The potential of AI in fleet maintenance stems from the ability to access, process and analyze massive amounts of data nearly instantaneously, adapt and evolve programming. But what is AI? The answer is — that depends. There’s generative AI, agentic AI, machine learning, deep learning, large language models, forecasting models, bespoke models that do more than prediction, etc.
For many years, fleet operators have been drowning in data on driver and vehicle performance captured by onboard technology, including engine data, video from dash cameras and compliance information from electronic logging devices. Harnessing the latest AI capabilities, fleet operators and their technology vendors are translating that sea of data into deeper insights on driver performance and safety risks. This is enabling fleets to take a more proactive stance on safety while empowering drivers to self-correct unsafe driving behaviors.
The Vehicle Maintenance Reporting Standards (VMRS) have long been the industry’s universal shorthand of maintenance reporting, eliminating the need for extensive written communications with all the inherent problems of miscommunication normally associated with the written word. Accordingly, VMRS provides a single, concise coding convention to manage fleets’ assets and analyze maintenance operation costs. But now, the advent of AI opens up many new possibilities for improving data analysis of fleet operations which use VMRS.
One of the biggest challenges fleet managers face is dealing with third-party maintenance providers, especially when it comes to breakdown or unscheduled maintenance events. With the vast majority of fleets performing their own maintenance, fleet managers often must work with service providers with which they may have little or no experience during such roadside breakdown incidents. Customer and service provider expectations regarding rapid repair assessment, downtime, and approval communications can vary greatly and lead to dissatisfaction among both parties.
Commercial vehicle specification is both an art and science, practiced by fleet managers who best know their own company’s operations. It’s true that recent trends toward vertical integration in manufacturing have somewhat limited a fleet manager’s options when it comes to spec’ing; however, a dazzling array of choices remain when it comes to specifying nearly all aspects of commercial vehicle design.
Much attention is being paid to how artificial intelligence can lead to fleet optimization in routing, dispatch, and driver performance, but how can emerging AI tools improve technician productivity on the shop floor? Half the battle is knowing what’s wrong and how to fix it, and AI tools have the potential to improve diagnostic and repair processes through better technician triage and training. During this session, our expert panel will discuss how AI solutions can improve productivity on the shop floor, shortening the time it takes to identify faults and restore asset availability.
The latest advances in AI are making it possible for back-office personnel at trucking and logistics providers to automate manual processes, work more efficiently and do more with less. As these AI capabilities become more engrained in freight operations, workers will focus less on routine processes and more on management by exception and higher-value activities. What are the long-term workforce implications? Will businesses be able to accomplish more with leaner back-office teams?
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