“By integrating Samsara Asset Tags, we’ve gained real-time visibility over £7.2 million worth of specialist equipment. What used to take weeks to locate is now found in minutes, allowing us to prevent theft and loss to the tune of £60,000 annually,” said Amber Kirkby, Product Owner of Samsara at Lanes Group. “It has transformed our operational efficiency by ensuring our teams always have the right tools exactly when they need them.”

Operations network just got better

Over the last two years, Samsara’s Network has doubled in density, reinforcing its position as one of the industry’s leading industrial-grade Bluetooth networks. This expansive mesh network leverages millions of Samsara-connected devices. By using industrial-grade Bluetooth signals to continuously ‘listen’ for Asset Tags, a single Asset Tag can be detected in real time.

To provide an even more comprehensive view, Samsara has integrated Hubble’s Terrestrial Network, comprised of 90M consumer smartphones. This integration builds on Samsara’s presence on roads, at job sites, and in residential areas by extending visibility into buildings.

“The integration with Hubble complements Samsara’s existing network,” said David Gal, VP of Connected Equipment at Samsara. “The Samsara Network leverages millions of gateways on assets from construction sites to motorways to rubbish trucks, while Hubble’s network uses primarily consumer smartphones, ensuring no lost or stolen asset can hide, even inside buildings. The best network in the business just got better, delivering unprecedented asset visibility.”

Intelligence delivers increased visibility and rapid asset recovery

With Samsara’s end-to-end theft and loss workflow, organisations can now detect at risk assets sooner, investigate incidents, and coordinate fast recoveries.
● Proactively identify at-risk equipment: With the new Left Behind Incident feature, managers are immediately notified when an asset is separated from its vehicle outside a trusted geofence. Rather than discovering the loss days or weeks later, customers can respond in real time to recover assets and prevent costly disruptions.
● Investigate with real-time information: Customers can mark an asset as missing and see critical context, such as photos of who last had the asset, which vehicle it was last seen with, and more, powered by StreetSense. This rich context helps determine the most efficient recovery method and allocates the resources needed for a successful retrieval.
● Rapidly recover assets and avoid lost time: Customers can coordinate quick asset recovery by dispatching a driver or sharing asset location with local authorities. Once dispatched, crews can quickly pinpoint an asset’s exact location using Compass Mode.
● Demonstrate return on investment: The new Asset Tag Overview page analyses asset photos with AI to calculate the value of assets protected and recovered. By tracking the total monetary value of assets, managers can demonstrate financial impact on the business.

Sized for equipment big and small. There’s nothing you can’t track.

The new Asset Tags are ruggedised devices engineered to operate in the most extreme and remote environments. With the compact Asset Tag and ultra-compact Asset Tag XS, equipment managers can mix and match devices based on the equipment’s size and shape.

● Asset Tag: Designed for both large and small equipment, the Asset Tag provides up to six years of maintenance-free battery life—a 50% increase over the previous generation.
● Asset Tag XS: Ideal for even smaller, high-value handheld tools or specialised equipment such as gas meters or IV pumps, the ultra-compact Asset Tag XS offers three years of battery life and flexible mounting options for the most obscure equipment.

“The scale of equipment loss in physical operations goes far beyond the cost of the tools themselves—it’s about lost productivity and project delays,” said David Gal, VP of Connected Equipment at Samsara. “To solve this, we’re doubling down on innovation, laying the foundation for new use cases. We’ve supercharged the network, the hardware, and the recovery workflow, and with the Asset Tag XS, now even the smallest assets stay within reach.”

New research reveals the multi-million dollar impact of asset loss

In physical operations, small assets play a big role in getting the job done; however, keeping track of these mission-critical tools is a growing challenge.

Research from Samsara’s forthcoming State of Connected Operations: Asset Theft & Loss report shows that in the past 12 months, 77% of organisations say a missing critical asset has resulted in a significant operational shutdown or delay. Moreover, asset shrinkage costs the average organisation without an asset tracking solution nearly £9.6 million annually, with smaller assets driving more than 70% of that cost.

The post Samsara Launches its Most Compact Asset Tag appeared first on Logistics Business.

At the International Robot Exhibition in Tokyo humanoids stole the show once again. Machines that walk, grip, and gesture like us have an undeniable magnetism, part science fiction promise, part genuine engineering marvel. Yet behind the spectacle, logistics leaders are asking whether these machines deliver demonstrable ROI, or if the industry is chasing a compelling idea that cannot yet scale.

