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Europe’s Competitiveness Problem Goes Beyond Energy. It’s About Decision Velocity.

Published

2 mois ago

10 août 2026

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.

The post Europe’s Competitiveness Problem Goes Beyond Energy. It’s About Decision Velocity. appeared first on Logistics Viewpoints.

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From Prompting to Persistent Decision Workflows in Supply Chain Operations

Published

9 heures ago

13 octobre 2026

Supply-chain AI is moving from episodic prompting toward persistent decision workflows. Repeated prompting can help an analyst solve a problem, but an operating capability must maintain context, monitor changing state, preserve decision logic, and connect recommendations to the work that follows.

A persistent decision workflow needs memory of the business object, the current plan, previous recommendations, overrides, policies, and outcomes. Without that continuity, users repeatedly reconstruct context and the model cannot reliably distinguish a new exception from a continuation of an old one. This persistence requirement becomes more important as the AI-agent execution model advances, because an agent cannot act safely if each interaction starts without durable operating state or prior decision context.

A mature design connects detection, context assembly, analysis, option generation, recommendation, approval, execution, and feedback. Some stages may use generative AI; others are better handled by optimization, rules, deterministic integrations, or human judgment. Persistence comes from the workflow and state model, not from the conversational interface.

The system should know what it may observe, recommend, prepare, execute, and escalate. Financial thresholds, customer impact, reversibility, confidence, and policy can determine the autonomy level. These controls make repeated decisions governable at scale.

ERP, planning, TMS, WMS, visibility, risk, and network platforms remain systems of record and execution; decision intelligence sits above and across them where context is assembled, tradeoffs are evaluated, actions are prioritized, and responses are coordinated. The persistent workflow therefore needs controlled write-back or action mechanisms, not only read access and summarization.

A decision workflow should record recommendations, human overrides, actions, and results. That creates an evidence base for improving rules, models, prompts, and autonomy thresholds. A chat transcript alone is not an operating learning system.

The architectural shift is from asking AI for help to designing a governed process in which intelligence participates continuously. That is a much stronger foundation for supply-chain decision support than repeated prompting.

Persistent decision workflows need memory and governance

A durable decision workflow has to preserve operating state across time. It should retain the event, relevant context, alternatives, constraints, prior decisions, approvals, overrides, actions, and outcomes so that the next step does not depend on a user reconstructing the problem in a new prompt. That persistence is what turns AI assistance into an operational capability.

Buyers should test continuity across handoffs. Can the system resume an unresolved decision after new data arrive? Can a different user see why an option was rejected? Can an agent act only within encoded authority? Can the resulting transaction be traced back to the evidence and rule that produced it? These are architecture questions, not user-interface questions.

Persistence changes the economics of decision support

When a workflow is persistent, the organization stops paying the same context-assembly cost every time a decision changes hands. New events can be evaluated against the existing state, prior alternatives remain visible, and approvals or overrides do not have to be reconstructed from email or chat history. That can reduce decision latency while improving traceability. It also creates a better foundation for automation because the system can distinguish a new decision from a continuation of an existing one. Buyers should therefore evaluate memory, state management, versioning, and auditability as operating capabilities rather than treating them as secondary AI features.

The 2026 Market Map is designed to help organizations understand the structure of the Decision Intelligence market, evaluate provider differences, and identify the capabilities most relevant to their operating environment. If your organization is evaluating Decision Intelligence platforms or clarifying where decision intelligence fits within the broader technology architecture, I would be glad to provide the Market Map brochure and discuss the evaluation questions and provider differences most relevant to your requirements.

Request the Decision Intelligence Market Map Brochure

For technology providers

Providers may request the brochure, discuss the research framework, or contact me to confirm how their capabilities are represented in the market assessment.

Discuss the research or confirm your profile

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Why SKU Selection Can Make or Break an AS/RS Business Case

Published

11 heures ago

13 octobre 2026

Why Sku Selection Can Make Or Break An As/rs Business Case

Automated Storage and Retrieval Systems (AS/RS) have become a cornerstone of modern warehouse automation strategies. Whether the technology involves shuttles, cube-based systems, cranes, or goods-to-person solutions, the business case is typically built around a common set of objectives: increasing throughput, reducing labor requirements, improving accuracy, and maximizing storage utilization. Yet one of the most important decisions in any AS/RS implementation often receives far less attention than the technology itself: SKU selection.

Companies frequently spend months evaluating vendors, software platforms, and automation architectures. However, even the most advanced AS/RS can fail to deliver expected returns if the wrong products are placed into the system. In many cases, the difference between a highly successful deployment and a disappointing investment comes down to a simple question: Which SKUs should be automated?

The Purpose of AS/RS Is Productivity, Not Storage

One of the most common misconceptions about AS/RS is that its primary purpose is to store inventory. While increased storage density is certainly a benefit, the real value of automation lies in improving warehouse productivity.

