Mark Burstein, SVP at Inspectorio, transforming how brands manage supply chain risk, quality and compliance through an AI-powered platform.
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Most consumers don’t think twice about the clothes they wear.
A shirt is chosen for its fit. A pair of jeans for its comfort. A jacket for its style. The decision is simple, almost instinctive. What remains invisible is the vast, complex supplier network required to bring that product to life.
Behind every garment sits one of the most intricate global supply chains in any industry, one that generates millions of data points every year.
This is where both the problem and the opportunity begin.
Fashion has always been global, but today’s supply chains operate at a scale and speed few industries can match.
A single brand may sell into more than 100 countries, across retail stores, e-commerce platforms, marketplaces and wholesale partners. To support that reach, it relies on an extensive network of suppliers, often hundreds or thousands, spanning multiple tiers.
At the center are Tier 1 factories, responsible for assembling finished goods. These factories depend heavily on Tier 2 suppliers for fabrics, trims and raw materials. The result is a deeply interconnected ecosystem, largely concentrated in regions like Southeast Asia, where coordination is critical and increasingly difficult.
Now consider the operational reality. A large brand may issue over 125,000 purchase orders each year, conduct more than 100,000 quality inspections across production stages and run upward of 50,000 lab tests to meet performance and regulatory standards. It typically may coordinate more than 20,000 shipments across approximately 1,500 global shipping lanes.
Each of these activities generates data, and a significant volume of it. Yet for most organizations, that data does not come together in any meaningful way.
If there is one misconception about supply chains, it’s that companies lack data. In reality, they are overwhelmed by it.
Data flows in from internal systems such as ERP and PLM platforms and from third parties through integrations. It is extracted from documents using OCR and manual processes and is also entered directly by suppliers, inspectors and partners across the network.
However, this data rarely aligns. Different formats, different standards and different systems all contribute to fragmentation. The same supplier, or even the same purchase order, can exist in multiple versions across disconnected tools.
The issue is not availability. It is fragmentation.
Different teams, including sourcing, quality, sustainability and compliance, all work from partial views of the same reality. Information lives in silos. It is inconsistent, difficult to reconcile and often outdated by the time it is used. The result is slower decisions, higher risk and missed signals.
Many leading organizations are recognizing that the solution is not more dashboards or incremental system upgrades. Instead, it requires a fundamentally different approach to how supply chain data is captured, structured and connected.
Instead of managing isolated data streams, the goal is to build a connected data layer. This is a standardized, continuously updated foundation that brings together information from every source, including internal systems, third-party providers, supplier inputs and all related documents.
More than a data warehouse, this is a living system that normalizes and structures data at scale.
When this foundation is in place, teams no longer need to chase information and can instead operate from a shared source of truth. Sustainability leaders can track regulatory requirements such as CSRD (the EU’s Corporate Sustainability Reporting Directive) with confidence. Compliance teams can validate adherence to laws such as UFLPA (Uyghur Forced Labor Prevention Act) without manual reconciliation. Sourcing and quality teams can monitor supplier performance in near real time.
The focus shifts from reacting to problems to anticipating them.
The manual effort required to collect, standardize and validate information across thousands of suppliers simply does not scale. However, with the rise of agentic AI, companies are deploying autonomous agents that continuously gather, structure and validate data across fragmented systems and stakeholders. What once required hours of manual effort per purchase order, per supplier or per audit can now be completed in minutes.
These systems do more than aggregate data: They activate it. They surface risks before they intensify. They flag inconsistencies across sources. They recommend corrective actions. They continuously assess supplier performance, quality trends and compliance exposure.
In doing so, they transform disconnected data into a dynamic intelligence layer that supports real-time decision-making.
How can brands begin their AI journey? For starters, data from internal and external sources must be collected, standardized and brought together into a single location, where it can be accessed by all stakeholders. With a structured data warehouse (the dynamic intelligence layer) brands can begin deploying AI agents, GenAI models and AI co-pilots with proper AI governance in place.
Companies can build trust in agentic AI and realize quick wins by utilizing purpose-built AI technology that is specific to their industry, with deep domain context and appropriate workflows. As teams work together and see results, they gain a clear vision for AI’s impact across the enterprise.
The brands that move early will not only improve efficiency. They will build more resilient, intelligent and adaptive supply chains, where data is connected, accessible and actionable. This provides a critical competitive advantage in fashion’s next chapter.
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