Investment banks are certainly bullish. Morgan Stanley forecasts a global humanoid robot market worth $5 trillion by 2050, with deployment rates eventually reaching one machine for every ten humans. Those forecasts may well prove directionally right over decades. But logistics buyers don’t invest on 2050 narratives, they invest based on what can be deployed, integrated, and scaled in the next 12–24 months.

Innovation is only real when scaled

I’ve had countless conversations with CEOs in this industry who express frustration about being trapped in endless pilots and struggling to achieve meaningful traction. The pattern is familiar; exciting technology, impressive demonstrations, but no clear path to the kind of measurable, referenceable customer value that drives genuine adoption. What’s changed in warehouse automation is that customers are no longer rewarding novelty, they’re rewarding repeatable, referenceable outcomes delivered fast, in brownfield sites, under real volatility.

While there has become a hyperfocus on humanoids, most of the attention is driven by the fact that they generate a big reaction. We live in a world where reaction doesn’t equate to return on investment. Tim Tetzlaff, Global Head of Digital Transformation at DHL, captured this dynamic perfectly when he said: “Innovation is only real when scaled. Otherwise, it’s just a nice idea.” Too many robotics companies have compelling ideas but struggle to scale effectively, missing the chance to create meaningful customer impact. In practice, the winners in this cycle are the firms that scale through software-defined flexibility, not the ones chasing the most cinematic demo.

There’s a real risk that funding will dry up as ambitions collide with reality. Training robots through thousands of hours of simulation can produce impressive physical capabilities, but it grants them little genuine understanding of how the real world actually works. Warehouses are messy, stochastic environments: congestion, mixed Stock Keeping Units (SKUs), shifting priorities, human variability, and peak swings that don’t show up in lab conditions. Physical AI only becomes meaningful when systems learn from millions of real tasks in production. Purpose-built fleets do that every day, they don’t just learn how to move, they learn how the operation actually behaves. Purpose-built warehouse robots accumulate vast operational experience in the environments they are designed to serve. They know the warehouse floor because they have worked it.

The Gap Between Demo and Deployment

This gap between demonstration and deployment is the crux of the matter. Promotional videos may show humanoids performing acrobatic feats, but none can yet walk into an unfamiliar warehouse and reliably execute the complex, repetitive tasks that drive logistics operations. The most advanced humanoid models on the market today are still positioned as research platforms rather than production ready solutions. Production environments don’t just need a capable robot, they need an orchestration layer that can integrate with Warehouse Management Systems (WMS), Enterprise Resource Planning (ERP), and Manufacturing Execution Systems (MES), balance priorities in real time, and keep performance stable through peak periods.

As such, I expect 2026 to bring a wave of consolidation across the robotics sector, as companies locked into humanoid development face mounting pressure to demonstrate tangible commercial value. We’ll see the hype start to fade as customers and investors demand real world results, creating an environment where only the purpose built will survive.

The Opportunity in Front of Us

Here’s the reality that often gets lost in the humanoid excitement, we estimate that less than ten percent of warehouses globally have sufficient levels of automation today. The opportunity isn’t to build robots that look like humans. It’s to build the right solutions for the right tasks. That’s also why flexible automation is winning: operators want capability they can deploy in weeks, scale up or down, and reconfigure when volumes or product mix shift. In a world of uncertainty, adaptability is the real throughput advantage.

At Locus Robotics, we’ve moved beyond Person-to-Goods automation to define an entirely new category: Robots-to-Goods. Robots can now autonomously pick, move, and replenish inventory, performing tasks that previously required multiple human touches. But the hardware is only one piece of the puzzle. The real breakthrough comes from integrating Agentic AI with Physical AI to create systems that sense, decide, and act as one. The value isn’t one heroic robot, it’s a software-defined operation that keeps improving because it learns from the work. Warehouses become cohesive ecosystems rather than disconnected islands of automation.

The Financial Times suggests Japan, with its shrinking population and cultural openness to robotics, could become one of the first major democracies to experiment with widescale humanoid adoption. Perhaps. But for logistics leaders making investment decisions today, the question is not whether humanoid robots are impressive, they unquestionably are, but whether they can deliver the demonstrable, referenceable ROI that operations demand. Purpose built robotics already can and already do.

The post Humanoid Hype? Get Real appeared first on Logistics Business.

At the heart of Atomix’s solution is its ‘1+4’ technology platform. The ‘1’ refers to Atomixer, an AI-native orchestration software platform that enables real-time coordination of heterogeneous robotic fleets while integrating seamlessly with existing WMS/WCS systems.

The ‘4’ represents Atomix’s four families of self-developed robotics, including 4-way pallet shuttles, pallet AMRs, and tote Storage Transfer Robots and Tote AMRs, designed for high-density storage, flexible handling, and efficient picking. These technologies are modularly combined into three subsystem solutions — ‘Storage Mix’, ‘Handling Mix’, and ‘Picking Mix’ — allowing system integrators to configure scalable automation systems tailored to specific warehouse needs.

