From underutilized fleets to empty trips and poor routing: the sector suffers from shortcomings in planning and execution that not only increase emissions but also undermine profitability. However, these challenges can be addressed.
Driving sustainable change with AI
AI is already delivering tangible sustainability gains in two crucial, yet perhaps unexpected, areas: vehicle maintenance and transport operations. By creating faster decision-making, streamlined processes, and smarter systems, AI can ensure cleaner logistics without compromising on performance – and forms the backbone of smarter logistics.
Much is at stake here. According to Siemens, the 500 largest companies in the world lose nearly $1.4 trillion annually due to unplanned downtime. That’s a staggering 11% of their revenue. Logistics operations, with tight delivery times and high resource deployment, feel this impact particularly strongly.
Fleet maintenance is often overlooked when it comes to sustainability in transport, but it is a crucial area of focus. Excessive maintenance of vehicles wastes resources, not only materials but also time. Conversely, insufficient maintenance leads to breakdowns, costly repairs, and premature replacements. Both scenarios are bad news for the company and the environment. Fleet maintenance is a critical focus area for sustainability and thus for making logistics smarter.
Smart maintenance with AI to extend lifespan
AI offers a better perspective, starting with standardized maintenance. Predictive and optimized maintenance is gaining ground, especially in North America, where new industry standards are setting the stage for AI-driven approaches. The core of this evolution is the need for standardized data. Because without that data, fleets rely on inconsistent or proprietary codes to track maintenance intervals, making it harder to train AI models at scale and share insights.
New frameworks such as the Vehicle Maintenance Reporting Standards (VMRS), developed by the Technology and Maintenance Council of the American Trucking Association, are changing that. They create a universal language for tracking maintenance, thus forming the basis for adaptive, AI-driven decisions - such as replacing oil based on actual load and engine usage, rather than at arbitrary intervals.
While VMRS represents a significant step forward in North America, there is still a long way to go globally. Much maintenance data remains fragmented. To fully leverage AI's potential, the industry needs a shared foundation: coded standards that serve as a common language for fleets, platforms, and regions. Some platforms are already building towards this, with open, interoperable data models designed for global application.
The effect is tangible. AI can determine the 'sweet spot' moment for maintenance, reducing waste from prematurely replaced oil and avoiding unnecessary wear. Today, we often still rely on a dashboard warning light, but AI makes a future possible where the vehicle not only alerts but also schedules an appointment, sends performance data, and arrives at the workshop at the perfect moment.
Optimizing operations to reduce empty miles
Beyond vehicle maintenance, AI is also changing how freight is planned, routed, and executed, truly becoming the heart of smarter logistics. One of the biggest challenges in logistics is empty miles: trucks driving without cargo, wasting fuel and time. Although some inefficiencies are structural - such as geographical factors or the way the network is built - much can be solved with the right technology.
AI-driven systems now analyze real-time data, as well as historical data, to determine the most efficient routes, plan multi-stop loads, and continuously adjust routes on the go in response to delays, traffic, or bad weather.
Smarter logistics with AI in planning, procurement, and visibility
Cloud platforms are already applying these capabilities, with AI dynamically matching loads to carriers and minimizing waiting times at loading docks. They also alleviate the pressure of just-in-time logistics, where tight time windows leave little room for error.
Autonomous procurement tools can now handle transport requests with minimal human intervention, using statistical and symbolic AI to analyze unstructured requests, identify suitable partners, and make the best choice based on time, cost, and environmental criteria.
Combined with intelligent planning tools for loads that maximize truck cargo space and reduce the number of trips, these systems contribute to reducing emissions per mile traveled and thus to smarter management of logistics.
The future is about collaboration and is AI-driven
When AI is applied to maintenance, execution, and operational processes, it can yield significant sustainability gains for the logistics sector. Although AI itself also consumes energy - especially with generative AI (GenAI) - the applications in transport and logistics are much less computationally intensive. The efficiency gains and emission reductions outweigh the ecological footprint of the technology itself in most cases. The net effect is what matters. And in this context, AI is already showing transformative potential for a more sustainable future.
However, this realization depends on shared data, interoperable systems, and collaboration among carriers, shippers, OEMs, and technology partners. Whether it’s about maintenance schedules or routing algorithms, AI only works if it has access to reliable data with broad applicability. Only then does it generate valuable insights.
That’s why standardization is so important. We are not just building tools; we are forming a smarter ecosystem where every decision - on the road or in the yard - contributes to a more efficient and sustainable whole.
AI will not change logistics overnight. But by focusing on the fundamentals, it is already changing how goods are transported, how fleets are managed, and how sustainability goals are achieved. Because when the sector moves together, we lay the foundation for a cleaner, resilient future.
Philipp Pfister, Sector Vice President, Transporeon
