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From Automation to Intelligence: ARC’s Next Industrial Technology Chapter

Published

2 mois ago

11 août 2026

In 1986, industrial technology looked very different. Factories were becoming more automated, but they were still dominated by specialized systems built to control specific machines and processes. Distributed control systems, programmable logic controllers, drives, instrumentation, and plant-floor computing were changing what manufacturers could see and manage, but many of those technologies still lived in relatively isolated worlds. The plant floor had its systems, the enterprise had others, and warehouses, transportation networks, suppliers, and customers operated through still more layers of technology, many of them disconnected.

It was into that world that Automation Research Corporation, later ARC Advisory Group, was founded. At the time, one of the central industrial technology questions was how far computing could reach into the physical systems that actually ran factories, utilities, energy infrastructure, and industrial operations. Almost forty years later, we know the answer: it reached nearly everywhere.

Now a new question is taking its place. What happens when intelligence does the same?

For Logistics Viewpoints, as part of ARC Advisory Group, that is not simply another AI story. It is the next chapter in a much longer technological progression that ARC has been watching since its beginning. Industrial technology learned first to control physical processes, then to connect them, then to see what was happening across them. Eventually, it learned to predict what might happen next.

Now it is beginning to decide what should happen next—and, increasingly, to act.

The Machines Learned to Talk

The first great transformation was the automation of physical processes. Machines became increasingly instrumented, control systems became more sophisticated, and industrial companies gained finer control over production, quality, throughput, and equipment performance. But automation had limits. A system might know exactly what was happening inside one production process while remaining almost completely blind to what was happening elsewhere in the organization.

The next breakthrough, therefore, was connectivity. Over time, systems that had once functioned independently began to communicate. Plant-floor systems connected with manufacturing applications, manufacturing information flowed into enterprise software, industrial Ethernet spread, standards improved, proprietary environments became more open, and operational technology and information technology began slowly moving toward one another.

That transition took years, and it rarely unfolded as neatly as technology presentations suggested it would. Factories could not simply stop running while their technology stacks were replaced. Refineries could not be rebuilt every time a new software architecture appeared. Warehouses, utilities, railroads, and global supply chains carried decades of accumulated infrastructure with them, so new technologies had to work alongside old ones.

That became one of the enduring lessons of industrial technology: invention matters, but integration determines whether the invention becomes useful.

Then Everything Began Producing Data

Once the industrial world became more connected, another transformation followed almost automatically. Connected systems produced information—enormous amounts of it. Machines, production systems, orders, warehouses, trucks, suppliers, and customers all became sources of data. At first, simply collecting and displaying that information created tremendous value.

Supply chain technology illustrates this particularly well. For years, companies struggled to understand what was happening beyond the walls of their own facilities. A shipment could leave a supplier and effectively disappear into a transportation network until it arrived—or failed to arrive. Transportation visibility platforms began changing that. Warehouse management systems provided increasingly detailed operational insight, control towers attempted to bring information from multiple systems into common views, supplier platforms digitized procurement relationships, and planning systems connected demand, inventory, production, and replenishment.

The industry spent enormous amounts of money trying to see the supply chain more clearly. And it worked, perhaps too well.

Visibility Created a New Problem

A modern supply chain can now tell you an extraordinary amount about itself. A shipment is running late. A supplier has missed a commitment. Inventory is below plan. A vessel is delayed. Demand is accelerating. A carrier has rejected a load. A distribution center is falling behind. A customer order is at risk.

Every one of those events can generate an alert, and a large enterprise can generate thousands of them. That creates an uncomfortable realization: the problem is no longer simply that organizations cannot see what is happening. Increasingly, the problem is that they can see too much.

Somewhere along the way, visibility ceased to be the final objective and became the beginning of another problem. Once you know that something has gone wrong, someone still has to determine whether it matters, understand why it happened, evaluate the alternatives, make a decision, and then act. For decades, that “someone” was usually a person.

Now, for the first time, that assumption is beginning to change.

