Behind greener supply chains, AI is driving a new energy load

The study identifies a less visible but increasingly important countertrend: the rapid growth in energy consumption driven by AI itself. Training large machine learning models, running continuous inference, storing vast datasets, and operating AI-enabled platforms all require substantial computing power. Data centers, which form the backbone of AI-driven logistics, are among the fastest-growing sources of electricity demand globally.

AI-driven systems promise major gains in efficiency, cost control, and resilience in global logistics and supply chain management. But a new academic study published in the journal Energies finds that these benefits come with a growing energy cost that could undermine sustainability goals if left unmanaged.

The study, titled Who Wins the Energy Race? Artificial Intelligence for Smarter Energy Use in Logistics and Supply Chain Management, examines the dual role of artificial intelligence as both an energy-saving tool and an emerging source of significant energy demand across logistics and supply chains.

The study challenges the assumption that AI-driven logistics is inherently sustainable. Instead, it argues that AI’s net climate impact depends on how closely digital transformation is aligned with clean energy deployment, efficient hardware, and strategic energy management.

AI delivers efficiency gains across logistics and supply chains

The study shows how AI is already transforming logistics operations at multiple levels. In transport, machine learning models analyze traffic patterns, weather conditions, fuel prices, and delivery schedules to optimize routing and fleet utilization. These systems reduce empty runs, cut fuel consumption, and shorten delivery times, offering immediate energy and emissions benefits.

In warehousing, AI-driven demand forecasting and inventory management systems reduce overstocking and unnecessary movement of goods. Automated storage and retrieval systems powered by AI minimize energy use per unit handled, while predictive maintenance tools lower downtime and prevent energy-intensive equipment failures. Ports and intermodal hubs use AI to coordinate vessel arrivals, crane operations, and cargo flows, reducing congestion and idle time that traditionally waste fuel and electricity.

The authors highlight digital twins as a particularly powerful application. By creating virtual replicas of logistics systems, companies can simulate energy consumption under different scenarios and identify efficiency improvements before making physical changes. This allows firms to test alternative routing strategies, warehouse layouts, and scheduling models with minimal risk and cost.

AI also strengthens supply chain resilience, which has indirect energy benefits. More accurate demand forecasts and real-time risk detection reduce emergency shipments, last-minute rerouting, and redundant inventory, all of which carry high energy penalties. In this sense, AI-driven stability can lower overall system energy intensity even as volumes grow.

The study notes that these benefits are most visible in advanced logistics networks with access to high-quality data, reliable digital infrastructure, and skilled personnel. In such environments, AI has become a key competitive tool, allowing firms to reduce operating costs while responding faster to market shocks.

Rising digital energy demand threatens sustainability gains

The study identifies a less visible but increasingly important countertrend: the rapid growth in energy consumption driven by AI itself. Training large machine learning models, running continuous inference, storing vast datasets, and operating AI-enabled platforms all require substantial computing power. Data centers, which form the backbone of AI-driven logistics, are among the fastest-growing sources of electricity demand globally.

Energy use from AI is not limited to centralized data centers. Edge computing devices, sensors, automated vehicles, and robotics embedded throughout logistics networks add to cumulative energy consumption. As AI systems become more complex and ubiquitous, their energy footprint expands across the entire supply chain.

This creates a paradox. At the operational level, AI reduces fuel use and emissions in transport and warehousing. At the digital infrastructure level, it increases electricity demand, often in regions still dependent on fossil fuels. Without careful coordination, the study warns, the second effect can offset or even exceed the first.

The paper highlights semiconductor manufacturing as another energy-intensive component of AI expansion. Producing advanced chips required for AI workloads involves significant electricity and water use, adding upstream environmental costs that are rarely accounted for in logistics sustainability assessments.

Regional disparities further complicate the picture. Advanced economies with access to renewable energy, efficient grids, and strict environmental regulation are better positioned to balance AI-driven efficiency with clean power. Developing economies, where logistics growth is fastest, often rely on carbon-intensive electricity and lack the infrastructure to integrate renewables at scale. In these contexts, AI adoption may improve logistics performance while increasing absolute emissions.

The authors argue that treating AI as a neutral or automatically green technology is a strategic mistake. Energy demand must be considered an integral part of AI deployment decisions, not a secondary concern addressed after systems are scaled.

Winning the energy race requires coordination, not automation alone

AI’s role in sustainable logistics is conditional rather than guaranteed. AI becomes a net positive for energy and climate goals only when it is deployed alongside coordinated energy strategies, organizational change, and supportive policy frameworks.

One key requirement is the integration of AI systems with renewable energy sources. Data centers and logistics hubs powered by solar, wind, or other low-carbon energy can dramatically reduce the emissions associated with digital operations. The study points to growing interest in colocating data centers with renewable generation and using AI itself to balance energy loads based on supply availability.

Energy-efficient hardware and algorithm design also play a critical role. Optimizing models to reduce computational complexity, using specialized low-power chips, and prioritizing energy-aware software development can significantly lower AI’s electricity footprint. The authors stress that efficiency at the algorithmic level is just as important as efficiency in physical logistics operations.

Organizational factors matter as well. Companies that treat AI as a strategic capability rather than a standalone tool are more likely to align digital investments with energy and sustainability objectives. This includes reskilling workers to manage AI-enabled systems, redesigning processes to avoid rebound effects, and embedding energy metrics into performance evaluation.

Carbon pricing, energy efficiency standards, and incentives for renewable energy adoption shape whether AI-driven logistics contributes to decarbonization or locks in higher emissions. The study suggests that fragmented policy approaches risk encouraging efficiency gains in one part of the system while allowing unchecked energy growth in another.

Equally important are transparency and measurement. Many companies track fuel savings from AI-optimized transport but fail to account for the electricity consumed by supporting digital infrastructure. Without full lifecycle assessment, sustainability claims remain incomplete and potentially misleading.