AS/RS creates value by reducing manual labor associated with inventory handling. Every automated retrieval eliminates walking, searching, traveling, and material movement. The more frequently a product is handled, the greater the opportunity for automation to generate measurable savings.

This is where SKU velocity becomes critical.

Not all inventory contributes equally to warehouse activity. In most distribution centers, a relatively small percentage of SKUs generates a disproportionately large percentage of order lines, picks, and replenishment activity. These high-velocity items create the majority of warehouse work and therefore present the greatest opportunity for automation.

Why High-Velocity SKUs Belong in AS/RS

The strongest AS/RS business cases are generally built around automating the products that move most frequently.

Consider two products:

SKU A is picked 2,000 times per week.

SKU B is picked 10 times per month.

Both consume storage space within the AS/RS, but only one generates meaningful operational value.

Every time SKU A is automatically retrieved, labor is eliminated and productivity improves. Over the course of a year, those savings accumulate rapidly. SKU B, on the other hand, occupies valuable automated storage capacity while contributing very little to throughput improvement or labor reduction.

Simply put, high-velocity SKUs produce the transactions that justify the investment.

When organizations prioritize their fastest-moving inventory for AS/RS storage, they maximize:

Labor savings

Pick productivity

Throughput performance

Equipment utilization

Return on investment

This is why leading warehouse operators often use ABC inventory analysis to determine AS/RS eligibility, with A-items receiving priority placement within the automated system.

The Hidden Cost of Selecting the Wrong SKUs

Many automation projects encounter challenges not because the technology underperforms, but because the SKU strategy was flawed from the start.

A common mistake is attempting to place too many slow-moving products into the AS/RS simply because capacity is available. While this may increase storage utilization, it often reduces economic utilization.

An AS/RS filled with low-velocity inventory can create several problems:

Lower Labor Savings

Products that rarely move generate few picking events. As a result, automation replaces little labor activity, weakening the original business case.

Reduced Throughput Benefits

System resources become occupied storing inventory that contributes minimally to order fulfillment activity, limiting the throughput impact of the investment.

Longer Payback Periods

If labor savings and productivity gains fall below projections, the expected payback period can extend significantly beyond the original ROI model.

Capacity Constraints

As demand grows, operators may discover that high-velocity items no longer have sufficient space within the AS/RS because slower-moving products are consuming capacity.

The technology itself may be performing exactly as designed, but the financial return can still disappoint.

Focus on Activity, Not SKU Count

One of the most important lessons for warehouse leaders is that AS/RS success should not be measured by the number of SKUs stored within the system.

Instead, it should be measured by the percentage of warehouse activity flowing through the system.

For example, placing 1,000 high-velocity SKUs into an AS/RS that captures 80% of total picking activity will typically generate greater value than storing 5,000 SKUs that account for only 40% of picks.

The goal is not to automate the greatest amount of inventory.

The goal is to automate the greatest amount of work.

That distinction has significant implications for system utilization and overall project economics.

The Importance of Continuous SKU Evaluation

SKU selection should not be treated as a one-time exercise conducted during system commissioning.

Demand patterns change. Product popularity shifts. New items are introduced while others decline.

As a result, warehouse operators should periodically re-evaluate which products belong inside the AS/RS. High-velocity inventory today may not be high-velocity inventory next year.

Organizations that continuously align automated storage capacity with current demand patterns are often able to sustain higher utilization rates and stronger ROI throughout the life of the system.

ROI strategy

Consider a simple ROI example. Assume a company invests $5 million in an AS/RS and expects to save $1 million annually through labor reduction and productivity improvements. The original business case assumes the system will handle 75 percent of warehouse picking activity.

If the company correctly slots high-velocity products into the system, the projected labor savings can be achieved, resulting in a payback period of approximately five years. However, if management fills a large portion of the AS/RS with slow-moving inventory, the system may only process 40 percent of warehouse picking activity. Annual savings could fall to $600,000, extending the payback period from 5 years to more than 8 years. The technology has not failed. The SKU strategy has.

Final Thoughts

AS/RS technology continues to play an increasingly important role in warehouse automation strategies. Yet successful implementations depend on much more than selecting the right hardware or software platform.

The most effective AS/RS deployments are built around careful SKU selection.

High-velocity products generate the majority of warehouse activity and therefore create the greatest opportunity for labor reduction, throughput improvement, and productivity gains. Conversely, filling automated systems with low-velocity inventory can significantly dilute returns and extend payback periods.

For warehouse and supply chain executives, the takeaway is straightforward: an AS/RS should be designed around activity, not occupancy. The objective is not simply to store products in an automated system. The objective is to automate the work that drives operational performance. In most facilities, that means ensuring the highest-velocity SKUs receive priority access to valuable AS/RS capacity.