A key differentiator of Atomix is its ability to orchestrate heterogeneous robotic fleets within the same environment. Powered by advanced AI algorithms such as MAPF and decentralized deadlock avoidance, Atomixer enables seamless collaboration between different robot types and third-party equipment, optimizing warehouse operations in real time.

Demo Centre

Globally, Atomix works through a partner-based delivery model, providing core technologies and products while local system integrators deliver project implementation and lifecycle services. With over 500 projects across 20+ countries, Atomix has built long-term partnerships with companies including Coca-Cola, Nestlé, Toyota, Yum China, Lotte, ITW, and Lenovo, achieving a 71% customer renewal rate.

In Europe, Atomix continues to expand through local integrator partnerships. The company has recently opened a Demo & Experience Centre in Belgium to support partners and customers across the region. Recent projects delivered in Romania and Greece demonstrate the flexibility of Atomix solutions across industries such as manufacturing and cold-chain logistics.

Visitors to LogiMAT 2026 are invited to meet the Atomix team to explore AI-powered automation solutions and discuss partnership opportunities. If you would like a free visitor ticket you can register here.

The post AI-Driven Warehouse Automation at LogiMAT appeared first on Logistics Business.

OPO Oeschger is a Swiss family-owned company headquartered in Kloten, founded in 1926 and now employing around 300 people. With a range of more than 70,000 items, OPO Oeschger is one of the leading suppliers of fittings and components for carpenters, wood, glass, and metal construction, as well as for schools and resellers. In addition to furniture and kitchen fittings, the range also includes door and building fittings, machines, and tools. In its logistics, OPO Oeschger consistently relies on highly automated processes to deliver not only quickly but also reliably. Order picking is also being specifically developed with new technologies to meet increasing demands.

When standard solutions don’t help

Many processes in logistics at OPO Oeschger are already automated. Nevertheless, the goal was to identify new potential. There was a particular need for action in the area of order picking. Although an automated small parts warehouse was in place, items were still being picked manually. Since the warehouse building offers only limited space and the processes are precisely coordinated with the conveyor technology, the new solution had to be implementable without major interventions.

Adjustments to the conveyor technology or upstream control processes were out of the question. The solution had to integrate seamlessly into an existing workplace without imposing new processes. This is precisely where other providers who offered only standardized systems failed. OPO Oeschger, on the other hand, was specifically looking for a solution that would fit into the existing system and could realistically replicate the behavior of a human picker.

The robot picks what fits

Sereact impressed OPO Oeschger with its willingness to consistently adapt to the existing framework conditions. “The Sereact team came to our site, took a close look at our processes, and very quickly understood how they work,” explains Daniel Schütz, Operations Manager Logistics at OPO Oeschger.

“While other providers proposed standard solutions, we were able to implement individual requirements together with Sereact.”

The picking robot was integrated into an existing picking workstation. Instead of adapting the environment, the robot was designed to perform the tasks of a human employee as realistically as possible: it picks up target cartons and places them in two target locations. If requested to pick up a destination container, the robot first removes the anti-slip mat from the container. The robot then removes items from a source container and places them in the prepared destination carton or container. These are then sent on to the conveyor system for further processing.

A prerequisite for commissioning was a targeted adaptation of the interface in the warehouse management system, which was implemented without any problems in cooperation with TGW Logistics. This meant that only orders suitable for automated picking could be forwarded to the robot. The selection of suitable items is carried out directly by OPO in the item master. The actual picking and placement logic — i.e., the decision on how to place and stack items in the box—is entirely controlled by Sereact’s AI-supported control system. No product training was necessary. The AI solution takes care of product recognition, selection, and picking independently. The precision of the solution is particularly evident with items for which the cardboard packaging has been calculated exactly. Targeted preselection is crucial for the stability of the process.

An employee who doesn’t need a break

With the use of the picking robot, OPO Oeschger has taken an important step toward future-oriented logistics processes. The robot reliably performs standardized picking tasks that were previously covered by manual labour, thus creating a noticeable reduction in the daily workload. It works stably and reliably, especially with items that meet clearly defined criteria. Technically, the robot would be capable of significantly higher performance. At OPO, however, the speed was deliberately throttled in order not to overload the existing structural structure. In its current configuration, its performance is roughly equivalent to that of half a full-time employee. This deliberate limitation is part of a strategic approach.