AI Enters the Operating Model

Most of the public discussion around artificial intelligence has focused on what AI can generate: text, images, code, answers, and summaries. Those capabilities are significant, but in industrial settings they may turn out to be the least important part of the story. The larger change begins when AI starts participating in operations.

Imagine a delayed inbound shipment. A traditional visibility system tells the planner that the container will arrive three days late. That is useful information, but the real business questions begin immediately afterward. What is inside the container? Which plants need those materials? Which production orders depend on them? Which customer commitments could be affected? Is replacement inventory available somewhere else? Can another supplier provide the component? Could production be resequenced? Would expediting another shipment cost less than interrupting production?

Ultimately, the organization needs to know which response produces the lowest total business impact while protecting its most important commitments. That is no longer simply a visibility problem. It is a reasoning problem, and increasingly it is becoming an AI problem.

From Information to Understanding

This is where the emerging architecture of industrial AI begins to matter.

AI needs more than a powerful model. It needs access to the information, relationships, tools, and operating context of the enterprise. Retrieval-augmented generation can ground AI in company-specific knowledge. Graph-based approaches can help it understand relationships among suppliers, products, plants, shipments, customers, and orders. Agent-to-agent communication can allow specialized AI systems to coordinate, while emerging protocols can connect those systems with enterprise tools and contextual resources.

ARC’s work on AI in the supply chain describes this as the foundation for a connected intelligence layer spanning existing enterprise and operational systems. The important point is not the collection of acronyms surrounding AI. It is what the architecture makes possible: moving from an AI system that answers an isolated question toward one that understands what an event means within the broader operation.

That distinction is particularly important in supply chains because almost nothing exists independently. A supplier is connected to components. Components are connected to products. Products are connected to plants. Plants are connected to customers. Shipments are connected to orders, and orders are connected to revenue.

A late container is therefore not simply a late container. It may be the beginning of a chain reaction.

And once a system can understand that chain reaction, the next question becomes unavoidable: should it act?

Software Begins to Cross the Line

For most of the enterprise software era, the division of labor between humans and systems was clear. Software stored transactions, calculated plans, displayed exceptions, produced forecasts, and recommended options. People connected the dots. A planner saw the problem, investigated the cause, called another department, evaluated alternatives, made a decision, entered the change, and watched what happened next.

Agentic AI begins to blur that division. Consider the same delayed shipment. A shipment agent detects the delay while an inventory agent determines which locations will be affected. A production agent identifies manufacturing orders at risk, a procurement agent searches for alternative supply, and a transportation agent evaluates expedited options. Those systems can potentially exchange information, compare alternatives, and weigh cost, service, inventory, and production consequences.

If the problem falls within predefined authority limits, the system could eventually take the action itself. If it does not, it could send the issue to a human with much of the investigative and analytical work already completed.

That is not simply better analytics. It represents a different operating model because software is beginning to cross the boundary between supporting work and performing work.

Forty Years in One Line

Seen from the perspective of 1986, the progression is striking.

Automation → Connectivity → Visibility → Prediction → Decision → Action.

Automation gave industrial systems the ability to control physical processes. Connectivity allowed those systems to exchange information. Visibility gave organizations a clearer understanding of what was happening across increasingly complex operations. Predictive analytics helped anticipate what might happen next. Decision intelligence began recommending what organizations should do about it.

Now agentic AI is beginning to explore the final step: taking action.

It is not a perfect description of every technology category, and not every operational process will move through all six stages. Nor should every business decision become autonomous. But the framework captures the larger direction of travel.

What makes the current moment especially interesting is that the final two stages are developing together. Decision intelligence and agentic AI are advancing at the same time, meaning the gap between knowing what should happen and actually making it happen could begin to shrink dramatically.

And Then Reality Arrives

Of course, every technology revolution looks easiest before it touches an operating environment. An AI agent resolving a simulated supply chain problem is impressive. Giving that agent authority over a real production schedule, inventory allocation, transportation move, procurement decision, or customer commitment is something else entirely.

