Uber’s decision to reduce its workforce by about 10% will inevitably be discussed as another large technology-company layoff. For logistics executives, however, the more important question is not whether AI eliminated these jobs. It is whether AI is beginning to eliminate some of the organizational structures that made many of those jobs necessary.

Reuters reported that approximately 3,300 positions will be affected in Uber’s largest workforce reduction since the pandemic. CEO Dara Khosrowshahi said the company is removing management layers, simplifying team structures, clarifying ownership and redirecting investment toward its largest opportunities. Uber itself describes the objective as becoming a “simpler, faster” company. Importantly, Khosrowshahi did not attribute the workforce reduction directly to artificial intelligence.

That distinction matters because the deeper logistics story is not simply about automating individual jobs. It is about reducing the coordination required to make an increasingly complex enterprise operate.

The Coordination Tax

Large organizations accumulate complexity almost naturally. Products, geographies, customers and channels multiply, followed by planners, analysts, supervisors, project managers and functional organizations to manage them. Each addition can make sense individually while the cumulative result is an organization in which a surprising amount of work consists of coordinating other work.

Uber is explicitly attacking that problem. The company says growth brought more layers, more coordination and increasingly fragmented ownership. It is broadening management spans, reducing positions concentrated on coordination and eliminating many “micro-teams” with only one or two reports. Uber says the number of employees seven or more organizational layers below the CEO will decline by 20%, while the number of micro-teams will fall by nearly half.

Anyone who has worked around a large logistics organization should recognize the pattern. A routine transportation exception can generate an alert, an email, a carrier call, another information request, a supervisor escalation, a customer update and eventually a KPI entry explaining what happened. No single step is necessarily unreasonable. The inefficiency lies in the number of people and systems through which information must travel before somebody has enough context and authority to act.

AI agents begin to change that equation. An agent can monitor a transaction, identify an exception, gather contextual information, consult business rules, communicate with other systems, recommend an action and, within defined guardrails, execute it. The opportunity is therefore larger than making every planner or analyst incrementally faster. In some workflows, the larger gain comes from eliminating the handoffs themselves.

This Is a Systems Engineering Problem

This is why I believe much of the current discussion around AI in logistics remains too narrow. We tend to evaluate individual technologies when the more important issue is how those technologies interact with people, physical assets, information flows, decision rights and business processes.

That is the central argument in our recent white paper, Systems Engineering in Logistics. Logistics performance does not emerge from a TMS, WMS, control tower, robotics platform or AI model operating independently. It emerges from the behavior of the larger system.

Uber’s restructuring is a useful real-world example. The company is not simply deploying another AI application. It is reconsidering management spans, operating structures, accountability and capital allocation while introducing increasingly capable digital systems.

The logistics industry has spent decades digitizing individual functions. Transportation received a TMS, warehousing received a WMS, planning acquired specialized applications, customer service adopted CRM platforms, and visibility produced control towers. The technology architecture became more sophisticated, but the organizational architecture often remained substantially unchanged.

AI provides an opportunity to revisit that architecture. If systems can increasingly exchange information, interpret events and execute routine decisions without waiting for a human intermediary, logistics leaders should ask more than, “Which tasks can AI automate?”

A better question is: “Which organizational boundaries exist because humans historically had difficulty coordinating information and decisions across them?”

Uber Freight Is Already Showing Us the Model

Uber’s own freight business provides a concrete example of what that transition looks like.

In its second-quarter 2026 prepared remarks, Uber said Freight is investing in AI capabilities designed to optimize transportation decisions for customers. The company specifically identified earlier detection of shipment risks and automation of routine operational workflows, including responding to shipment inquiries, validating documents and correcting shipment data.

Those may appear to be incremental applications, but they target precisely the activities that generate administrative work throughout transportation organizations. Every automatically resolved shipment inquiry or corrected document can remove an email, a queue, a handoff or an escalation.

The progression matters. The first wave of generative AI in business was mainly about individual productivity: write an email faster, summarize a document, generate code, help an analyst find an answer. The next wave connects AI to workflows, enterprise data, APIs and business rules.

At that point, AI stops being merely a productivity application and becomes part of the operating model.

Logistics is particularly exposed to this transition because freight procurement, appointment scheduling, track-and-trace, carrier communication, invoice reconciliation, inventory exceptions and delivery management all depend on large volumes of structured information moving among organizations and systems.

That is fertile ground for agentic automation.

Uber Is Also Betting on Physical Autonomy

There is another dimension to the Uber story. While simplifying its human organization and expanding AI use, the company is simultaneously making a very large investment in autonomous transportation.

Uber said in its Q2 2026 prepared remarks that it expects to commit more than $10 billion over the coming years through equity investments, infrastructure and vehicle commitments intended to bring autonomous vehicles to market at scale. Autonomous vehicles were already live on Uber in seven cities, the company said, with as many as 15 expected by year-end. Its partners have committed approximately 120,000 vehicles to the Uber network over the coming years.