OPO is using the project to learn specifically how AI-based robotics can be integrated into existing processes and what conditions need to be created for later scaling. At the same time, the expertise of the employees remains central: they now focus more on complex picking processes that involve handling flexible or sensitive products, for example. The combination of robot-assisted automation and human experience increases overall efficiency and process quality. Items that are not suitable for the robot are specifically excluded in the warehouse management system. For OPO Oeschger, the use of this technology was not a measure to reduce staff. Rather, the focus is on gaining knowledge. The aim is to further develop the system in a targeted manner and make it scalable.

What works today will continue to grow tomorrow

The experience gained from the project forms the basis for future automation projects at OPO Oeschger. The company is already working with Sereact and TGW Logistics to further develop the interface logic. The aim is to control even more precisely which items the robot can handle in the future, even for more complex orders with mixed items. In the medium term, the picking robot will interact intelligently with manual workstations and automate where it makes economic and procedural sense.

“We deliberately viewed the project as an investment in know-how,” explains Schütz. “We also wanted to learn at an early stage how picking robots can be meaningfully integrated into our processes, with a view to today’s operations and future logistics strategies.”

The aim is also to increase the utilization of the existing robot. The use of additional units is also planned for the future. Based on the knowledge gained, a scalable solution is to be developed that fits seamlessly into future infrastructures and consistently exploits the potential for automation. With the knowledge gained, OPO Oeschger and Sereact are working together to further optimize logistics processes in order to reap the full benefits of automation in the long term.

The post Order Picking with Intelligent Robotics appeared first on Logistics Business.

In today’s world, warfare, sanctions and climate instability are fracturing global supply chains and upending business plans with little warning. In fact, we must accept that volatility has shifted from being an exception to a defining feature of the operating environment.

In response, organisations are accelerating their adoption of AI, drawn by its promise to improve decision making and build resilience in an increasingly unstable world. Yet, as the enthusiasm for AI grows, so do the risks associated with how it’s being deployed.

Many businesses have already embraced early generative AI tools that operate alongside existing processes, though without fully embedding them. While these systems can speed up analysis, they often lack access to critical data and an understanding of wider business context, resulting in new forms of risk rather than increased protection.

Unlike earlier tools, agentic AI can not only analyse information but simultaneously take action, considerably expanding its potential impact. It also increases the consequences of getting it wrong, though.

When AI systems operate without full situational awareness or clear governance, the outcomes can be immediate and damaging, ranging from misdirected inventory and excess production to costly compliance failures.

This is a pivotal moment for AI adoption; agentic AI will play a central role in the future of supply chain decision making, but its success will depend less on the speed of adoption and more on how responsibly these systems are integrated in core processes.

As organisations begin to use AI to help them navigate disruption, they face a clear choice. On one side, generative AI tools and copilots are added onto existing processes, offering quick wins and impressive demonstrations. Yet because they sit outside of the workflows where decisions are made, they rely on fragmented data and produce outputs that lack context and accountability.

In complex supply chain environments, any shortcomings can escalate rapidly, with misaligned decisions leading to undermined trust and increased risk exposure.

On the opposite side, organisations can begin embedding intelligence directly into decision making workflows. At its most advanced, this involves agentic AI systems that operate on real-time data alongside the wider business context, allowing them to coordinate responses across the organisation.

When AI is embedded like this, organisations can move beyond reactive responses and gain the ability to anticipate disruption and act decisively before any issues can escalate.

With all this, maintaining human oversight and accountability when using AI systems should remain a design requirement. While there are concerns that AI might replace people, agentic systems will only deliver the most value when they are designed to work alongside humans.

People are and will remain responsible for the most important decisions. They define objectives, approve actions with significant impact and remain accountable for outcomes.

Within these outlines, autonomous agents can monitor signals, coordinate activity across functions and generate response options. As a result, human decision makers can then focus on areas where judgement and morals, as well as regulatory understanding, are crucial.

More importantly, embedding agentic AI into decision workflows enables oversight to be applied from the beginning. Unsafe or non-compliant actions can be prevented automatically, rather than identified after the fact. As regulators, particularly in the EU, place greater emphasis on transparency and explainability, this level of control is becoming increasingly necessary.

Supply chains are at risk due to a lack of systems that enable transparent, coordinated decision-making.
As uncertainty and instability continue to rise, advantage will come from adopting AI responsibly and embedding it into core decision processes with clear governance and human accountability.
Ultimately, trust is not the result of faster decisions. It is what makes them possible.

The post AI and the Future of Supply Chains appeared first on Logistics Business.

