In 2008, Europe appeared to be in an enviable economic position. The European Union’s economy had reached roughly $19.3 trillion, making it approximately 30 percent larger than the $14.8 trillion U.S. economy when measured at prevailing exchange rates. Germany remained an industrial powerhouse, Britain was one of the world’s leading financial centers, and European companies held commanding positions in automotive manufacturing, industrial automation, chemicals, aerospace, transportation, and logistics.
Less than two decades later, that relationship has been reversed.
The International Monetary Fund’s April 2026 World Economic Outlook projects U.S. nominal GDP of approximately $32.38 trillion this year, compared with $23.03 trillion for the European Union. That puts the EU economy at roughly 71 percent of the size of the U.S. economy in nominal dollar terms.
The comparison requires some caution. Exchange rates affect nominal GDP measured in dollars, and the EU of 2026 is not identical to the EU of 2008 because the United Kingdom left the bloc. The chart therefore should not be interpreted as a simple measurement of underlying productivity. But the direction of travel is difficult to ignore. Over the past two decades, the United States and Europe have followed markedly different economic trajectories.
The explanations are familiar. Europe endured the sovereign debt crisis, Brexit, weak demographics, greater exposure to the post-2022 energy shock, and increasing industrial competition from China. Meanwhile, the United States has benefited from stronger technology investment, a large integrated domestic market, abundant capital, and more recently an extraordinary wave of spending associated with artificial intelligence. The IMF itself has highlighted strong U.S. productivity performance as one contributor to the economy’s recent resilience.
All of those factors matter. But for supply chain leaders, the divergence raises another, more provocative question: has decision velocity itself become a source of competitive advantage?
I believe it has.
When Physical Assets Stop Being the Only Constraint
For most of modern industrial history, competitiveness could be understood through a relatively familiar set of inputs. Labor mattered enormously. Capital determined which companies and countries could build factories and infrastructure. Energy availability and cost influenced where energy-intensive industries could operate competitively.
Those factors have not disappeared. A chemical plant cannot overcome uncompetitive energy economics with a better dashboard. A manufacturer cannot ignore labor availability, financing costs, or transportation infrastructure simply because it has implemented artificial intelligence.
But the industrial system has acquired another critical input: information.
Modern supply chains generate extraordinary volumes of operational data. Transportation management systems report shipment activity. Warehouse management systems track inventory movement. ERP platforms contain orders, production schedules, and financial transactions. Supplier portals, visibility networks, IoT devices, planning platforms, weather feeds, market data, and external risk services generate additional streams of information.
Twenty years ago, much of the problem was obtaining that information. Today, many large companies face almost the opposite problem. They can see enormous amounts of what is happening across their networks, yet translating all of those signals into coordinated action remains difficult.
The bottleneck is moving.
It is increasingly found in the distance between knowing something has happened and doing something useful about it.
The Monday Morning Problem
Consider an ordinary supply chain scenario.
A planner arrives Monday morning and learns that a critical inbound shipment will arrive two days late. A modern visibility platform may have identified the delay hours earlier. The event itself is no longer hidden.
What happens next is more revealing.
Someone must determine which production orders depend on the material. Inventory planners need to understand whether another facility has stock available. Procurement may need to contact the supplier. Transportation teams may evaluate expedited modes or alternate routes. Customer service needs to know which commitments could be affected. Finance may have to approve additional freight expense.
The company may possess every piece of information necessary to solve the problem, yet the response still travels through spreadsheets, emails, messaging applications, meetings, phone calls, and approval workflows.
A digital system detected the problem in seconds. The organization may require hours—or sometimes days—to decide what to do.
That gap deserves more attention.
I increasingly think of it as decision-to-action latency: the elapsed time between detecting an operational event and executing an appropriate response.
Supply chains already measure transportation lead time, dock-to-stock time, order cycle time, manufacturing cycle time, and countless other forms of latency. Yet the time consumed by organizational decision making is rarely measured with the same discipline.
It should be.
In a volatile supply chain, a theoretically optimal decision that arrives tomorrow may be less valuable than a sufficiently good decision made this afternoon.
