Beyond the hype: How integrated, configurable AI is redefining logistics

As legacy systems give way to modular, cloud-native architectures, companies are harnessing ecosystem-driven, configurable AI to optimize operations and unlock real-world value

By Wolfgang Lehmacher and Hans Galland · August 28, 2025 · Supply Chain Management Review

Artificial intelligence is casting off its experimental image to become a practical, cost-effective, and customizable toolset tailored to the complex demands of logistics, transport, procurement, manufacturing, and aftersales.

This transformation goes far beyond flashy algorithms or incremental tech upgrades. It represents a fundamental shift in how businesses consume AI, embedding it within their systems and workflows to unlock real-world value. The era of off-the-shelf software or fully bespoke builds is fading. In its place rises a new generation of modular, integrated, and configurable AI systems that adapt fluidly to an organization’s unique data landscapes, processes, and operational realities.

At the core of this evolution is a move away from monolithic, one-size-fits-all platforms toward microservices-based architectures. Companies today assemble best-in-class components, including cloud analytics, data lakes, real-time visibility tools, and AI-powered robotics into bespoke solutions designed to orchestrate supply chains with unprecedented agility. The sophistication of its models does not define AI’s value, but rather how seamlessly those models plug into existing business systems. Without overcoming the limits of legacy infrastructure and integrating into supplier networks and workflows, even the most advanced AI remains theoretical rather than transformative.

Overcoming the limits of legacy infrastructure

It is increasingly evident that legacy infrastructure has fundamental limitations that prevent it from meeting the demands of modern AI operational layers, particularly in terms of AI consumption and capabilities. Multi-tenant cloud computing has become a necessary foundation for cost-effective AI operations for several key reasons. First, AI algorithms—especially large language models (LLMs)—require vast computational resources that are most efficiently provided in the cloud. Second, since LLMs are typically hosted in the cloud, reliable and low-latency integration depends on cloud-based foundational data layers. Finally, embedding analytics directly into workflows is essential, as many operational decisions require response times measured in milliseconds.

Legacy on-premise (on-prem) or private cloud services face several significant constraints that limit their effectiveness for AI-driven operations. First, they cannot achieve the scalable computational resources offered by modern cloud environments. Second, they lack the low-latency connectivity essential for effective AI operational layers. Third, they often introduce considerable integration delays, with data retrieval from on-prem systems sometimes taking 10 minutes or more, making them unsuitable for time-sensitive tasks such as load planning. Finally, user adoption suffers when AI recommendations are delivered via external cloud-based platforms instead of being directly integrated into core on-prem systems like transportation management systems (TMS), frequently resulting in little to no adherence to these recommendations.

The market strongly signals accelerating demand for an “AI-native stack”—an integrated technology architecture designed from the ground up to support AI operations efficiently and effectively. This represents a fundamental shift from retrofitting legacy systems to building purpose-built infrastructure that can fully leverage AI capabilities. To some extent, this also applies to legacy processes that remain non-digital. In a reversal of the usual logic, the potential value that AI can unlock is now driving deeper levels of digitization, levels we have long aspired to achieve. The rise of this AI-native approach reflects not only a technological necessity but also a market-driven evolution toward more responsive, scalable, and integrated operational frameworks.

Collaboration and ecosystems: The new AI advantage

Success in this new era relies heavily on collaboration and ecosystem thinking. Leading organizations form partnerships with global systems integrators, consultancies, and specialized AI developers, combining domain knowledge with technical expertise. Core systems of record are often structured using an open system architecture in which APIs offer standardized connectivity between ecosystem components. This collective approach enables tackling thorny issues such as minimizing empty freight runs, optimizing logistics capacity, and deploying distributed ledger technologies for real-time, auditable transactions. These collaborative ecosystems accelerate the shift from pilot projects to scalable AI solutions that directly impact operations.

Integration extends beyond software to encompass cloud infrastructure, cybersecurity, and data governance. Forward-thinking supply chains treat AI as a strategic enabler, not a mere efficiency hack, to build resilient networks that anticipate risk and adapt quickly. Over the last 24 months, AI has moved from IT into the C-Suite and is now becoming a board-level imperative, even in the most traditional transportation enterprises. Near-shoring initiatives and the glass pipeline, which describes a state of maximum end-to-end visibility, reflect this shift, allowing stakeholders to monitor goods and supply chain risks in real-time and make agile, data-driven decisions.

At the operational heart of supply chain networks lie digital control towers, real-time command centers connecting procurement, manufacturing, transportation, warehousing, and distribution data through advanced analytics and automated exception management. These control towers replace fragmented, isolated tools with intelligent ecosystems that learn and self-optimize, delivering precision and speed across functions ranging from predictive pricing and returns management to shipment routing and supplier collaboration.

AI-driven transformation across the supply chain

The AI-driven transformation unfolds across the entire value chain, from factory floors equipped with digital twins, IoT sensors, and robotics with predictive maintenance, to aftersales services enhanced by automated billing, diagnostics, and AI-assisted warranty claims. These capabilities create continuous feedback loops that refine processes, products, services, and customer experiences.

Sustainability is now integral to this technological fabric. AI helps track environmental compliance, enforce supply chain transparency through blockchain-based product passports, and empower circular economy initiatives like repurposing and recycling. In a world of mounting regulatory and societal expectations, supply chains must navigate a dual mandate: be efficient and responsible.

Nevertheless, the path to successful AI adoption is complex and ongoing. It requires continuous investment in maintenance, compliance, training, and infrastructure upgrades, such as migrating transportation management systems to cloud-native, multi-tenant platforms and developing enterprise-wide data lakes and data lake houses, morphing into active, collaborative platforms powering dynamic, distributed, and AI-driven data organizations, rather than a static single source of truth. Navigating regulatory and ethical considerations is equally critical to maintaining trust and effectiveness.

This new complexity has blurred traditional boundaries: software vendors now act as consultative partners, while consultancies develop sophisticated, software-like AI solutions. Together, they deliver hybrid models finely tuned to individual company data profiles, operational challenges, and strategic goals. In logistics, a field marked by complexity and variability, configurability is no longer optional; it is a decisive competitive advantage.

Embedding AI for lasting impact and competitive edge

Ultimately, the lesson is clear: AI’s true power lies not in standalone technology, but in integration and adaptation. Digital leaders will be those who make the move away from legacy systems and processes and embed configurable AI deeply into their daily workflows, legacy systems, and organizational cultures, transforming supply chains into living, intelligent networks and ecosystems capable of anticipating disruption, optimizing flows, and driving innovation. In this evolving landscape where “one size never fits all,” mastering these fundamentals defines the frontier of logistics AI’s promise.

About the authors

Wolfgang Lehmacher is a global supply chain expert. The former director at the World Economic Forum and CEO Emeritus of GeoPost Intercontinental is an advisory board member of The Logistics and Supply Chain Management Society, an ambassador for F&L, and an advisor to RISE. He contributes to the knowledge base of Maritime Informatics.

Hans Galland is a technology entrepreneur and CEO of BeyondTrucks, a provider of an AI-native transportation management system for complex and specialized enterprise fleets. The 2025 Stevie Award Winner for Best Transportation Entrepreneur is a Stanford Graduate School of Business graduate and serves on multiple for-profit and non-profit boards. He is an author and frequent speaker on the impact of AI transformation on the transportation industry, its operations, and workforce.