The hidden costs of supply chain blind spots-and how AI can solve them

Most people never think twice about how a package gets to their doorstep, how a medication arrives on time, or how produce stays fresh from farm to fridge. But when things go wrong—like store shelves sitting empty or critical goods arriving damaged—the mystery of what happened often comes down to one key issue: a blind spot in the supply chain.

A "blind spot" is any place in the supply chain where businesses lose track of goods, conditions, or processes. These are the invisible moments between scan points, in transit, or during handoffs—where data disappears and assumptions replace certainty. And as global supply chains grow increasingly complex, the hidden costs of these gaps are mounting, Trackonomy says.

But now, a new generation of artificial intelligence tools is making it possible to see—and solve—what was once unknowable.

What Supply Chain Blind Spots Really Cost

The fallout from blind spots isn't always visible to consumers, but their impact is significant.

• Wasted products: Perishables may spoil due to unmonitored delays, improper temperature control, or rough handling. In the U.S., about 30%-40% of the food supply is wasted, with significant impacts throughout the supply chain.
• Rising costs: To compensate for uncertainty, businesses often rely on expensive emergency freight, overstocking, or manual audits—all of which eat into profit margins. Direct and indirect costs of blind handoffs could total $64 billion-$94 billion per year.
• Environmental waste: Extra emissions, unnecessary packaging, and discarded goods contribute to a larger carbon footprint. 
• Human toll: Workers face delays, rework, and frustration trying to trace missing items or explain shipment errors. Customer service teams are often left apologizing for problems no one saw coming.

These aren't just operational hiccups—they're expensive, reputationally damaging, and increasingly avoidable.

Why Blind Spots Persist Despite Digital Supply Chains

While many supply chains have embraced digital tools, the reality is that much of the infrastructure still relies on static or outdated systems. Traditional enterprise resource planning (ERP) software was built for linear, stable processes—not the real-world messiness of subcontractors, port delays, multimodal transport, and global uncertainties.

Visibility often ends the moment goods leave a facility or move between parties. Even sophisticated analytics systems can struggle when the data is delayed, missing, or unstructured. This is where blind spots grow—and where decision-making becomes reactive instead of strategic.

The AI Revolution: Filling in the Gaps With Intelligence

Artificial intelligence is changing the equation. Rather than relying on perfect inputs, AI can analyze imperfect, incomplete, and real-time data to spot patterns, flag anomalies, and surface actionable insights.

This marks a shift toward "liquid computing"—where intelligence flows across cloud systems, edge devices, and physical infrastructure. AI-powered tools now operate directly on the shop floor, in vehicles, or embedded into packaging—making decisions at the edge and alerting the right people, at the right time, based on priority and proximity.

Here's how it works in practice:

• Real-time monitoring: Sensors track temperature, humidity, movement, and shock during transport.
• Tampering detection: Unexpected detours, unauthorized access, or broken seals trigger automatic alerts.
• Autonomous workflow intervention: If a high-priority shipment is misrouted, the system can reroute it mid-transit, modifying the destination dynamically.
• Package intercepts: AI enables real-time delivery intercepts or redirection for time-sensitive cargo.
• Facility-level pickup verification: Tracks how many packages were expected vs. actually picked up—eliminating guesswork and reducing reliance on manual manifests.
• Misload alerts and guided locationing: Visual or audible cues help frontline workers catch loading errors instantly and recover misplaced items.

This is more than automation—it's a proactive model that sees around corners, preventing problems before they escalate.

Real-World Examples: When Seeing Clearly Makes All the Difference

AI-powered visibility is already making a difference across industries:

• Preventing cargo theft through real-time intelligence: A logistics provider uncovered a case of carrier fraud after an AI-enabled tracking system detected inconsistencies in trailer movements. The shipment had been double-booked by a fraudulent carrier. Because the system provided real-time alerts and trailer-level visibility, the team was able to intervene immediately, prevent theft, and reassign the load. By identifying the issue before it escalated, the company protected both the cargo and its customer relationship.
• Ensuring successful vaccine delivery under pressure: During the peak of the COVID-19 pandemic, rapid vaccine distribution became a national priority. In collaboration with logistics partners, AI-powered smart labels were deployed to monitor millions of vaccine shipments under Operation Warp Speed. These labels tracked temperature, movement, and handling conditions in real time—ensuring doses arrived safely at their destinations across the country. The solution's ability to provide visibility at the individual package level helped reduce spoilage, maintain compliance, and streamline delivery for one of the largest public health mobilizations in history. The system helped achieve 99.9% deliverability.
• At a recent developer event, teams using an AI-powered logistics platform created real-time geofencing solutions to prevent livestock handling errors, and 3D mapping tools to detect transport hazards for pharmaceuticals—enabling action before damage occurred.

These aren't just theoretical wins—they're practical examples of how better visibility translates into smarter operations.

From Fixing Problems to Planning the Unplannable

What's most exciting is that AI doesn't just help resolve issues—it helps companies prepare for volatility in entirely new ways.

Dynamic systems can reoptimize schedules, reroute inventory, and adjust workflows based on live conditions. This evolving model—what some are calling "dynamic ERP"—goes far beyond static spreadsheets or dashboards. It continuously learns, adapts, and closes feedback loops in real time.

That means businesses can shift from reacting to disruptions to orchestrating outcomes, even in the face of uncertainty.

Resilience Starts With Clarity

From climate-driven delays and labor shortages to geopolitical shifts and rising customer expectations, modern supply chains face growing complexity. But blind spots don't have to be part of the cost of doing business.

By embedding intelligence across the physical and digital layers of supply chains, AI is providing the clarity needed to build systems that are resilient, responsive, and ready for what comes next.

Because in supply chains—as in life—what you can't see can hurt you. But now, we have the tools to finally see it all.