Fashion brands turn to AI as traceability rules tighten
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For years, artificial intelligence in supply chain management was discussed mainly in terms of forecasting, inventory planning and logistics efficiency. For fashion companies, that conversation is changing.
New transparency and sourcing requirements are forcing brands to understand their supplier networks in far greater detail. The challenge is not simply knowing which factory manufactured a garment. Companies increasingly need information about mills, processors, raw materials and other suppliers several steps removed from the finished product.
A single garment can pass through farms, textile processors, mills, manufacturers, logistics providers and packaging suppliers before reaching a consumer. Information generated at each stage may sit in spreadsheets, emails, certificates, enterprise systems and supplier portals that were never designed to communicate with one another.
AI is emerging as a way to process that fragmented information faster. Business Insider reported that fashion businesses including ASOS, Gap and others are adopting AI-powered supply chain tools as regulatory pressure around transparency increases. One example cited in the report illustrates the potential productivity gain. A regulatory compliance workflow that could require at least 30 hours of manual work for each purchase order can reportedly be reduced to about one hour using AI alongside human review.
Regulation is turning supply chain visibility into an operating requirement
Supply chain transparency was once closely associated with voluntary sustainability programs, corporate reporting and reputational risk. Increasingly, it is becoming a compliance function.
The EU’s Ecodesign for Sustainable Products Regulation created the legal framework for Digital Product Passports, which are designed to make product information available throughout the value chain. Textiles are among the categories expected to face product-specific requirements.
The Digital Product Passport Registry went live in July 2026, moving the concept another step from policy toward operating infrastructure. For apparel companies, the practical implications could be substantial. Meeting future requirements may depend on maintaining structured information covering areas such as product composition, origin, environmental characteristics and other product-level data.
California is applying pressure through a different regulatory model. Its Responsible Textile Recovery Act establishes an extended producer responsibility system for apparel and textile products. Under the program, producers take greater responsibility for how products are handled when consumers discard them, including collection, reuse, repair and recycling.
CalRecycle estimates that Californians discard about 1.2 million tons of textiles each year. Managing that volume becomes considerably more complicated when companies have limited information about material composition and product origin.
Forced labor rules create another traceability requirement. Under the Uyghur Forced Labor Prevention Act, certain products connected to Xinjiang or entities identified by US authorities face a presumption that they cannot enter the US.
For importers, visibility into a direct supplier may not be enough. The relevant cotton, yarn, material or processing activity can exist several layers upstream.
Different regulations address different problems, but the operational consequence is similar: businesses need better evidence about what happens before a finished product reaches them.
AI is turning fragmented supplier records into usable intelligence
Traditional supply chain systems tend to work well when information is structured and standardized. Modern supplier networks rarely satisfy that condition.
A company may receive purchase orders, invoices, audit reports, transaction certificates, shipping documents and supplier declarations in different formats from businesses operating across multiple countries.
AI tools can help classify those records, connect related information and identify missing or conflicting data. The result is a shift from periodic supplier mapping toward something closer to continuous supply chain intelligence.
ASOS offers one example. In 2025, the online fashion retailer selected traceability technology company TrusTrace to support supply chain transparency, risk management and compliance. The system is designed to help reconstruct the path of products from raw materials through manufacturing to finished goods using documented chains of custody.
Knowing that a factory produced a garment provides one piece of information. Establishing where its fabric was made, where its fibers originated and whether those claims can be linked through documentation requires a much deeper level of data. AI can reduce the amount of manual work involved in assembling that record.
Systems can scan large document sets, extract relevant information and flag gaps for human investigation. They can compare supplier claims against transaction records or certificates and help compliance teams focus on exceptions instead of reviewing every document manually.
This is a more practical use of AI than the idea of an autonomous system making regulatory decisions. Human oversight remains critical. A model can identify that two documents contain different supplier names or that a required certificate appears to be missing. It cannot make unreliable source data reliable simply by processing it faster.
The distinction becomes more important as companies move beyond their Tier 1 suppliers. Fashion supply chains can contain thousands of businesses spread across farming, fiber processing, spinning, weaving, dyeing, finishing, manufacturing and distribution. Many brands have strong visibility over direct manufacturers but progressively less information further upstream.
The next supply chain advantage may be the ability to prove origin
AI does not solve the central weakness in supply chain transparency: data quality. A traceability platform remains dependent on information submitted by suppliers, manufacturers and other participants. If the underlying records are incomplete or inaccurate, automation can accelerate processing without improving the truthfulness of the result.
Companies still need governance around supplier onboarding, documentation standards, verification and chain-of-custody processes. Interoperability presents another challenge. Brands, suppliers, technology providers and regulators may store similar information in different structures. Digital Product Passports could increase pressure for common standards by creating expectations around what product information must be available and how it can be accessed.
That could make traceability less dependent on proprietary databases and more closely connected to shared product information. Supply chain software has historically been purchased to answer questions such as how much inventory a company needs, when a shipment will arrive or where production capacity should be allocated.
Manufacturers facing rules around product composition, carbon emissions, sourcing, recycling or forced labor encounter variations of the same information problem. The more complex their supplier networks become, the more expensive manual verification becomes.
AI can change those economics by reducing the amount of human effort required to organize and interrogate large volumes of supply chain documentation. The technology will not remove the need for reliable suppliers, credible records or compliance expertise. It can make those resources easier to use at scale.
Fashion is becoming an early test case because its supply chains are fragmented, international and increasingly scrutinized. What happens there offers a preview of a broader change in supply chain management.
The next generation of supply chain technology may not be judged only by whether it moves products faster or predicts demand more accurately. Its value may increasingly depend on whether a business can demonstrate, with credible evidence, exactly where its products came from.
Source:
Business Insider