Managed AI for Logistics: How a new category is reorganizing intralogistics

Logistics in the tension between costs, complexity and volatility

Logistics has historically been caught in the middle: it is simultaneously a cost center, a service provider, and a strategic lever. In recent years, however, the framework conditions have drastically worsened. Energy prices in Europe are sometimes two to four times higher than in the USA or Asia, which puts massive margin pressure on energy-intensive industrial and logistics locations in particular. At the same time, overall logistics costs are rising significantly, driven by higher transport costs, wages, energy, land costs, and automation expenses.

At the same time, the industry is struggling with a structural labor shortage: massive bottlenecks in the transport and warehousing sectors are being observed in Europe; studies show that around three-quarters of the logistics operators surveyed are suffering from staff shortages, a significant proportion of whom report severe shortages. While demand from e-commerce, omnichannel retail, pharmaceuticals, automotive battery logistics, and other high-growth sectors continues to rise, it is proving extremely difficult to attract and retain sufficient qualified personnel.

At the same time, technical complexity is increasing. The market for warehouse automation is growing at double-digit annual rates; estimates predict a volume of over US$55 billion by 2030 and global growth of around 15 to almost 19 percent per year. The market for intralogistics automation solutions is already valued at over US$20 billion and is also growing significantly, driven by e-commerce, higher service demands, and limited space.

The use of AI along the logistics chain is developing even more dynamically. The global market for AI in logistics was in the high single-digit to double-digit billion range in the mid-2020s and is expected to grow to several hundred billion US dollars by the early to mid-2030s, with annual growth rates exceeding 40 percent. A similar trend is expected for AI in warehousing: here, too, double-digit billion-dollar markets and growth rates well over 20 percent are anticipated.

The result is a tension: Logistics managers are investing in automation, robotics, and software, but at the same time grappling with enormous volatility in demand, capacity, energy costs, and personnel. Managing these highly networked, increasingly automated systems with traditional IT and organizational approaches is reaching its limits. This is precisely where the idea for a new product and solution category comes in: Logistics Managed AI.

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From Industrial Managed AI to Logistics Managed AI: Why logistics needs its own approach

In recent years, the concept of Managed AI, or Industrial Managed AI, has become established in the enterprise environment. This refers to platforms and services that deliver AI not just as a model or standalone solution, but as a fully managed system: from data integration and model development through operation, monitoring, and governance to security and compliance. In industry, Industrial AI services primarily address topics such as predictive maintenance, process optimization, energy efficiency, and quality control.

These concepts are valuable, but mostly remain generic or heavily focused on production processes. In logistics – especially in intralogistics with high-bay warehouses, automated small parts storage, shuttle systems, conveyor technology, and robotics – the requirements are fundamentally different:

Firstly, logistics is much more critical in real time. Delayed or incorrect decisions in warehouse or transport management have a direct and visible impact on service levels, delivery times, and customer satisfaction.

Secondly, many logistics processes are highly stochastic: Irregular goods receipts, volatile orders, short-term promotions, seasonal peaks, failures of transport capacities or sudden disruptions in the network can only be represented to a limited extent using classic planning models on a weekly or monthly basis.

Thirdly, logistics systems operate within a tightly integrated ecosystem of WMS, TMS, ERP, robotic controllers, IoT sensors, carrier platforms, platform traders, and customer systems. The logic is distributed across numerous technical and organizational interfaces.

While a generic managed AI offering may provide the technical foundations (data platform, MLOps, governance), it rarely addresses the fine-grained logistical orchestration tasks that need to be solved every minute. Therefore, logistics doesn't just need "AI," but its own domain-specific category: Logistics Managed AI – a managed AI layer specifically designed for intralogistics and logistics processes.

What is Logistics Managed AI?