The difference between a successful automation investment and an underperforming one may ultimately come down to a decision that appears deceptively simple: choosing the right SKUs.

The post Why SKU Selection Can Make or Break an AS/RS Business Case appeared first on Logistics Viewpoints.

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What the Descartes Innovation Forum Said About the Next Phase of Logistics Execution

Published

11 heures ago

13 octobre 2026

After several days at the 2026 Descartes Innovation Forum, the clearest takeaway was not simply that artificial intelligence is coming to logistics. That part is already obvious. What stood out more was where the technology is moving and why it matters now.

Across the keynote, a transportation leadership panel, a Walmart Marketplace session, and a detailed discussion of carrier fraud, the same operating problem kept surfacing in different forms. Logistics organizations have more data, more applications, more connectivity, and increasingly more AI, but the real value comes when those capabilities help someone make a better decision or execute the next step faster, more reliably, and with the right controls in place.

That matters because the operating environment is not getting easier. Transportation capacity is tightening, rates and fuel costs remain under pressure, fraud and cargo theft are serious concerns, customer expectations continue to rise, and companies are being asked to improve productivity without adding equivalent labor. Against that backdrop, the most interesting technology at the Forum was not technology that simply explained what was happening. It was technology aimed directly at the work itself.

The Swivel-Chair Problem Is Still Real

Descartes opened the Forum with a problem that will sound familiar to anyone who has spent time inside transportation operations. Companies may have capable systems and extensive networks, yet much of the actual work still happens in the gaps between those systems.

Someone receives an email, opens a portal, checks a transportation system, moves to another application, sends a message, waits for a response, updates a record, and then starts the process again somewhere else. Descartes referred to this as the “swivel chair tax,” and the phrase worked because it described a problem technology has never fully eliminated.

The company’s answer is its Agent Control Plane, which is designed to expose capabilities from Descartes applications as skills that AI agents can use. As I covered earlier in Descartes Takes Agentic AI Into the Logistics Workflow at the Descartes Innovation Forum, Descartes was careful to distinguish that approach from a basic chat interface or a traditional workflow engine. The intent is to allow agents to work across applications and adapt as conditions change rather than simply follow a rigid sequence of predefined screens.

The live demonstration made the concept more tangible. Descartes followed an order across four representative companies—a retailer, freight forwarder, customs broker, and final-mile provider—with 15 Descartes products supporting 28 process steps behind the Agent Control Plane. Many of those steps happened without requiring a person to manually move information from one application to another.

What stood out to me was not the amount of automation. It was where Descartes chose not to automate.

At one point, an agent identified a carrier option that could save roughly 8 percent. The carrier, however, had a poor trust score, thin operating history, and other warning signs. Rather than make the lower-cost choice automatically, the system surfaced the decision for human review. The human declined the cheaper carrier.

That is a much more practical model for agentic AI in logistics than the idea of simply handing an operating process over to a machine. Descartes repeatedly described autonomy as a dial, not a switch. Agents have identities, their actions are logged, activity can be replayed, and organizations can determine which decisions require human approval.

The significance is straightforward. Once AI begins taking actions inside real operating processes, governance stops being an abstract IT issue and becomes part of the operating model.

The Market Is Making Better Execution More Valuable

The transportation leadership panel I moderated later in the Forum put that technology discussion into a much tougher operating context.

The freight market described by the panelists was not one of strong demand and easy growth. It was one in which relatively weak demand is colliding with meaningful reductions in capacity. One participant cited a decline in the number of trucking companies from roughly 340,000 to 282,000 and dry-van truckload rates up approximately 33 percent year over year. The larger point was that the conversation has shifted. Two years ago, shippers were largely focused on rates. Today, capacity availability and reliability matter much more.

That tighter environment also increases risk. When capacity is harder to find, there is greater temptation to use carriers that may not have passed the same diligence in a looser market. Panelists tied that pressure to growing concerns around freight fraud, cargo theft, safety, insurance, litigation, and carrier qualification.

As discussed in Transportation Leaders See AI Moving From Experiment to Operating Model at the Descartes Innovation Forum, the response is not simply to cut spending. Technology did not sound discretionary in this discussion. One participant described a single repetitive business activity being performed roughly 750,000 times per month, representing the equivalent of about 1,500 employee-hours per day. That is exactly the kind of workload where automation can move from being interesting to economically significant.

The panel also showed how quickly expectations around technology development are changing. Voice agents, email automation, operational workflows, data science, and agentic AI were all discussed as practical tools rather than distant possibilities. One participant described a proprietary TMS containing more than 30 million lines of code and roughly 2,000 database tables. A 12-person team working with AWS and Anthropic was then tasked with recreating the system using AI-assisted development and reportedly produced a replication in approximately six weeks.

