Trucks
From the workshop to the driver’s seat, AI offers huge potential for improving productivity, uptime, fuel consumption and safety. In many areas, it is already having a major impact – and its impact will only grow.
The emergence of AI is heralding new gains in efficiency and productivity across society – and the trucking industry is no different. It is helping to accelerate existing trends as well as enabling new capabilities that until recently were inconceivable. Here are seven major areas where AI is impacting logistics and road freight.
1. Faster and more accurate predictive maintenance
In recent years, one of the biggest developments within trucking is the ability to collect data from the vehicle and use it to help predict and anticipate faults before they cause breakdowns. While this is no longer new, AI allows much larger amounts of data to be processed and analyzed – and far quicker.
This makes it easier to identify patterns in the data and make connections between specific faults and their contributing factors. It generates greater insights into the warning signs likely to lead to a breakdown, so they can be addressed through scheduled maintenance.
The speed of AI also has the potential to enable real-time data retrieval and analysis and significantly shorten diagnostics times. The sooner the truck owner is forewarned, the easier it is to plan services and repairs.
2. Service scheduling by need – not miles
As well as predictive maintenance, connectivity and data are also enabling adaptive maintenance. While service visits have traditionally been scheduled according to the calendar or a vehicle’s mileage, adaptive maintenance is scheduled according to the truck’s specific workload and condition. If a truck is in good condition, a service can be delayed. Conversely, if a potential fault has been identified or if the truck has been operating in harsh conditions, a service visit can be brought forward to minimize the risk of any unplanned breakdown. Either way, the truck spends more time out on the road.
Again, this is not new, but AI is accelerating and improving the process. It is making it even easier and faster to evaluate a truck’s condition remotely, and in real time. This way a truck only needs to come into the workshop for a service when it really needs it.
3. More efficient route optimization and fleet management
Intricate planning and coordination are integral to any efficient logistics operation, and route optimization can help ensure every truck is as productive as possible with minimal empty miles. However, it can be a complicated process, with multiple changing variables such as traffic, weather and customer needs. It is particularly complex for haulers that transport mixed goods across multiple delivery points.
With AI, route optimization can be enhanced to a whole new level. It can be used to design efficient schedules and delivery routes and make adaptations in real-time based on changing circumstances. UPS, Amazon, FedEx and DHL are just some of the major logistics companies currently using AI-powered route optimization.
This will become even more valuable as the industry shifts to electrification. The need to charge adds another layer of complexity to route planning. However, AI-powered solutions have the potential to simulate routes and energy consumption and seamlessly add opportunities for recharging with minimal disturbances to the driver’s delivery schedule.
4. Better driver support services – in real time
Much of the vehicle data that is collected today is connected to driver behavior. It can be used to identify things like frequent harsh braking and acceleration – behavior that has a negative impact on both fuel consumption and safety. There are already connected services that can analysis and process this data, and be used to help support drivers to improve their driving technique.
With AI, these services can be enhanced so that they react faster and process more data. Instead of statistical reports, perhaps they can provide real-time coaching.
5. Smarter active safety systems
Active safety systems already enable huge improvements in road safety. To be effective, these solutions rely on complex algorithms and computing power capable of processing multiple data points, before making decisions in microseconds. They need to be able to monitor the vehicle’s surroundings and identify things like pedestrians and other road users. As part of their development, active safety systems need to be tested for a wide range of traffic scenarios to ensure they’re effective in any given situation.
With AI, even more data points can be processed enabling rapid decisions. Testing simulations can be done faster and incorporate a broader range of situations. This will hopefully improve their ability to identify different moving objects, as well as street signs and traffic lights. And further in the future, there is the potential to develop more self-driving support functions to aid the driver in hazardous situations. For example, a function that prompts the truck to pull over and come to a safe stop autonomously if it detects that the driver is unconscious.
6. Digital workshops
Digitalization is also impacting workshops, with technicians increasingly reliant on IT systems for retrieving instructions and documentation before performing services and repairs.
One possible solution that is being explored is to equip technicians with AI-powered handheld devices, which would enable them to access this information far faster than they can today. Many people are already successfully using AI tools to solve complex problems using plain language and uploaded images. It should therefore be possible to create the same support function for technicians. The result will be faster and more effective repairs.
No one can say for sure what the future holds, but one thing is certain. AI will continue to create multiple exciting possibilities for the trucking industry.
If you’re interested in reading more about digitalization, connectivity and data, you might like to read:
- How connectivity and AI are improving truck uptime
- How technology contributes to efficient driving
- What role can Machine Learning play in the trucking industry?
[1] Sarah Whitman, ’ Real-World Examples of AI Being Used for Route Optimization’, 298 September 2025, Debales, https://debales.ai/blog/real-world-examples-of-ai-route-optimization-in-logistics