The AI-based simulator takes into account variables such as forecast demand in each region, transport costs and the operational capacity of each warehouse to test various inventory replenishment policies without affecting real-world operations. “The genetic algorithm enables multiple simulations to be run using different parameters until the most efficient logistics strategy is identified. Companies can compare scenarios and select the one that best fits their operations,” says Dr. Matthias Winkenbach, Director of Research at the MIT Center for Transportation & Logistics and the Intelligent Logistics Systems Lab.

Once data and variables are entered into the system, GENESIS generates the optimal solution along with advanced statistical dashboards. Users can analyse indicators such as consumption patterns, regions with high demand variability, SKUs with a greater risk of stockouts or warehouses experiencing supply issues.

Redistribute before purchasing

One of the system’s key features is its ability to rebalance inventory across warehouses. Instead of automatically placing new orders with suppliers, the tool analyses whether it is more efficient to transfer products from another facility within the network where excess inventory is available. In this way, companies can reduce costs and make better use of existing stock.

The system also recommends how to organise transport. For example, it suggests whether shipments should be consolidated to optimise truckloads or whether specific orders should be fulfilled from a particular location to reduce delivery times and costs.

“The real challenge wasn’t finding the right algorithm — it was making it fast enough to be practical. We developed GENESIS from the ground up to evaluate thousands of scenarios simultaneously rather than sequentially. What used to take days now takes minutes, which means companies can use it for real tactical planning, not just theoretical analysis,” says Rodrigo Hermosilla, Research Engineer at the MIT Intelligent Logistics Systems Lab.

Unlike analytical solutions reserved for specialised users, GENESIS is designed for both technical teams and business decision-makers. “The goal is to help companies minimise the total cost of their logistics network while ensuring the highest service level,” says Javier Carrillo, CEO of Mecalux.

Upcoming AI applications

The AI-powered simulator is one of the first tangible results of the joint initiative between Mecalux and MIT CTL. The collaboration is now entering a new phase focused on expanding the application of AI to other logistics processes, such as internal replenishment, digital twins in high-density automated storage systems, and slotting optimisation.

The MIT Center for Transportation & Logistics (MIT CTL) is a world leader in supply chain management research and education, with over 50 years of expertise. The centre’s work spans industry partnerships, cutting-edge research and driving supply chain innovation into practice through three pillars: research, outreach and education

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When Steve Jobs stepped onto the stage at the Macworld Expo in August 1997, he wasn’t introducing a groundbreaking new product (he would announce the iPhone at the same event a decade later).

At the time, software was rigid. Systems were siloed. Data arrived late. People worked within the confines of the technology, content to be limited by its many shortcomings. Jobs was casting a vision for his company and the customers who would refuse to settle for the status quo.

It was a rejection of the way people were being forced to work with technology, and a promise and an invitation to think different and change the world. The modern supply chain took shape at the same time, and its software solutions were built around batch planning, static forecasts, and point-in-time data.

These weren’t the ideal solutions. It was simply what the technology could support. For a long time, it worked. Disruptions existed, but they were exceptions, not norms.

Today, global supply chains are more expansive than ever before, operating with more velocity and precision but vulnerable to disruption. As one survey of 1,000 senior supply chain leaders concludes, ‘Supply chain disruptions are no longer rare — they’re the new normal.’

Why Two Decades of Technology Spending Left Supply Chains Brittle

Two decades and $200 billion in supply chain management technologies have left many supply chains reactive and convoluted. This staggering investment has not delivered the expected resilience; global disruptions now cost the average company 8% of its annual revenue. McKinsey & Company estimates that extended supply chain disruptions lasting more than a month now occur every 3.7 years and can cost a business up to 45% of a year’s profit over a decade.

Despite this significant spending, most organizations are still operating on their heels, trapped in a cycle of:

● Making decisions based on fixed time horizons that ignore the fluidity of global trade
● Relying on data that is outdated by the time it reaches the dashboard
● Operating in silos, where teams are neither connected nor informed
● Reacting to crises rather than adapting to trends.

First-wave supply chain management solutions were designed to record and report, not to decide. They rely on fixed time horizons and historical data to inform the future. When disruption, uncertainty, and change are the norm, it’s clear that we need to think differently about our supply chain software.

Transitioning from Reactive Networks to Adaptive Decision Engines

Decision-making itself has become a first-class enterprise capability. It’s why a decision-centric approach is the defining framework of successful, agile enterprises.

Yes, it involves a new technology schema. Yes, it puts data at the centre of everything. It’s also more than that. It’s a new operating model where decisions are explicit, intelligence is continuous and adaptive, execution is connected, and humans and technology collaborate at scale.

