (under the Ecodesign for Sustainable Products Regulation) will require many products to carry a Digital Product Passport: SKU-level data, materials, sustainability credentials, circularity status. AI becomes essential to tag, maintain and audit this level of data.
In the U.S., California Bill SB 253 & SB 261 (2024-25) introduce mandatory emissions disclosures and due-diligence obligations for large enterprises. AI-enabled systems help automate supplier data-ingestion and Scope-3 emissions measurement.
In France, the AGEC Law (2024 update) tightens labelling, traceability and end-of-life obligations for textiles and mandates better product traceability - again driving AI deployment.
Why AI is essential:
SKU-level impact tracking: thousands of items per season; manual systems cannot scale.
Real-time data processing: supply-chain disruptions, materials changes, emissions variations require dynamic monitoring.
Automated Scope-3 emissions measurement: supplier-tier data, product use-phase, logistics flows need algorithmic synthesis.
Supply-chain verification: AI and blockchain/digital passport tools allow verification of claims, detect greenwashing, and maintain audit trails.
In short, regulation is no longer just a disclosure exercise; it is a driver for digital/AI transformation of sustainable supply chains in fashion.
Despite the opportunities, brands face significant challenges and risks when deploying AI for sustainability.
AI-enabled greenwashing risk: In 2024-25, critics flagged that AI tools might enable superficial “sustainability claims” without substantive impact, especially when dataset quality or transparency is weak.
Energy consumption of large AI models: According to recent research (e.g., from the International Energy Agency and Nature journal in 2024), training large-scale AI models consumes significant energy, which can offset sustainability gains unless renewable power is used.
Data gaps & quality issues: Many brands lack full supplier-tier data, which undermines predictive AI and impact-reporting systems.
Dependence on incomplete or biased datasets: If AI is trained only on readily available data (e.g., Tier 1 suppliers), it may mis-estimate risk at Tier 3-4, leading to blind spots in sustainability strategies.
Implementation complexity & cost: Many smaller brands struggle to justify heavy investment, and AI pilots often remain siloed rather than scaled.
Transparency & explainability issues: As AI becomes embedded into decision-making, brands must maintain human oversight, governance and auditability - otherwise regulatory or reputational risk increases.
These complexities mean that while AI offers strong potential for “AI sustainability fashion”, real-world implementation requires rigorous governance, high-quality data and alignment with brand strategy.
Looking ahead to 2025-2030, a number of forecast-driven predictions emerge, supported by recent data and trend analysis:
According to Gartner (2024), by 2028 an estimated 25% of KPI-reporting in supply chains will be powered by GenAI models.
The digital supply-chain transformation report by Boston Consulting Group (2024) predicts micro-factories and AI-driven manufacturing will become commercially viable in fashion by 2027-2030.
Predictions:
Overall, the trend is clear: AI will shift from pilot to mission-critical for fashion sustainability. By 2030 brands that have embedded AI into sustainability operations will be at an irreversible advantage in terms of regulatory compliance, operational efficiency and circular-economy readiness.
Between 2023 and 2025, AI has shifted from a supporting technology to a core infrastructure for sustainability in the fashion industry. As regulatory frameworks such as the CSRD, the Ecodesign for Sustainable Products Regulation (ESPR), and emerging Digital Product Passport (DPP) requirements reshape expectations, fashion companies can no longer rely on manual reporting or fragmented systems. AI now underpins everything from traceability and impact measurement to inventory optimisation, circularity, and material innovation, creating a measurable shift toward verifiable ESG performance.
Real-world deployments - including Inditex’s AI-enhanced logistics, H&M and HKRITA’s AI-driven recycling automation, LVMH’s DPP rollout, Kering’s impact-measurement tools, and Zalando’s AI resale and sizing systems - confirm that AI is already transforming the sector’s operational and environmental footprint. These cases demonstrate that sustainability progress is becoming inseparable from digital capability.
Looking ahead, the trajectory is even clearer. By 2030, fashion will operate within an environment where AI-enabled DPP tagging, real-time environmental dashboards, on-demand micro-factories, and AI-powered circular business models are no longer experimental but embedded across value chains. Brands that adopt these systems early will gain regulatory readiness, operational efficiency and consumer trust - while laggards face increasing compliance, reputational and financial risk.
In short, 2023-2025 marks an irreversible turning point: AI is no longer simply a tool but the foundation of scalable, credible, and future-proof sustainability in fashion. The industry’s path forward - more transparent, circular and accountable - will be built on the intelligent systems being deployed today.
Cover Image: Zalando Website