Logistics Managed AI can be described as an independent product and solution category that merges three levels:

• Firstly, a logistics-specific, domain-oriented data and integration layer that connects operational systems (WMS, TMS, ERP, robotics controllers, sensors, carrier interfaces) in real time and understands them semantically.
• Secondly, a collection of predefined, customizable AI building blocks for typical logistics decision domains: inventory optimization, slotting, workforce planning, order release, wave formation, routing, carrier selection, dynamic service level control, risk and resilience models.
• Thirdly, a managed operations and governance model that provides these AI building blocks as a continuous service: with SLAs, 24/7 operation, monitoring, continuous retraining, regulatory compliance, documentation, and a clear framework for human intervention and approvals.

Unlike traditional WMS or TMS systems, Logistics Managed AI is not primarily a transactional system that manages and "processes" orders. Rather, it is the overarching, learning decision layer that controls, coordinates, and continuously optimizes the behavior of these systems in real time – embedded within a managed service model.

Unlike generic enterprise or industrial managed AI solutions, Logistics Managed AI is radically tailored to logistics processes. The pre-built use cases, data models, and decision patterns are designed to be directly integrated into warehousing and transportation processes, rather than requiring abstract definition at the enterprise level.

Economic rationale: Why a separate category makes business sense

The question of whether a new product category makes sense is ultimately always an economic one: Can a structural added value be generated with an independent, clearly defined category that would otherwise be unattainable or only achievable at high opportunity costs?

In the case of Logistics Managed AI, several macroeconomic and microeconomic factors support this.

At a macro level, the relevant markets are growing rapidly and simultaneously approaching a level of maturity that transcends individual solutions. The market for AI in logistics and warehouse management is growing at annual rates well above 20 percent, in some areas even exceeding 40 percent. The intralogistics and warehouse automation markets will reach tens of billions of US dollars by 2030/2034. At the same time, the adoption of robotics is increasing rapidly: estimates suggest that by 2025, around half of all large warehouses will be using some form of robotics.

This dynamic creates a new layer of complexity: the more systems, sensors, robots and cloud services are integrated, the greater the need for a coordinating, domain-specific "intelligence" that not only optimizes in specific areas but orchestrates holistically.

At the micro level, companies are increasingly grappling with the question of how to simultaneously achieve operational excellence, resilience, and cost efficiency. Studies show that AI-supported warehouse processes can enable inventory accuracy approaching 99 percent, significant reductions in storage and personnel costs, and substantial shortening of lead times. At the same time, however, fixed costs for space, automation technology, and IT are also rising. The economic logic is shifting: those already bearing high fixed costs need the highest possible utilization of equipment and processes to amortize these costs.

Logistics Managed AI addresses this economic logic by not just delivering isolated efficiency gains, but by dynamically and data-drivenly utilizing all available capacity – warehouses, technology, people, transport network. The added value lies not only in percentage points of cost reduction, but in a structural improvement in capital efficiency, resilience, and predictability.

Storyline: A typical mid-sized company owner faces a decision.

To make the need for Logistics Managed AI tangible, a narrative perspective is helpful. Let's imagine a typical Central European medium-sized company, such as an automotive or mechanical engineering supplier with a large high-bay warehouse, a rapidly growing e-commerce subsidiary for spare parts, and several regional distribution centers.

In recent years, the company has invested heavily: an automated high-bay warehouse with thousands of pallet spaces, an automated small parts warehouse (AS/RS) with a shuttle system, new conveyor technology, autonomous mobile robots for internal transport, a modern warehouse management system (WMS), a transport management system (TMS) for route planning, and various interfaces to customer and supplier systems. The investments were justified by the promise of personnel savings and increased space efficiency, as well as the ability to respond more flexibly to customer needs.

The reality in the field is considerably more contradictory. On peak days, such as at the end of the quarter or before seasonal peaks, certain areas of the warehouse reach their limits, while others remain underutilized. Despite all planning, staff shifts are often not optimally staffed because short-term sick leave and unexpected order surges disrupt the plans. Some shuttle systems are running at capacity, while other aisles remain relatively quiet.