Decision-centric organizations are not just focused on data collection, but also on applying this information to drive specific business outcomes. For supply chain entities, this means using available intelligence and analytical tools to become more forward-looking and responsive to market shifts before they become crises. These initiatives are undoubtedly powered by artificial intelligence (AI).

Making Intelligence Operational

AI is ubiquitous in the supply chain sector. A quick Google search reveals countless think pieces on the subject, and executives are eager to talk about how they are deploying the latest to achieve the elusive promise of total visibility.

What it actually does for them is a different story. AI-powered, decision-centric supply chains are defined by three pillars that produce real results.

1: Centralizing Data
Best-in-class supply chain entities are centralizing their data into a single, unified platform. AI-powered supply chain optimization doesn’t work if data silos and disparate teams are running the show. Integrate and unify data so AI models can train on a complete, vertical, end-to-end picture of the operation, rather than on conflicting or incomplete datasets.

2: Intelligent Responses
Decision-centric companies turn insights into action. They rely on clean, centralized information to identify problem root causes and respond in real time. Even better, generative AI solutions make information searchable, allowing decision-makers to query data to derive actionable insights, and machine learning helps teams arrive at complex, data-driven decisions.

3: Predictive Sales and Operations Planning
AI-driven demand sensing turns real-time data from the external world into insights that anticipate and understand subtle shifts in customer behaviour, market trends, and potential disruptions before they impact the bottom line.

Rather than relying on last year’s information, supply chain entities can use this technology to adapt to real-time, even unprecedented, circumstances, responding with robust solutions that clarify uncertainty and create opportunities from disruption.

For instance, 76% of fashion executives believe tariffs and trade volatility will be the defining issues of 2026, requiring this heightened level of agility. Generative AI-powered digital twins can help retailers understand the financial or operational implications of any given decision or scenario.

This AI-first approach connects planning, execution, and analytics in real time to deliver speed, resilience, and measurable business impact. When implemented effectively, it changes how supply chains work, converting reactive networks into adaptive decision engines.

A New Era of Strategic Advantage

When Steve Jobs challenged Apple and its audience to ‘think different’ he was redefining the relationship between creators and their tools, businesses and their processes and potential. It was a response to a status quo that desperately needed updating.

The logistics and supply chain sector is ready for a similar revolution. Specifically, the modern supply chain must be built to be actively anti-fragile. The transition to a decision-centric enterprise marks the end of an era defined by reactive management.

For decades, we required supply chain professionals to serve the limitations of their software. We’ve left expert planners firefighting exceptions in spreadsheets, while reaching the company’s strategic goals have remained elusive.

Adopting a decision-centric model changes this dynamic. It empowers people and their teams to think differently. They can be different, operating with a level of specificity and agility that meets this disruptive moment.

The post Broken Supply Chain? appeared first on Logistics Business.

Freight forwarders and carriers alike are investing heavily in AI programs, but most solutions remain bolt-on tools that sit disconnected from the most relevant knowledge source: structured data. The result is a fragmented technology landscape where AI promises efficiency but delivers complexity and does not progress beyond the pilot phase. cargo.one’s multimodal AI-native operating system addresses these challenges with a unified approach where agentic workflows and operational data exist natively in a single system.

The acquisition of Cargofive, which closed on February 25th, fundamentally expands cargo.one‘s rate data foundation by adding connections to the top 10 ocean carriers and scalable ocean rate data ingestion and management capabilities. Cargofive offers a full spectrum of ocean rates spanning four million trade lanes and is trusted by hundreds of forwarders globally. cargo.one can now enable freight forwarders to automate air and ocean workflows from a single platform, rather than managing fragmented tools.

As an AI partner, cargo.one combines technology quality, fully integrated rate data, and in-house logistics expertise. cargo.one’s AI-native operating system equips logistics companies to deploy ready-made AI agents or build custom ones using open protocols like MCP servers. Built on comprehensive multimodal rate data, the infrastructure includes RAG-based knowledge retrieval and supervision layers that monitor AI outputs to ensure accuracy and reliability.

Unlike bolt-on AI tools that require integration with separate systems and third-party data, cargo.one’s workflows operate natively within the same platform. Humans and AI work side by side using the same data, ensuring teams maintain full control while automation handles repetitive tasks.

“Most AI projects in logistics fail to deliver ROI because they lack access to robust, structured data,” says Moritz Claussen, Founder and Co-CEO of cargo.one. “Real returns come from unified data infrastructure operating at enterprise scale. With Cargofive, we’re expanding the foundation already embedded inside many of the world’s top forwarders’ operations to encompass ocean needs, and we are delivering what makes AI actually work in production.”

