More sustainable vehicles and operations are an attractive proposition for fleet owners. The right approach can at once reduce costs, meet compliance mandates, and limit environmental impact.
But in an industry defined by wear and tear, intricate logistics, and abundant energy consumption, fleet owners may believe increased sustainability and fleet efficiency are out of reach, only possible through prohibitive investments in entirely new fleet vehicles and equipment.
Fortunately, there are multiple routes to more sustainable operations. In 2025, two techniques and technologies in particular, asset life cycle management and artificial intelligence, can have an outsized impact, especially when coupled together. Fleet owners should learn about both technologies’ impact on efficiency and then deploy them for their trucks, buses, railway cars, and other fleet assets.
Asset life cycle management and fleet maintenance
Maintenance is a regular part of every fleet owner’s experience. But while the need for maintenance is constant, how each owner approaches the task varies significantly. Some take a reactive approach, waiting for gaskets to blow or radiators to overheat before they act. Other fleet owners take a proactive approach, tying maintenance to a set timetable, such as checking coolants every number of months. The most prudent owners, however, take a predictive approach, drawing on historical data to anticipate and address potential issues before they become bigger problems.
This predictive approach is at the heart of ALM. ALM entails the careful monitoring of fleet vehicles for their full lifespan, from their first day on the road or rails to their last. Tools like IoT sensors are used to monitor assets at scale and in detail. This data provides real-time insights. It also fuels a historical data set that can be used to forecast future problems before they emerge, such as the type and timing of equipment malfunctions.
ALM can work in tandem with other asset management strategies such as condition-based maintenance. CBM also uses a mix of sensors and software to monitor fleet assets, identifying failures and minimizing vehicle downtime. CBM monitors fleet assets’ current condition, however, not their future condition, making ALM a necessary partner.
See also: Trucking's AI outlook: What solutions await in 2025
Artificial intelligence unlocks automation
In recent years, AI has helped fleet owners boost sustainability, from optimizing routes to guiding smarter fuel usage. Still, many in the industry are not capitalizing on the technology.
One prime technology is computer vision, a branch of AI that enables computers to “see” and analyze objects, such as train cars and truck parts. Cameras, drones, and other devices infused with computer vision can monitor and analyze assets at a scale and speed that humans cannot.
Meanwhile, generative AI, another, newer branch of AI, provides a different kind of automation. Generative AI models are trained on vast data sets of work orders, maintenance forms, and other information, and then put to work automating formerly mundane processes like filling out, filing, and summarizing maintenance paperwork. Fleet owners can leverage generative AI to create reports, recommendations, and other communications, freeing up humans for more strategic work.
AI technology is advancing quickly, with a new focus on “agentic AI” systems, or “agents,” that can complete complex tasks. Experts expect to see AI agents enter the transportation space in 2025.
ALM + AI
ALM and AI are powerful techniques and tools on their own. Together, they are even more capable of achieving sustainable operations, for example, the predictive maintenance aspect of ALM coupled with the power of computer vision. Fleet owners can train computers to monitor a sprawling fleet of vehicles and related equipment, pulling in real-time data on an asset's condition. This provides deep insight into how assets are performing and, just as importantly, how they will perform in the days, weeks, and months to come.
I’ve seen the impact of ALM plus AI firsthand at my organization, IBM. We worked with the Downer Group, the Australian company building and operating fleets of light and heavy rail and the largest manager of passenger rollingstock in the country. ALM and AI are at the core of TrainDNA, Downer Group’s platform for managing hundreds of trains. The approach allows Downer to monitor each asset of each vehicle of each fleet. The upshot? A major decrease in malfunctions, resulting in a 51% increase in train reliability. Transport for London has had similar success, marrying ALM and AI to better manage rollingstock and long stretches of railway and roadway.
Fleet owners have a busy year ahead: Their assets enable essential commerce and public services. With the proper use of ALM and AI, owners can meet these challenges with more efficient and effective vehicles. Over time, these energy and operational savings can make an outsized impact on the bottom line.