Replicating a system is not the same as proving it in full production, so that example deserves some caution. Still, it shows how quickly the economics of software development may be changing.

Transportation technology has traditionally changed slowly because systems were complex, deeply integrated, and expensive to modify. AI may compress that cycle dramatically. For transportation leaders, that creates an interesting tension: the operating environment is getting harder at the same time that the technology available to respond to it is improving faster.

Walmart Brought the Conversation Back to the Customer

A Walmart Marketplace session provided a useful counterweight to the AI-heavy discussions elsewhere at the Forum. Bryce Caswell, Director of Ship with Walmart for Walmart Marketplace, focused on something much more basic: the customer promise.

Walmart described four foundations for marketplace success—assortment, listing quality, pricing, and shipping—and emphasized that the four are interdependent. Good assortment will not solve poor content. Good content will not offset uncompetitive pricing. And none of it matters if the seller cannot deliver the product when promised.

That may sound obvious, but it was one of the more useful reminders of the Forum. Logistics technology discussions can quickly become dominated by architecture, optimization, visibility, and AI. The customer evaluates something much simpler: did the order arrive when you said it would?

As I wrote in Walmart Is Turning Marketplace Logistics Into a Platform, Walmart’s emphasis was not simply on speed. Reliability and consistency mattered just as much. During holiday peaks, sellers were encouraged to protect inventory availability, leave enough time to move product into the fulfillment network, understand what they are promising, and then meet that promise.

The company is also trying to simplify the mechanics underneath that promise. Walmart Fulfillment Services provides pick, pack, and ship capabilities through Walmart’s fulfillment network, while its multi-channel model allows sellers to support multiple sales channels from a single inventory pool.

Ship with Walmart extends that logic into transportation by offering access to negotiated carrier rates, including national, regional, and LTL options, while adding certain protections around on-time delivery and lost shipments.

The larger point is that simplification has value only if it improves execution. Whether the tool is an AI agent or a fulfillment network, the objective is the same: remove unnecessary friction so people can focus on the decisions and exceptions that actually matter.

Fraud Changes the Meaning of Visibility

The Forum’s carrier fraud session added another layer to the discussion because it showed how operating data is being repurposed for trust.

Descartes demonstrated how MyCarrierPortal and MacroPoint can be used together to strengthen carrier vetting before a load is tendered. The process extends beyond checking insurance or basic carrier status. It can incorporate configurable risk assessments, tracking history, lane history, inspection information, validated users, equipment data, and driver-level tracking statistics.

That is a subtle but important shift in how transportation visibility data is used. Historically, tracking information primarily answered a familiar question: where is my load? Increasingly, that same information can help answer a different question: should I trust the party moving it?

The same theme runs through Descartes Expands Freight Fraud Prevention Across Carrier Vetting and Shipment Execution. Descartes showed how historical lane activity and tracking behavior can be used as additional carrier-vetting signals. Once a shipment is in transit, the Notification Center can surface route deviations and other exceptions that warrant attention without requiring someone to monitor every load manually.

ELD Plus adds another layer by allowing two tracking sources to be used within the same tracking session. If mobile-device data and telematics data begin to diverge, that mismatch becomes a signal that someone may need to investigate.

Carrier lookup goes further by combining information such as power units, active loads, tracking history, spoofing flags, and recent lane activity. The example used during the session was simple: if a carrier has 100 power units but appears to be active on 600 loads, that discrepancy may deserve a closer look.

More automation does not reduce the need for trust. It raises the stakes. If systems are going to make recommendations or take actions faster, the quality of the signals behind those decisions becomes even more important.

Execution Is Becoming the Real Battleground

Looking back across the Forum, I do not think the central story was simply AI. It was execution.

Descartes is trying to reduce the swivel-chair work that still exists between logistics applications and companies. Transportation leaders are looking for technology that can improve productivity and help them operate through a tighter, riskier freight market. Walmart is focused on simplifying fulfillment so sellers can keep the customer promise. Carrier-verification tools are turning tracking data into a source of trust as well as visibility.

Put together, that points to a broader shift in logistics technology. Software is moving closer to the actual transaction, the actual decision, and the actual exception. The value of that technology will increasingly be judged by whether it helps organizations act better, not simply see more.

The companies that benefit most will not necessarily be the ones with the most AI, the most dashboards, or the most data. They will be the ones that can connect intelligence to execution without losing control of the process.

For me, that was the real takeaway from the 2026 Descartes Innovation Forum. The next phase of logistics technology is not just about making systems smarter. It is about knowing when the system should act, when a person should decide, and how to make both work together fast enough to keep freight moving and the customer promise intact.

The post What the Descartes Innovation Forum Said About the Next Phase of Logistics Execution appeared first on Logistics Viewpoints.