Sebastian Cazajus, Founder & CEO of Cargofive, added:

Across the industry, forwarders are asking for integrated air and ocean solutions that eliminate data silos. cargo.one has already set the standard in air. Together, we are bringing that same quality and scale to ocean freight, creating a truly multimodal operating foundation to enable agentic workflows.

“Data and AI are inseparable – quality data is the foundation for quality AI,” says Stefan Borggreve, Member of the Management Board at Hellmann Worldwide Logistics. “cargo.one has built a comprehensive operating system that our teams trust. When AI workflows operate using the same reliable data our people use daily, we can confidently deploy automation and focus on delivering the best customer experiences.”

When evaluating AI partners, logistics leaders should look beyond individual features to the underlying foundation… Features become commoditized quickly; what matters is having a partner with comprehensive data infrastructure and industry-specific expertise that can evolve with your needs.

says Bob Goodman, Partner at Bessemer Venture Partners.

cargo.one’s AI-native operating system is available now, enabling freight forwarders and carriers to run agentic workflows, including those for rate management, quoting, booking, and customer support, using consistent data and under their teams’ full control. The first customers have already been onboarded to its ocean rate management and quoting solution, with cargo.one’s wider customer base to benefit in the coming weeks.

The post cargo.one Acquires Ocean Platform Cargofive appeared first on Logistics Business.

Don Mabry, SVP Global Trade Solutions:

“Supply-chain transparency is rapidly shifting from a competitive advantage to a regulatory expectation. What was once considered best practice is becoming the minimum standard, as regulators extend their focus beyond border clearance to the full lifecycle of goods. Increasingly, compliance is judged not by how quickly shipments move, but by how confidently organisations can explain where products came from, how they were sourced, what they cost, and why those claims can be trusted.

“This shift is exposing a structural weakness across global trade operations. Many trade processes were built to optimise throughput, not withstand audit-grade scrutiny. Paper documentation, email chains, and spreadsheets may still move goods, but they cannot reliably support multi-tier supplier visibility, cost validation, or origin proof at scale. As enforcement intensifies, the inability to produce accurate, timely, and traceable data is no longer an operational inconvenience – it is a compliance risk.

“At the same time, the physical dynamics of trade are changing. In-transit inventory is on track to surpass on-hand stock in value, effectively turning the ocean into the world’s largest warehouse. Longer transit times, buffer-stock strategies, and geopolitical volatility mean more working capital is tied up between origin and destination. Yet visibility often disappears once goods leave the factory or port, leaving organisations exposed during the most financially significant phase of the journey.

“The implication is clear. Future-ready trade operations will be defined by less-touch execution, automation by default, and data that can be verified rather than inferred. Compliance must be designed into trade processes, not reconstructed after the fact. The ability to prove origin, validate costs, model tariff exposure, and demonstrate compliance on demand will matter as much as speed or service levels.
“Ultimately, the organisations that succeed will not be those that move goods the fastest, but those that can explain – clearly and confidently – how their supply chains stand up to scrutiny. In a transparency-first world, velocity without visibility is no longer an advantage. It is a liability.”

Richard Stewart, EVP, Product & Industry Strategy:

“In 2026 and beyond, logistics will move decisively into a new era of precision and autonomy – powered by artificial intelligence. The question will no longer be whether AI has a role to play, but how deeply it can be embedded into everyday operations and decision-making.

“Clearly defined use cases will emerge as intelligent systems anticipate needs, optimize workflows, and manage complexity quietly in the background. Humans will remain in the loop – where their insight or approval truly adds value. This collaborative model between people and technology will make problem-solving faster, more accurate, and less reactive.

“As AI maturity deepens, pre-built solutions will evolve into configurable platforms that allow organizations to shape bespoke, AI-enabled operations tailored to their unique challenges. Each proven use case will spark new ideas and innovations, as customers begin to imagine and create what’s next.

“The most forward-thinking companies won’t just adopt AI – they’ll design it. And as this transformation continues to unfold, logistics will look increasingly intelligent, resilient, and self-optimizing: systems that learn continuously, adapt seamlessly, and empower humans to focus on higher-value, strategic work.”

Omar Akilah, SVP of Product Strategy:

“In 2026, modular supply chain execution will finally overtake the traditional monolithic platform. The era of 18–36-month implementations is ending, replaced by composable, fast-to-value architectures that let companies plug in capabilities exactly where they’re needed. Instead of ripping out entire stacks, organizations will fix specific pain points with targeted modules that deliver immediate ROI, eliminate shelfware and dramatically reduce transformation risk. Digital transformation will shift from a one-off overhaul to an ongoing operating rhythm – enabling supply chains to adapt faster, innovate with confidence and evolve continuously in real time.”

