How to Use AI to Transform Supply Chains from Data to Action

Every shipment moving through a logistics network represents much more than a journey from one point to another. Behind it are thousands of decisions. Some take place before the shipment leaves its origin. Others are made while it is in transit. Many depend on weather conditions, traffic, demand, available capacity, regulatory changes, inventory levels, critical schedules, and increasingly high customer expectations.

Modern logistics operates under three clear requirements: speed, visibility, and certainty.

A few years ago, much of the technology conversation in logistics focused on process automation and efficiency improvements. Today, the challenge is different. Supply chains operate in a more dynamic environment, under greater pressure, and far more vulnerable to disruptions.

Technology primarily helps optimize processes and anticipate outcomes. Artificial intelligence is taking that conversation a step further. Its value no longer lies solely in predicting what may happen. It lies in helping turn those predictions into more timely decisions and more consistent execution.

That distinction is critical. In logistics, a good prediction without operational action has limited value. What matters is whether it improves a route, reduces friction, anticipates a need, protects a delivery commitment, or strengthens the customer experience.

In a global logistics operation such as FedEx, that can translate into faster decisions to redistribute capacity, adjust routes, or respond more accurately to unexpected shifts in demand.

Data That Anticipates Scenarios

Supply chains operate in a more dynamic environment than they did just a few years ago.

Global trade is being reshaped. Demand for digital infrastructure and artificial intelligence capabilities is also changing the physical flows of logistics. According to the McKinsey Global Institute, shipments of semiconductors, servers, and networking equipment increased by nearly 40 percent and accounted for roughly one-third of global trade growth[1]. For the logistics industry, this means more complex freight movements, greater pressure on strategic infrastructure, and new connectivity requirements between markets.

The same analysis indicates that the United States added approximately half of the world's new data center capacity. That growth is not only transforming the digital economy. It is also reshaping supply chains, distribution patterns, and operational requirements for companies moving high-value, highly sensitive goods.

These data points highlight something important for our industry. Artificial intelligence is generating new freight flows, new industrial priorities, and new connectivity needs.

For a logistics company, anticipating change is no longer simply about projecting volumes. It is about interpreting market signals with greater accuracy and agility. It means understanding shifts in demand, consumption patterns, capacity pressures, and emerging manufacturing hubs. It also means creating stronger connections between operational insights and execution capabilities across air routes, ground operations, customs processes, and last-mile delivery.

At FedEx, this perspective drives us to continue adopting technology, advanced analytics, and digital capabilities that strengthen our understanding of the market and our ability to respond. We are talking about enriching operational judgment with information that is clearer, more timely, and more useful for decision-making.

Today, the difference lies in understanding earlier what is changing around supply chains and how to respond before disruption reaches the customer.

Logistics has always been an industry of movement. Today, it must also be an industry of signals.

Turning Prediction into Response

The true value emerges when a logistics network can translate information into action. When an alert becomes an adjustment. When a trend becomes a plan. When potential friction becomes a decision before it reaches the customer.

Artificial intelligence is changing that sequence.

In the past, many supply chains reacted after a disruption occurred. Today, organizations with greater technological maturity aim to act earlier, reallocate capacity, improve visibility, and respond with stronger operational discipline.

In practice, this may mean identifying potential bottlenecks at a logistics operations center, anticipating weather-related disruptions along critical routes, or adjusting capacity during e-commerce demand peaks.

This point is especially relevant in a more complex international trade environment. McKinsey Global Institute reports that trade between the United States and China declined by nearly 30 percent in 2025. That shift has accelerated the search for new markets, new trade routes, and new manufacturing locations.

The report also notes that Chinese exporters reduced consumer goods prices by approximately 8 percent to attract buyers in new markets[2].

As trade flows are redistributed, logistics networks must also adapt quickly to maintain visibility, capacity, and operational reliability.

Mexico has a clear opportunity in this environment. Our geographic position, integration with North America, and industrial base make logistics connectivity a critical competitiveness factor. For companies with cross-border operations such as FedEx, having tools that enable faster responses to changes in demand, capacity, and trade flows becomes even more important.

Artificial intelligence can support that competitiveness when it is integrated with real processes, well-prepared teams, and networks capable of execution.

At FedEx Mexico, we view this transformation from an operational perspective. Every technological improvement must answer a specific question. How does it help the customer? How does it improve visibility? How does it strengthen reliability and certainty? How does it enable better decisions in less time?

Innovation that does not translate into execution remains only a narrative.

Making the Entire Experience Smarter

Artificial intelligence is also expanding the way we understand customer experience.

While logistics discussions traditionally focused on delivery, today the experience encompasses the entire journey, including purchasing, documentation, collection, transit, delivery, returns, support, and resolution.

Reverse logistics is a strong example. McKinsey estimates that US consumers returned nearly US$1 trillion worth of merchandise in 2024. That volume has led retailers and wholesalers to spend an estimated US$200 billion annually to recover value from those returns[3]. This illustrates how the logistics experience no longer ends when a package reaches its destination. It also depends on the ability to respond efficiently when a product needs to return to the supply chain.

This is not a marginal issue. It is a structural component of modern commerce.

For customers, a return can be either a moment of friction or a moment that builds trust. For businesses, it can be either a difficult cost to manage or an opportunity to recover value, improve inventory management, and strengthen the commercial relationship.

Artificial intelligence can help bring greater order to that process. It can support better decisions regarding classification, prioritization, timing, routing, inventory management, and customer support. Yet again, the value lies not only in generating information but in converting that information into more agile processes and a more reliable customer experience.

Its impact depends on something deeper than technology itself. It depends on the ability to integrate it into clear, measurable processes aligned with real customer needs.

That is the difference between adopting a trend and transforming an operation.

From Prediction to Action

The logistics industry of the future will not be defined solely by who has the most data. It will be defined by who can transform data into useful decisions and those decisions into reliable execution.

That is the fundamental shift.

Artificial intelligence makes anticipation possible. But it also demands discipline. It requires skilled talent. It requires robust processes. It requires an operating culture capable of learning, adapting, and continuously improving.

At FedEx, we see technology as a tool in service of a promise: moving commerce, connecting markets, and responding to customers who depend on speed, visibility, and certainty.

We need to elevate the conversation around AI and focus it on how to use it with purpose, responsibility, and operational focus.

Because in logistics, competitive advantage does not come only from predicting better. It comes from responding better and executing more consistently when conditions change.

[1] Geopolitics and the geometry of global trade: 2026 update | McKinsey

[2] Geopolitics and the geometry of global trade: 2026 update | McKinsey

[3] From cost center to competitive advantage: modernizing reverse logistics with AI