A container ship arrives at Long Beach two days late. Inside are goods tied to dozens of purchase orders. Some are carrying basics that can wait, while others are holding seasonal items for promotions starting in three weeks, and a few are high-velocity SKUs that are already running thin at stores.
Which containers do you expedite? Which routes can absorb the delay? Which deliveries need priority to avoid out-of-stocks during your promotional window?
Your transportation team sees the delay. Your import team scrambles to adjust. But by the time procurement understands the PO impact, the merchandising team learns about promotional risk, and store operations realize they’ll need surge labor, the window to avoid problems has closed. You’re reacting, not orchestrating.
This is where most supply chain automation initiatives hit a wall — not from lack of technology, but from lack of shared understanding across systems, while there’s still time to act.
The Problem Isn’t Visibility Anymore
Most large organizations can track shipments in real time now. The transportation team knows where trucks are. The DC knows what’s on the dock. Procurement can see PO status. Most have ‘visibility’, but it’s not enough.
You might know where things are, but you need to understand the purpose the impacted goods serve. Which promotional windows matter, and which decisions protect margin versus which ones just optimize isolated steps?
Your systems can see events. What they lack is shared context about what those events mean for business outcomes.
The import team works off container IDs. Procurement tracks purchase orders. The DC operates on ASNs. Merchandising operates in terms of SKUs and promotional calendars. Store operations manages labor against delivery schedules.
When that ship arrives late, each system sees its own piece. No system understands the full picture. So automation can’t either.
Why Automation Stays Incremental
This is why most AI and automation initiatives deliver time savings measured in minutes rather than financial impact measured in dollars.
You can optimize dock scheduling. You can refine replenishment algorithms. You can automate carrier selection. These are real improvements, but they operate within system boundaries. They make individual processes faster without preventing the coordination failures that actually erode margin.
The two-day delay on that container ship? It’s trivial for baseline replenishment and critical for seasonal goods tied to dated promotions. However, your automation cannot distinguish between shipments unless it can connect shipment data to promotional calendars, DC capacity, store-level demand patterns, and planograms — all in real-time.
The problem exists at what data architects call the semantic layer: the absence of a shared, machine-readable representation of business objects that links their states and operating contexts across your network. As Bain says, “The semantic layer is becoming the bottleneck for agentic AI — without a shared representation of business objects, even the most advanced models cannot autonomously act.”
In the case of supply chains, without a real-time insight across your network and a digital twin of your supply chain, the AI you implement will only be able to suggest and flag. It will not be able to orchestrate across modes and nodes because it won’t have a common understanding of what needs orchestrating.
The Cost of Disconnected Systems
For a large retailer moving billions in inventory annually, these coordination failures become expensive quickly.
Compressed delivery windows force overtime at DCs. Lumpy arrivals require unplanned staffing. Products arriving out of phase with promotional timing reduce full-price sell-through. Early floor sets to accommodate delayed shipments dilute promotional windows. Fees pile up when detention and demurrage become unavoidable because no single system could see the full constraint set.
The automation that moves dollars, not just minutes, prevents these outcomes. It protects promotional margin. It avoids fees before they’re incurred. It optimizes working capital by understanding trade-offs across the network thanks to an end-to-end, machine-readable layer that represents goods, movements, and states across TMS, WMS, ERP, carrier systems, and supplier networks — with enough context to understand the purpose, not just the position.
How to Make Sure AI Saves Millions, Not Minutes
If your automation initiatives are delivering time savings but not financial outcomes, the bottleneck probably isn’t the AI. It’s the infrastructure underneath it.
The question isn’t whether to invest in automation. It’s whether your systems can coordinate around a shared understanding of what events mean for business outcomes—or whether you’re adding capabilities to platforms that can’t support true orchestration.
You don’t suffer from a lack of data. You suffer from a lack of shared meaning, in time to act. Until you address that gap, your automation will stay confined to optimizing individual steps while the coordination failures that erode margin continue unchecked.
The semantic layer determines which path you’re on. With it, automation can protect the numbers that matter. Without it, even the most advanced AI will keep escalating decisions to humans because it can’t connect the dots across your network.
Matt Elenjickal is the Founder and Chief Executive Officer of FourKites. He founded FourKites in 2014 after recognizing pain points in the logistics industry and designing elegant and effective systems to address them. Prior to founding FourKites, Matt spent 7 years in the enterprise software space working for market leaders such as Oracle Corp and i2 Technologies/JDA Software Group. Matt has led high-impact teams that implemented logistics strategies and systems at P&G, Nestle, Kraft, Anheuser-Busch Inbev, Tyco, Argos and Nokia across North America, Western Europe and Latin America. Matt is passionate about logistics and supply chain management and has a keen sense for how technology can disrupt traditional silo-based planning and execution. Matt holds a BS in Mechanical Engineering from College of Engineering, Guindy, an MS in Industrial Engineering and Management Science from Northwestern University, and an MBA from Northwestern’s Kellogg School of Management. He lives in Chicago.
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