Visibility Was Never the Destination
This is particularly important because the supply chain technology industry has spent much of the last decade pursuing visibility.
That investment was necessary. Transportation visibility platforms, control towers, supplier monitoring systems, warehouse technologies, IoT networks, and event-management platforms have dramatically improved the ability of companies to understand what is occurring across distributed operations.
But visibility was never supposed to be the destination.
It was infrastructure for better decisions.
Many companies are now discovering that more visibility does not automatically produce more responsiveness. In some cases, it produces additional alerts, exceptions, dashboards, and notifications that people must investigate.
The resulting paradox is striking: organizations can have near-real-time information flowing into processes that still operate at human administrative speed.
A shipment may be tracked continuously while the response to its delay waits for a meeting.
An inventory problem can appear immediately on a dashboard while the reallocation decision moves through several organizational layers.
A supplier risk may be detected automatically while people spend hours assembling the evidence required to decide what to do about it.
The problem is no longer simply information scarcity. It is decision scalability.
This Is Where AI Becomes More Interesting
Much of the discussion surrounding artificial intelligence still focuses on chatbots, document creation, copilots, and task automation. Those applications will create value, but I suspect they understate AI’s eventual importance to supply chains.
The larger opportunity is to compress decision cycles.
Return to the delayed shipment. An AI-enabled system could detect the disruption, identify the products and orders dependent on the shipment, calculate projected inventory exposure, evaluate alternate sources, compare transportation options, assess customer priorities, and recommend the most economically sensible response.
Within governed boundaries, some of those recommendations could eventually be executed automatically.
The transformation is therefore not simply from manual work to automated work. It is from slow organizational reasoning to much faster machine-assisted reasoning.
That distinction matters.
Enterprise technology originally gave companies systems of record. Later generations increasingly provided systems of visibility. We are now moving toward systems that participate in reasoning and decision making.
ARC’s work on AI in the supply chain points toward an emerging architectural foundation for this transition. Agent-to-Agent communication can allow specialized AI agents to coordinate across functional domains. Model Context Protocol can connect AI systems with enterprise tools and contextual resources. Retrieval-Augmented Generation can ground model responses in trusted enterprise knowledge, while Graph RAG can help AI reason across the relationships that characterize supply chains: supplier to component, component to product, shipment to facility, facility to customer.
The important point is not any individual acronym. It is what happens when these capabilities begin operating together.
A supply chain can increasingly move from merely sensing events toward understanding their significance, evaluating possible responses, coordinating activities across functions, and eventually executing some decisions within defined governance constraints.
That is a fundamentally different operating model.
Now Return to Europe
Viewed from this perspective, Europe’s competitiveness challenge becomes more nuanced.
Europe has not forgotten how to manufacture. It remains home to extraordinary industrial capabilities, deep engineering expertise, sophisticated infrastructure, and companies that lead globally in automation, automotive production, industrial equipment, chemicals, logistics, aerospace, and other complex sectors.
Energy is also unquestionably part of the problem. Europe’s industrial base has faced a difficult adjustment since Russia’s 2022 invasion of Ukraine disrupted an energy model that had provided significant parts of European industry, especially Germany, with access to relatively inexpensive Russian energy.
But energy primarily affects the economics of producing.
Decision velocity affects the economics of adapting.
And adaptation becomes increasingly important when the operating environment changes continuously.
A factory that produces extremely efficiently under stable conditions can still lose ground if competitors respond to changes in demand, supply availability, transportation capacity, customer behavior, or technology considerably faster.
This is why the U.S. technology ecosystem matters to an industrial discussion.
The United States has built enormous capabilities in cloud computing, enterprise software, data infrastructure, venture financing, semiconductors, and artificial intelligence. These technologies do not remain isolated inside the technology sector. They become inputs into the productivity of retailers, manufacturers, distributors, logistics providers, and virtually every other industry.
The connection between digital leadership and industrial leadership is becoming increasingly difficult to separate.
A Fourth Input to Competitiveness
For much of the twentieth century, industrial competitiveness was largely evaluated through labor, capital, and energy. Those three variables remain fundamental, but they no longer capture the entire picture.
A fourth belongs alongside them:
Decision velocity.