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Artificial Intelligence, Machine Learning, Generative AI – the buzz phrases keep coming. “That’s the world of today, concepts are developed so quickly,” smiles Hervé (pictured, below), Senior Director, Technical and Support Services at supply chain technology leaders Manhattan Associates.

The latest term is Agentic AI. Let’s get straight to the point – what is it?

“If we look at AI in its original definition, for several decades it was about the ability to understand complex algorithms,” he begins. “We then developed IT systems able to make predictions on a very high number of data sets and then even improve those data sets, which we can call ‘Machine Learning’. Then two years ago, Chat GPT arrived along with the phrase ‘Generative AI’, which I would describe as the capacity to make sense of content – whether text, music, sounds, or pictures – and also create this type of content. When you can make sense of language you can begin to ‘push’ these systems to execute tasks for you.

AI Agents take this a stage further. They are geared towards actually achieving a specific goal, rather than simply making a response.

Autonomous capability

A key breakthrough is autonomy, he says.

“Operationally, AI Agents are empowered to make decisions and act on those decisions. They also have the ability to interface with the user in normal language. Agents take the instructions in natural language and show the decisions made and steps taken in natural language. Remember, it has an ultimate goal and is able to derive the steps it should follow to reach that goal autonomously.”

As a layman, like many others I’m as nervous of AI’s much-feared potential for chaos as I’m dazzled by its transformationally positive capabilities. Does the autonomy of Agentic AI not make it more likely that repetitive mistakes might become wired into the system?

“Good question, but just like every system it needs to be tuned and optimised,” says Raphaël Hervé. “Let me turn it around. When a complex IT system NOT based on generative AI, or not trained to operate autonomously, makes an error, it is actually very hard to understand why. Because you have to debug, analyse, go into source code. With an agent, you just need to tell it, ‘I think that’s wrong. Why did you say that?’ And the agent will say, ‘I did this for this reason’ and it is therefore far easier to derive the source of the anomaly. Agentic AI is a lot more dynamic in terms of fine tuning than was possible in the past. And unlike your dog or your child it will not resist your instructions,” he adds.

The clarity of AI Agents in explaining what steps they take and why they are taking them is reassuring. “They are very efficient in making adjustments, should you need to,” he promises.

There are several logistics contexts in the Manhattan Associates portfolio of solutions.

An examples is Labor Agent, which is not actually a fully 100% autonomous agent that reaches a goal on its own. Think of it more as an assistant to manage your labour efficiency.

“But it can autonomously sift data, analyse, and procure advice on your labour optimisation,” Raphaël explains. “A typical use case might be a warehouse manager asking Labor Agent if that day’s packing deadlines are likely to be met in terms of human resource. If Labor Agent replies that the team is likely to be late because three people are lacking, it can explore the opportunity to take capacity from elsewhere, for instance from Picking. That team might be able to supply up to five people, so Labor Agent might perhaps select the top three resources with highest ratings and performance on packing. It can then message all parties and reassign via text. The agent is working autonomously and speaking to the user in natural, normal language.”

Time-saving benefits

The question all warehouse managers – and finance leaders – will want answered is, how will we see the benefits manifested in day-to-day use?

One precious win is time, invaluable in any warehouse context.

The example we just gave is perhaps a 30-second conversation via text, which would have been 15-20 minutes in the past. If packing is late because it lacks three people, it is a complex calculation without the assistance of Labor Agent. You’re looking at process, schedules, performance of packers. There can be big variables, which you then need to compare with what you’re expecting to achieve. The agent can do this for you in seconds.

Manhattan’s transportation portfolio offers AI Agents with similar benefits.

“Our Freight Invoice Agent is capable of picking up any form of document – PDF, email, spreadsheet – used as carrier invoice materials, and will automatically reconcile actuals with the expected cost of that shipment. This is a role traditionally carried out by manual resources, who spend time receiving documents, comparing screens, shipping costs, taxes, driver hours, and it’s a process that can eat up 15 minutes per invoice. We have built an agent that will automatically ingest anything that comes up, recognise the shipment, align it with expectations, and explain any anomalies. What used to take multiple people hours a day is managed in a few moments.”

Manhattan formally released AI Agents in January of this year, and are marketing the technology to all customer segments, large and small. It even includes Agent Foundry, a developer workspace for customers to build their own agents to their own specifics, either from templates or from scratch.

Raphaël Hervé is brimming with confidence about the prospects.

We believe Agenti AI is very powerful in terms of productivity gains for our customers. It will drastically improve human-machine interactions, and it will make access to data and functions faster and easier. Customers will enjoy acceleration in project implementation, because integration, mapping, and development are all so much faster.

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