Added to this are external shocks: a suddenly delayed shipping container, a short-term bottleneck in transport capacity, energy-cost-related restrictions on night shifts, or reduced operating times in refrigerated areas. Each of these disruptions requires quick, sound decisions – decisions that are often still made ad hoc based on experience, gut feeling, and Excel analyses.

At the same time, the company has launched its first AI projects: a demand forecasting solution, a pilot project for dynamic inventory optimization, and a routing optimizer within the TMS. However, these initiatives are scattered across different departments, utilize different databases, and are managed by different service providers. The result: a patchwork of AI islands that delivers promising results on a small scale, but no comprehensive transformation on a large scale.

This is precisely where Logistics Managed AI would come in: not as another tool, but as a managed, overarching intelligence layer that orchestrates existing assets instead of creating new silo islands.

Architectural concept: From individual solutions to an orchestrated AI layer

Technically and conceptually, Logistics Managed AI can be understood as a layer between the operational systems and corporate management.

At the lower end are the transactional systems and physical assets: WMS, TMS, ERP, robot controllers, conveyor technology, IoT sensors, carrier platforms, yard management, control centers. These systems generate and consume events at a high frequency: order creation, goods receipts, picking orders, transport orders, changes in system status, fault messages, and GPS positions of vehicles.

At the top end are the classic management and planning tools: S&OP processes, budget and investment planning, network design, location and layout decisions, strategic supplier and carrier selection.

Many companies have a gap in this area: They have operational control centers, but hardly any consistently unified decision-making layer that learns, recommends, optimizes, and intervenes across all logistical sub-areas. This is where Logistics Managed AI comes in.

The architecture typically comprises four core elements:

• First, a logistics-specific data and event platform that harmonizes and enriches operational data in near real-time and translates it into semantically understandable objects. The system must know what an order, a position, a storage location, a route, a slot, or a resource is – not just technically, but also from a business perspective.
• Secondly, a library of AI agents and models, each responsible for specific decision domains: forecasting, optimization, classification, and generation models, combined with rule-based and heuristic logics. These agents do not operate in isolation but are interconnected in an orchestration layer.
• Thirdly, an interaction and control layer that allows human dispatchers, control room staff and management to interact with this AI layer: granting approvals, simulating scenarios, setting guardrails, changing priorities, defining exceptions.
• Fourthly, an operational and governance framework that ensures ongoing operation, monitoring, model maintenance, compliance with regulatory requirements (such as AI regulation, data protection, labor law, product liability) and documentation.

The key feature of a Logistics-Managed-AI approach is that this architecture is not only designed, but also delivered and operated as a service from a single source – with clear responsibilities, SLAs and economic indicators.

Typical application areas in intralogistics

In high-bay warehouses and other intralogistics environments, numerous opportunities arise for Logistics Managed AI.

A key use case is dynamic order release and wave formation. Instead of grouping orders according to rigid rules – such as cut-off times or destination regions – an AI layer can continuously decide which orders are fed into the system, when, and in what combination, in order to avoid bottlenecks, minimize lead times, and optimize the utilization of available resources. This process incorporates forecasts of incoming orders, current system states, personnel scheduling, and transport slots.

A second use case involves slotting, i.e., the distribution of items to storage locations. AI-supported methods can dynamically place items where they can be picked with minimal effort, taking into account volume trends, seasonal patterns, return flows, and physical constraints. Studies show that intelligent slotting and inventory strategies can deliver measurable efficiency and cost benefits.

A third area is the management of personnel deployment and shift planning. Given the labor shortage in warehousing and transportation, it is economically crucial to utilize available employees optimally. Logistics Managed AI can translate forecasts of order volumes and process load into concrete shift models, identify overtime requirements early on, and simulate alternative scenarios (for example: How many orders can be processed with a given number of employees and at what service level?).

Fourth, the deep integration of robotics and AI opens up new potential. Autonomous mobile robots, shuttle systems, and robotic picking solutions generate large amounts of data that can be used for predictive maintenance, path optimization, bottleneck management, and collaboration with humans. Logistics Managed AI can act as a "brain" that coordinates different robotic systems, prioritizes their deployments, and balances safety, efficiency, and ergonomic criteria.