What is particularly interesting is how Uber defines its role. The company does not need to manufacture every vehicle or develop every autonomous-driving system. Instead, it can provide demand aggregation, dispatch intelligence, vehicle integration, fleet operations, charging infrastructure, financing, insurance and regulatory relationships around an ecosystem of partners.

Uber is increasingly positioning itself not simply as a transportation marketplace, but as an orchestration layer across digital and physical transportation.

That distinction should matter to logistics executives. Autonomous transportation does not end with removing the human driver. The network still requires demand forecasting, capacity allocation, dispatch, maintenance, charging or fueling, customer communication, exception management and financial settlement.

If physical automation develops alongside digital agents capable of coordinating those activities, the operating model changes much more profoundly.

Digital and Physical Autonomy Converge

We are therefore beginning to see two forms of autonomy develop at the same time. Physical autonomy moves vehicles and goods with less direct human operation. Digital autonomy makes and coordinates a growing number of the decisions surrounding those movements.

Consider an autonomous delivery network in which AI agents forecast demand, allocate capacity, reposition vehicles, schedule charging, monitor maintenance, communicate with customers and manage exceptions. Removing the driver is only one component of the automation. Much of the administrative infrastructure surrounding the vehicle can also become increasingly autonomous.

The important development is not any one technology. It is the interaction among them.

That is again a systems-engineering issue.

What Logistics Leaders Should Look For

This does not mean logistics companies should begin eliminating management layers simply because Uber is doing so. Nor does it suggest that human judgment becomes unimportant. The implication is that companies should begin identifying where coordination costs have become embedded in their operating models.

Where does information sit waiting for somebody to move it? Where does an exception pass through several employees before reaching someone with the authority to resolve it? Where are multiple groups maintaining slightly different versions of the same operational truth? Where do recurring meetings exist because underlying systems and decision rights remain poorly integrated?

These are no longer merely process-improvement questions. They are increasingly systems-architecture and AI questions.

The organizations that gain the most from AI may therefore not be those that deploy the largest number of copilots. They may be the organizations willing to redesign processes once the technological limitations that created those processes begin to disappear.

Human expertise remains essential, but its value shifts toward judgment, relationships, system design, accountability, risk management, strategic tradeoffs and genuinely novel exceptions. Routine information gathering, reconciliation and coordination become increasingly machine-assisted or machine-executed.

The likely result is a flatter logistics organization with clearer process ownership, broader spans of control, fewer administrative handoffs and more automated decision execution.

Uber’s restructuring is worth watching because several developments are occurring simultaneously. The company is reducing organizational layers, redesigning operating structures, applying AI to transportation workflows and investing billions of dollars in autonomous mobility.

Viewed independently, each initiative is interesting. Viewed as a system, they point toward something much larger.

The future logistics enterprise may not simply automate more tasks. It may require far fewer layers to coordinate them.

The post Uber’s Restructuring Shows Where AI in Logistics Is Really Going appeared first on Logistics Viewpoints.

Weekly highlights

Ocean rates – Freightos Baltic Index

Asia-US West Coast prices (FBX01 Weekly) increased 2%.

Asia-US East Coast prices (FBX03 Weekly) increased 2%.

Asia-N. Europe prices (FBX11 Weekly) decreased 1%.

Asia-Mediterranean prices (FBX13 Weekly) decreased 4%.

Air rates – Freightos Air Index

China – N. America weekly prices decreased 7%.

China – N. Europe weekly prices increased 1%.

N. Europe – N. America weekly prices stayed level.

Analysis

The increasingly cold war in the Strait of Hormuz – including reported progress in an Iran-Oman authority-sharing agreement – heated up a little recently. Alongside more Iranian strikes on vessels and US claims of demining progress, the US hit Iranian rocket launchers possibly dedicated to deploying more mines and Iran responded by targeting US sites in Jordan.

Transpacific ocean rates ticked up by 2% last week to new peak season highs for both coasts as volume strength has stretched on through August despite the early start to peak season demand back in late May.

Prices passed the $7,600/FEU mark for the West Coast and climbed to about $9,800/FEU to the East Coast. Carriers are increasing capacity slightly for September in anticipation of still-elevated volumes – with more rate increases, especially for the East Coast, possible to start the month – up until October’s Golden Week, with blanked sailings set for the holiday period. Though there is no clear explanation for the surprisingly resilient demand, the absence of tariff hikes in late July and an increase in data center hardware volumes may both be contributing. Tariff refunds that are enabling some retailers to reduce prices may also be spurring some retailers to increase inventories.

Coming Panama Canal, low water restrictions have some carriers planning surcharges for transiting containers in September, which could add pressure on some East Coast rates soon.

Another likely contributor to elevated transpacific container rates is the unrelenting congestion in major Far East hubs from the succession of typhoons that have hit the region since mid-July. The latest, Typhoon Saudel, closed ports including Shanghai and Ningbo for several days last week, disrupted operations as far north as Busan and could stay strong enough to impact Shenzhen later this week.

The series of storms has prevented impacted ports from completely clearing backlogs before new shutdowns, with as many as ninety ships waiting more than a week for a slot in Shanghai, and carriers skipping calls at backed up ports leading to increased transhipment volumes at other ports in the region.