How quickly can an organization detect meaningful change? How quickly can it understand the consequences? How quickly can it evaluate alternatives? How quickly can it select and execute a response? And how quickly can the organization learn from the outcome and improve its next decision?
These capabilities affect almost every supply chain performance measure that matters.
Faster, better decisions can reduce inventory.
They can prevent stockouts.
They can improve asset utilization.
They can mitigate disruptions before those disruptions cascade through a network.
They can improve transportation decisions, sourcing decisions, production decisions, and customer-service decisions.
More importantly, the gains compound.
A manufacturer does not make one consequential decision each year. A large industrial enterprise makes thousands upon thousands of operational decisions every day. Improving the quality or speed of any one decision may have negligible financial impact. Improving thousands of them consistently can transform an operating model.
That is why decision velocity potentially matters beyond the enterprise.
Scale those improvements across hundreds or thousands of companies and what initially appears to be a software advantage begins to resemble an economy-wide productivity advantage.
The Next Productivity Gap
Globalization created one of the great productivity transformations of the modern era by allowing companies to reorganize manufacturing and sourcing around global differences in labor cost, production capability, and transportation economics.
Artificial intelligence could create another productivity gap through a very different mechanism.
Instead of moving work geographically, companies may increasingly compress time.
A planning process that previously required a week might take a day. A sourcing decision that took several days might be resolved in hours. A transportation exception requiring hours of investigation might be analyzed in minutes. Routine decisions that currently wait in an approval queue may increasingly occur automatically when predefined conditions are satisfied.
None of these changes sounds revolutionary in isolation.
That is precisely why their potential may be underestimated.
Supply chains consist of millions of decisions. Small reductions in decision-to-action latency, repeated continuously across planning, procurement, production, warehousing, transportation, and fulfillment, can accumulate into substantial productivity differences.
By 2035, I suspect we may view decision latency much differently than we do today.
We routinely measure the time a truck spends waiting at a facility. We measure warehouse dwell time. We measure manufacturing cycle time. We measure supplier lead time.
Eventually, executives may ask another question with equal seriousness:
How much time does our organization spend waiting to decide?
The Supply Chain of 2035
The answer will matter because the supply chain of the next decade is unlikely to be defined by one dominant application.
It will increasingly resemble a network of intelligent systems.
Some decisions will continue to be made entirely by people, particularly those involving strategy, ethics, unusual tradeoffs, or substantial financial consequences. Others will be generated by machines and approved by humans. Still others will become autonomous because the parameters and risks are sufficiently well understood.
The objective should not be maximum automation.
It should be maximum appropriate responsiveness.
That is an important distinction. The point of AI is not to eliminate people from supply chain management. Human judgment, institutional knowledge, negotiation, accountability, and creativity will remain essential.
The opportunity is to eliminate unnecessary latency surrounding those people.
Machines can gather the evidence.
Machines can monitor thousands of conditions simultaneously.
Machines can calculate downstream consequences.
Machines can evaluate routine alternatives.
Humans can concentrate their attention where human judgment creates the greatest value.
The resulting supply chain is not simply more automated. It is more adaptive.
The Lesson Behind the GDP Chart
This is what makes the U.S.–Europe GDP comparison interesting for supply chain leaders.
It would be too simplistic to look at the chart and declare that artificial intelligence, software, or decision velocity explains twenty years of economic divergence. It does not. The historical gap reflects a complex combination of demographics, exchange rates, fiscal and monetary policy, industrial structure, energy, investment, technology, Brexit, and other factors.
But the chart raises an important question about what determines the next twenty years.
Industrial heritage alone will not guarantee industrial leadership.
Neither will excellent infrastructure, inexpensive labor, abundant capital, affordable energy, or superior visibility.
Increasingly, competitive advantage will also depend on how quickly organizations can absorb information, understand what it means, make a decision, and act.
For supply chain executives, this makes decision-to-action latency more than another operational metric. It deserves to become a strategic one.
The last twenty years of supply chain technology helped companies see the world more clearly.
The next twenty years may be defined by how quickly they can respond to what they see.
And that may ultimately prove to be the more consequential transformation.
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