Suddenly the questions become less glamorous and much more important. What information can the agent access? Which systems can it modify? How much money can it commit? Which decisions require human approval? What happens when two agents optimize for different objectives? How do you reconstruct a decision six months later? Who is responsible when the system is wrong? And how does this sophisticated new intelligence layer interact with an ERP implementation that may be fifteen years old or a warehouse system that the business cannot afford to replace?

These questions may sound like constraints, but they are actually where industrial transformation happens. ARC has seen variations of this problem repeatedly. Open systems did not make proprietary infrastructure disappear overnight. Cloud computing did not eliminate enterprise systems. Industrial IoT did not replace control systems. Digital transformation did not sweep away decades of installed technology. Instead, every new layer had to find its place within what already existed.

AI will be no different.

The Future Will Be Built on the Past

There is a tendency in technology to describe every new era as though everything before it suddenly became obsolete. Industrial technology rarely works that way. The future tends to accumulate rather than replace: new layers sit on top of old ones, new architectures connect with installed systems, and new intelligence depends on existing transaction platforms.

AI will not erase ERP, TMS, WMS, procurement, planning, or control systems. It will increasingly operate across them. That is why the AI conversation inside industry will eventually become less about models and more about architecture: data harmonization, interoperability, security, governance, context, decision rights, and human oversight.

Those are the mechanisms through which AI stops being an impressive demonstration and becomes part of the operating fabric of the enterprise. The real competitive advantage may ultimately come not from having access to the most powerful model, but from connecting intelligence successfully to the data, systems, workflows, and decisions through which the business actually operates.

The Categories Are Starting to Move

We can already see the consequences in the supply chain software market. Visibility platforms are moving toward exception management. Exception management is moving toward decision intelligence. Decision intelligence is moving toward execution. Planning systems are incorporating generative AI, transportation and warehouse platforms are beginning to add autonomous capabilities, and control towers are evolving toward orchestration environments.

Software categories that once seemed distinct are beginning to overlap. That makes this a particularly important moment for technology research because mature and emerging markets require very different kinds of analysis. In an established category, the questions are familiar: Who are the leaders? How large is the market? What features does each supplier offer? How quickly is the category growing?

Emerging markets create harder questions. What exactly is the category? Where does it begin and end? Which capabilities actually belong inside it? What architecture is required? Which decisions should remain advisory and which can become autonomous? What level of buyer maturity is necessary?

Before suppliers can be compared, the market itself often has to be defined.

That has always been one of the most important functions of industrial technology research, and it becomes particularly important when the underlying architecture is changing as quickly as it is today.

Back to 1986

And so, after nearly forty years, the story comes almost full circle.

When ARC began, the defining question was what would happen as computing became deeply embedded in industrial operations. The answer unfolded over decades as machines became automated, systems became connected, operations became visible, data became pervasive, and analytics became predictive.

Now the industry is moving into another phase. The question is no longer simply whether industrial systems can collect information or even understand what is happening. The question is whether they can increasingly determine what should happen next and whether, under the right circumstances, they should be allowed to make it happen.

For Logistics Viewpoints, that makes the AI era particularly significant. We are not watching a technology category appear in isolation. We are watching the next stage in the evolution of the systems that run supply chains and, more broadly, the physical economy.

ARC has spent nearly forty years studying that evolution. The technologies have changed, but the underlying question has not: How does a breakthrough in computing become something industry can actually trust, integrate, and use?

In 1986, that question began with automation. Today, it begins with intelligence.

The next industrial era will be defined not simply by systems that can see more or predict more, but by systems increasingly capable of deciding and acting. For ARC, that makes AI less a break with its past than the logical continuation of it: another fundamental change in the architecture of the physical economy, and another transition whose real significance will only become clear when the technology meets operations.

The post From Automation to Intelligence: ARC’s Next Industrial Technology Chapter 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.

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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.