Far East congestion – as well as N. Europe hub backlogs, partly due to low, but improving, water levels in the Rhine – is also a factor to current Asia – Europe rate levels. Prices have cooled on easing demand since mid-July but capacity constraints may be helping rates remain above pre-peak levels. Asia – N. Europe prices have fallen more than $1,000/FEU since their July peak, but at $4,600/FEU are up about 70% compared to before the early start of peak season in mid-May. Rates at $4,800/FEU to the Mediterranean are down more than $2,000/FEU but are still 40% higher than three months ago.

Transatlantic rates climbed $400/FEU in the last two weeks to $2,600/FEU as carriers reduce capacity on the lane. Several carriers are planning additional, significant price increases for September, though some observers are skeptical that these rate hikes will stick.

In air cargo, the Freightos Air Index global benchmark eased 10% last week, but remains more than 20% higher than a year ago due to elevated fuel costs and some lingering capacity constraints. Far East – US rates eased 7% to about $6.00/kg and prices to Europe ticked up 1% to $4.60/kg last week, though both lanes are trending up so far this week, possibly due, once again, to typhoon-driven disruptions.

The post Typhoon disruptions driving congestion and putting pressure on rates – September 25, 2026 Update appeared first on Freightos.

What Is a WMS in 2026? The Warehouse Management System Is Becoming Something More is ultimately a question about category boundaries. In 2026, warehouse management systems still has a recognizable core, but the value increasingly comes from what happens around that core: how operating state is shared, how decisions are coordinated, and how quickly the system can respond when conditions change. Buyers therefore need a definition based on the work the platform is accountable for, not on the longest possible feature list.

The core job has not disappeared

At the center, the category remains the operational system that manages inventory location, warehouse work, task priorities, replenishment, picking, packing, staging, and shipping inside the distribution operation. Core execution discipline matters because advanced analytics or AI cannot compensate for weak transaction integrity, incomplete master data, or unreliable operating state. A modern platform has to do the foundational work consistently before its higher-order intelligence becomes valuable.

That foundation now spans inventory control, receiving and putaway, replenishment, wave and waveless work release, picking and packing, labor coordination, shipping, yard and dock interfaces, analytics, and increasingly automation orchestration and AI-assisted decision support. The breadth matters, but breadth alone is not the differentiator. Two products can check many of the same boxes and behave very differently under real operating pressure.

The category boundary is expanding

The market is being pulled outward by labor scarcity, tighter customer cutoffs, omnichannel fulfillment, higher SKU complexity, automation investment, faster order cycles, and the need to coordinate people and machines in real time. As a result, platforms are being asked to operate on shorter planning cycles, exchange more events with adjacent systems, and support decisions that used to be handled through email, spreadsheets, meetings, or manual follow-up.

The architectural context is increasingly ERP and OMS upstream; WMS at the inventory-and-work core; WES/WCS, robotics, conveyors, sortation, labor systems, YMS, parcel, and TMS around the execution edge. That makes interoperability part of functional performance. A capability that cannot receive the required state, make a timely decision, or push a usable action into the execution environment is less valuable than its demo may suggest.

What still defines the boundary

A WMS should remain accountable for warehouse inventory and work state even as orchestration, automation control, and decision support extend beyond the traditional application boundary

A useful category definition should therefore separate adjacent capabilities from genuine responsibility. The question is not whether the platform can display or discuss warehouse management systems; it is whether it can reliably perform the work, govern the decisions, and sustain the operating state that the category requires.

The 2026 buyer test

Buyers should evaluate operational fit, configurability without excessive customization, automation integration, real-time work orchestration, data and API architecture, scalability, implementation model, upgradeability, and measurable warehouse outcomes. The practical proof should come from operating scenarios such as a late inbound trailer, a constrained dock, a wave that threatens a carrier cutoff, an automation cell that goes down, or an urgent order that must be reprioritized without destabilizing the rest of the facility. Those scenarios force providers to show how the product behaves when plans change, data are incomplete, objectives conflict, or the preferred option disappears.

That is what makes the 2026 market different. The category is no longer defined only by what the software records. It is increasingly defined by how effectively it helps the operation decide and act.

A broader WMS category needs stronger boundary discipline

As WMS expands into orchestration, automation, labor, analytics, and AI-assisted work, buyers should be more—not less—precise about accountability. Inventory state, work state, task release, exception handling, and shipping execution still need an authoritative operating core. Adjacent tools may contribute specialized intelligence or equipment control, but the architecture should make clear which system owns the state that downstream decisions depend on.

This matters during implementation as much as selection. A platform can look broad in a demonstration yet create fragile operations if inventory, work priorities, automation signals, and carrier cutoffs are reconciled through custom logic outside the product. Buyers should ask where state lives, how quickly it changes, which interfaces are standard, and how the design behaves during upgrades, automation outages, or sudden reprioritization.

Related Logistics Viewpoints research

2026 Warehouse Management Systems Market Map
The New Architecture of Logistics
Systems Engineering in Logistics
The Digital Backbone of the Warehouse: Trends Shaping the 2026 WMS Market

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