Finally, linking intralogistics and transport logistics via a shared AI layer enables end-to-end optimization from goods receipt to delivery. This allows cut-off times, packing strategies, and loading plans to be dynamically adjusted to carrier availability, traffic forecasts, and cost trends.

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Application areas in transport and network logistics

Even outside of warehousing, a Logistics-Managed AI category offers diverse fields of application. In transport logistics, the volatility of demand and capacity has increased significantly in recent years; freight prices fluctuate drastically, and disruptions due to weather events, geopolitical tensions, or capacity bottlenecks have become more frequent.

A logistics-specific managed AI layer can function as an "agent ecosystem" that balances transport orders, available capacities, external market data (spot rates, tolls, fuel costs), and service level commitments in real time. Agents can, for example, plan alternative routes, dynamically reallocate carrier mixes, identify backhauls, or recognize consolidation opportunities and directly submit suggestions to the TMS or dispatchers.

In interconnected logistics networks – such as those of large 3PLs, parcel service providers, or networks of spare parts distribution centers – Logistics Managed AI can help smooth flows, shift peaks, and optimize resources network-wide rather than location-specifically. This also includes strategic questions: Which orders are picked in which distribution center? Where is cross-docking worthwhile? What inventory levels should be maintained in which regions to buffer volatility without unnecessarily tying up capital?

In multimodal networks, AI can also consider operating and transfer times, train schedules, terminal capacities, and road traffic in a joint optimization process. Given increasing sustainability requirements and CO₂ pricing, the decision-making layer can explicitly incorporate emission costs into the optimization, thus linking cost and climate policy objectives.

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Business models: How Logistics Managed AI can be offered and priced

For Logistics Managed AI to become economically viable as a product category, clear business models are needed. Three approaches are obvious.

A platform-centric approach delivers a standardized, cloud-based logistics-managed AI platform with pre-built connectors, data models, and use cases. Customers license usage based on users, warehouse locations, transaction volume, or a combination thereof. Additional value-added services—such as model customization, consulting, and change management—are priced separately.

A service-centric approach positions Logistics Managed AI as an ongoing managed service, where a service provider assumes responsibility for operation, continuous optimization, and reporting. Compensation here can be more results-oriented, for example, through efficiency gains, cost savings, or improved service levels. However, this requires a clear baseline definition and transparent key performance indicators (KPIs).

A hybrid approach combines platform and service elements: The technical basis is provided as a standardized platform, while selected customer modules run as an individually managed service – for example, in the case of particularly critical locations or networks.

From an economic perspective, a partially outcome-based approach is particularly interesting, as it better aligns the incentives of both provider and customer. Providers who deeply integrate their AI systems into their operations generally have more leverage to achieve tangible improvements in results and can demonstrate these to the customer.

Differentiation: How Logistics Managed AI differs from WMS, TMS and generic Managed AI

A new category only makes sense if it can be clearly distinguished from existing categories.

Logistics Managed AI differs from a WMS in that it doesn't primarily manage transactions, but rather makes decisions. A WMS knows which orders exist, which storage locations are occupied, and which resources are available; it is the executing instance. Logistics Managed AI, on the other hand, decides which orders should be released and when, how they should be bundled, where they should be routed, and how resources should be deployed – and learns from the results.

Logistics Managed AI differs from a TMS in a similar way: A TMS creates routes, manages shipments, and communicates with carriers. Logistics Managed AI determines when which orders are assigned to which route, which carriers should be used and in what mix, how service levels are optimized from a cost perspective, and how external disruptions can be best mitigated.

Logistics Managed AI differs from generic enterprise or industrial managed AI offerings through its domain-specific models, ontologies, and use cases. While generic platforms primarily provide infrastructure, tools, and governance, Logistics Managed AI additionally delivers ready-made intelligence modules tailored to logistics and an understanding of logistics-specific key performance indicators, conflicting objectives, and processes.

