Fragmented data and disconnected workflows are the Achilles heel of any apparel business. Not only does it mean companies lack insight into what’s happening, they’re also unable to take action. By embedding artificial intelligence into the workflows that already drive sourcing, product development, supplier management, compliance, quality and order management, the industry can reduce risk and build agility.
Supply chain solutions company TradeBeyond takes an integrated approach, immersing AI into existing supply-chain workflows rather than treating it as a standalone layer. Here, Eric Linxwiler, vice president of TradeBeyond, discusses how connecting the supply network gives AI the shared, contextualized view it needs to deliver actionable intelligence.
HGI: What does “AI-powered supply chain” mean in practice for a fashion or retail executive?
Eric Linxwiler: It means moving from simply seeing what happened to understanding what is likely to happen and what action should be taken next. It could mean identifying a supplier compliance issue before it delays production, flagging a purchase order at risk of missing its delivery window, identifying changes in trade regulations that could affect landed cost, or surfacing inconsistencies in product and supplier data before they create downstream problems.
The value is not AI for its own sake. It is giving teams better information earlier, automating routine decisions, and allowing people to focus on the exceptions and strategic decisions that have the greatest impact on cost, speed, risk and customer experience.
HGI: Why is creating a “single source of truth” across sourcing, supplier management, product development, compliance, quality and logistics so difficult for brands and retailers?
EL: Retail supply chains have evolved around specialized systems, teams and processes that were often implemented independently. Sourcing may have one system, product development another, logistics another, while supplier compliance and quality information may sit in spreadsheets, email or separate third-party platforms. Each system can work reasonably well on its own, but the gaps between them create significant friction.
The challenge is even greater because retail supply chains are inherently multi-enterprise. Data moves between brands, retailers, sourcing organizations, factories, suppliers, logistics providers and other partners, often across different countries, technologies, and operating models.
HGI: What is the fallout from such fragmentation?
EL: It creates a fundamental visibility problem. A purchase order is not simply an order-management record. It connects to the product being ordered, the supplier producing it, the factory manufacturing it, the compliance requirements associated with it, the shipment moving it, and ultimately the invoice that needs to be reconciled. When those pieces are disconnected, teams spend enormous amounts of time manually matching information and resolving discrepancies.
TradeBeyond addresses this by creating a connected, multi-enterprise environment where information can move across these processes rather than remaining trapped in functional silos. Our work with organizations such as Otto International, Home Depot and Walgreen’s demonstrates the value of replacing fragmented systems with a common digital ecosystem that connects retailers, brands and suppliers.
A shared digital ecosystem improves data collection and accuracy, strengthens collaboration across stakeholders, and achieves greater visibility into shipping inspections, social compliance and ESG requirements.
HGI: As companies deal with shifting trade regulations, sustainability requirements, geopolitical uncertainty and cost pressures, how is AI changing how—and when—they anticipate, prioritize and respond to risk?
EL: The biggest change is the shift from static compliance and risk management toward continuous monitoring and proactive intervention. AI can analyze large volumes of structured and unstructured information, identify patterns and exceptions, and connect those signals to the transactions and suppliers they affect.
For example, if a regulatory change could affect a particular product, supplier or sourcing market, the organization should be able to identify the exposure early enough to evaluate alternatives. The same principle applies to supplier performance, production delays, quality issues, and sustainability requirements.
AI can help prioritize those risks so teams can focus attention where the potential financial, operational or compliance impact is greatest. The objective is to make supply chain management more predictive, giving executives the ability to ask not only, “What is happening?” but “What could happen next, which areas are most exposed, and what should we do about it?”
HGI: What metrics should companies use to determine whether AI is genuinely transforming their supply chain vs simply adding another tech layer?
EL: Companies should start with business outcomes rather than technology adoption metrics. The question should not be how many people are using an AI feature, but whether the organization is making faster, more accurate and more profitable decisions as a result.
Important metrics include reductions in manual processing time, order and data accuracy, supplier onboarding time, product development cycle time, exception resolution time, invoice reconciliation time, compliance incidents, late shipments and avoidable costs. Companies should also measure how quickly teams can identify and respond to risk. If an organization previously needed days to determine which purchase orders or suppliers would be affected by an issue and can now identify that exposure in hours or minutes, that represents tangible operational value.
HGI: What about the ROI around scalability?
EL: If a retailer can onboard hundreds of suppliers, expand into new markets or increase product volume without proportionally increasing administrative headcount, that demonstrates the value of automation and connected data.
Ultimately, successful AI should improve the economics and resilience of the supply chain. If AI simply creates another dashboard that employees have to monitor, it is another technology layer. If it reduces work, improves decisions, catches problems earlier and produces measurable improvements in cost, speed, accuracy, and risk management, it is transforming the operation.
HGI: Looking a few years ahead, how will AI change the relationship between brands, retailers, suppliers and sourcing organizations?
EL: AI will make supply chain relationships more collaborative and data-driven. Instead of each organization operating from its own version of events, brands, retailers, sourcing organizations and suppliers will increasingly work from shared information and workflows. Teams will spend less time exchanging basic information and more time solving problems, developing suppliers and making strategic decisions.
HGI: What capabilities will separate the companies that build truly resilient supply chains from those that remain stuck managing complexity manually?
EL: The companies that succeed will be those that combine connected data, automation, predictive intelligence and strong multi-enterprise collaboration. They will know their suppliers, understand their product and order data, have visibility into compliance and sustainability performance, and be able to identify risks before they become disruptions.
They will also be able to act on intelligence. Predicting that an order is at risk is useful, but the real advantage comes from connecting that insight to the workflows needed to resolve it. That could involve engaging a supplier, adjusting a production milestone, rerouting a shipment or evaluating an alternative source.
Resilience will depend on how quickly an organization can move from signal to decision to action. Companies with connected platforms and embedded intelligence will be able to spend more time acting on it.
HGI: From what you’re seeing across the market, where are the biggest remaining data and visibility gaps in global sourcing—and what information do executives still struggle to get quickly enough?
EL: One of the biggest remaining gaps is not necessarily a lack of data, but the inability to connect data across organizations and processes quickly enough to make it useful. Executives often have access to supplier information, product specifications, compliance documentation, quality reports, shipment information and purchase order data, but that information may exist in different systems and formats.
Supplier-level visibility is particularly important. Companies need to understand not just who their direct suppliers are, but how suppliers are performing, whether required certifications and audits are current, where compliance risks exist, and how those factors could affect specific products or orders.
There are similar gaps around order and shipment visibility. Executives need to know which orders are on track, which are at risk, why they are at risk, and what the financial or customer impact could be—all in the context of product, supplier and logistics data rather than as an isolated status report.
HGI: What’s the next step for the industry?
EL: Moving from visibility to actionable intelligence. Executives should not have to assemble information manually to understand where their greatest exposures are. The supply chain platform should bring those relationships and risks to the surface automatically, allowing leadership to focus on decisions rather than data gathering.