This distinction makes it clear: Logistics Managed AI is not a competitor to WMS/TMS or Industrial AI platforms, but rather a missing layer in between and above them – an interpreting, learning, coordinating layer that generates real, continuously managed added value from data and systems.

Drivers of demand: Cost, risk, service, regulation

The demand for such a category is driven not only by technological possibilities, but primarily by business necessities.

Cost and margin pressure is a key driver. Rising energy prices, wages, and the costs of space and materials are putting logistics and industrial companies under immense pressure. Those who have invested in expensive automation must maximize the utilization of these assets and minimize planning errors. Logistics Managed AI addresses precisely this optimization challenge.

Risk management and resilience are increasingly coming into focus due to crises, geopolitical tensions, and the growing frequency of extreme weather events. Traditional S&OP cycles and static contingency plans are insufficient to manage highly volatile situations in real time. A managed, AI-powered decision layer can help by identifying disruptions early, calculating alternative scenarios, and providing actionable recommendations.

Service expectations continue to rise. E-commerce customers have become accustomed to fast and predictable deliveries; B2B customers increasingly expect similar transparency and responsiveness. Those who not only react but proactively manage these processes will differentiate themselves in the market.

Regulation and governance are also gaining in importance. Energy and emissions regulations, due diligence obligations in supply chains, security requirements in warehousing and transport processes, data protection, and emerging AI regulations place high demands on transparency and control. A structured, managed approach to AI in logistics is becoming a prerequisite for ensuring compliance, limiting liability risks, and building trust with customers and regulatory authorities.

Hurdles and risks: Why Logistics Managed AI won't catch on by itself

However convincing the economic logic may seem, the path to establishing Logistics Managed AI as a category is fraught with obstacles.

Technically, many logistics systems have evolved organically over time and are highly fragmented. Different WMS versions, in-house developed tools, legacy interfaces, and proprietary robot controllers complicate integration. Without a clear roadmap for data and system harmonization, every managed AI project risks failing due to complexity.

Organizationally, roles and responsibilities are often unclear. Who ultimately decides: the control center, the AI, central supply chain management, or IT? How are conflicting objectives between costs, service, inventory, and sustainability goals resolved? Without clearly defined governance, there is a risk that an AI layer, while technically functional, will be blocked or ignored in daily operations.

Culturally, the transition from a strongly experience- and heuristic-driven management model to a data- and AI-driven model is challenging. Many dispatchers and warehouse managers possess enormous experience and local optimization expertise; this needs to be leveraged rather than overridden by algorithms. A managed AI approach must consciously emphasize collaboration between humans and machines.

Finally, there is the risk of vendor lock-in. Outsourcing the control logic of logistics to an externally managed AI service largely ties companies to its technology and data model. Open interfaces, model and data portability, and a clear exit plan become strategic criteria when selecting a vendor.

Implementation scenarios: How companies can gradually adopt Logistics Managed AI

Against this background, a gradual, focused approach makes sense. A typical path could begin with a clearly defined, narrowly limited use case that can be measured quickly: for example, dynamic wave formation in an e-commerce warehouse, AI-supported workforce planning in a highly fluctuating distribution center, or agent-based carrier and route optimization on selected routes.

It is important to consider the managed dimension from the outset: not just to develop a model and roll it out once, but to define ongoing operation, monitoring, retraining, adaptation to process changes, and governance. This allows companies to learn on a small scale what it means to partially delegate logistics decisions to a managed AI layer.

In the next step, further use cases can be added, ideally those that build on the same data and integration foundation: inventory optimization, slotting, inbound on-time delivery, and prioritization of orders by service level and margin. This gradually creates an ecosystem of AI agents that is initially limited to a local area (e.g., a single warehouse) but can later be scaled network-wide.

At a higher level of maturity, Logistics Managed AI can also be integrated into strategic planning and decision-making processes: network design, location decisions, investment planning for automation, and negotiations with carriers. The same data and decision-making foundation used operationally then also feeds into strategic scenarios.

Perspective for providers: Who can credibly fill the Logistics Managed AI market?

From a provider's perspective, the Logistics Managed AI category opens up new positioning opportunities. Several player groups are worth considering.

Providers of WMS, TMS, and warehouse automation systems possess deep domain knowledge and access to operational data. They can extend their existing systems with an AI and orchestration layer and offer this as a managed service. Crucially, they should not limit themselves to their own ecosystem but remain open to third-party integrations to enable true end-to-end orchestration.

Cloud and enterprise AI platform providers bring strong capabilities in data management, MLOps, scaling, and security. They can build logistics-specific solutions on their generic platforms, but should work closely with logistics and intralogistics specialists to achieve the necessary depth of understanding in processes and key performance indicators.

Specialized consulting and integration firms with a logistics focus can play a bridging role: They understand processes, systems and organizations and can develop individual Logistics-Managed-AI roadmaps that combine technology, organization and governance.

Finally, new players will emerge, operating from the outset as logistics-managed AI platform or service providers. They will attempt to establish integrated, cloud-native, agent-based solutions that connect to existing WMS/TMS/ERP/robotics landscapes via standardized connectors.

In the long term, the market will likely see hybrid forms: larger platforms that provide basic AI and data functions, and specialized Logistics-Managed-AI solutions built on top of these, which connect via APIs and domain models.

Long-term vision: From managed warehouse to self-optimizing logistics chain

As Logistics Managed AI establishes itself as a category, the target images for logistics organizations will also change.

As a first step, warehouses and networks are being "AI-supported": Dispatchers and control centers use recommendations, simulations, and forecasts, but ultimately remain the decision-makers. The system explains its suggestions, quantifies their effects, and learns from rejections or alternative decisions. The organization becomes accustomed to cooperating with an intelligent entity.

In an advanced stage, certain areas become "AI-driven" with human oversight: specific routine tasks, such as prioritizing standard orders, allocating robotic resources, or selecting carriers according to clearly defined criteria, are largely automated. Humans concentrate on exceptions, complex considerations, and strategic decisions.

In the long term, a "self-optimizing" logistics chain emerges, in which Logistics Managed AI continuously learns from real-time data, feedback, and external signals. It recognizes patterns that escape the human eye and proactively suggests changes to layout, process settings, contract structures, or network topologies. Management decisions become more data-driven and transparent.

This vision is not an end in itself. It is a response to structural constraints: skills shortages, cost pressures, volatility, and regulatory requirements can only be managed to a limited extent using traditional methods. In this context, a consistently managed, domain-specific AI layer is less a "nice-to-have" than a logical next step in the evolution of logistics.

Logistics Managed AI as a necessary development, not a buzzword

The development towards Logistics Managed AI reflects a broader trend: AI is moving out of pilot projects and laboratories and becoming an operational production tool – similar to forklifts, conveyor technology, or IT systems. In logistics, where data volume, process density, and real-time requirements are particularly pronounced, this transition is especially noticeable.

A standalone product category, Logistics Managed AI, makes economic and strategic sense because it bridges several gaps: between generic AI platforms and specialized logistics systems, between individual solution thinking and end-to-end orchestration, and between isolated efficiency gains and structural resilience.

It is not a replacement for WMS, TMS, robotics, or ERP, but rather the missing intelligence layer that integrates these systems in such a way that technology investments actually generate sustainable economic benefits. Its implementation requires technical, organizational, and cultural changes, but the alternatives—further fragmentation, insufficient use of automation assets, and increasing margin pressure with growing complexity—are not very attractive from a business perspective.

In a world where logistics has become a critical differentiator in virtually every industry, competition will increasingly hinge on who best strategically manages their physical flows through a managed, learning intelligence layer. Logistics Managed AI provides the conceptual framework for this – and marks the transition from "more technology" to a truly managed, intelligent logistics operation.

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