1. How can AI help me reduce fuel costs in my fleet?
AI can process information at a larger scale than any human team. If you are a fleet owner or operator, levering this capability has big implications: Once the data piping is set up in the right way, AI can look at the entire operation planning from a bird’s eye view, and decide within seconds the best way to build and dispatch loads.
By making optimal decisions, AI will help you reduce driven distance, while balancing service quality, driver workloads, and other factors. One of the main benefits of integrated, centralized decision making will be fuel savings.
2. How do I calculate total cost of ownership with AI?
The key step to calculating ROI is to understand how much value would be unlocked from implementing AI in my operations. Usually, value is driven by one or multiple of the following factors:
Reduction of menial work performed by the team, through process automation:
Ingestion of unstructured data for load building
Optical recognition and transcription of physical documents (BOL etc.) in driver workflows
Calculating rates/fees, preparing related documents (invoicing, payroll, etc.)
Managing communications, negotiating fees (agentic AI)
Reduction of errors from automating tasks (related categories as above)
Optimized, centralized decision making.
Orchestration of the fleet’s operations (load building, dispatching)
Navigation
Refueling
It is not easy to estimate the value generated by one of these factors beforehand. But asking simple questions that quantify the impact can help: We are automating load building: what if my dispatcher now saved 3 hours a day? How many preventable errors am I making, and how much do they cost?
By going through all categories listed above and thinking about the implications of AI implementation, it is possible to get a rough estimate of the benefits.
3. What is the most efficient way to manage maintenance schedules?
There are multiple fleet management software (FMS) solutions that can help a fleet operator manage maintenance schedules of their fleet. They range from simple tools to extraordinarily complex software that can manage the intricacies of large fleets. (What Questions to Ask Your TMS Vendor)
In fleets, it is critical to ensure that maintenance does not interfere with operations, and vice versa, which means that it is important that the FMS is integrated with the TMS. That way, for example,
truck availability can be blocked when maintenance is due, and dispatchers do not have to manually check for availability when assigning loads.
4. How can I compare different TMS software for my fleet?
Different TMS solutions speak to different customers. While some fleets have incredibly complex operations that require custom solutions, and others are perfectly well with a simple tool.
When comparing TMSs it is important to understand what are the factors that will influence your decision. A few questions that might help this process are:
What do I want to achieve? Do I want a system of recording, a tool that will help me with decision making, a platform to centralize data infrastructure, a solution for driver workflows? All the above?
What processes would I like to automate, and can this TMS deliver?
What systems can it integrate into?
Is it a particular integration custom or off the shelf? What does this mean in terms of engineering costs?
How easy is the interface to use? Will my team want to use this system?
To help with the decision, a good practice is performing “deep dives,” where all the relevant processes and systems are considered and compared with the TMSs capabilities. The aim is to visualize how processes look after the TMS is implemented. How simple is the final solution? And does it solve my pain points?
5. Can AI help me improve driver retention or satisfaction?
AI is a powerful tool with consequences that can both increase and decrease driver satisfaction. It is important to think about how AI is used best to drive higher retention rates.
One factor that we have observed in the field as an effective tool for increasing satisfaction is the distribution of driver workloads. When decision making is centrally managed, dispatchers can ensure an even workload distribution between drivers, which in turn increases sensations of fairness. This is key, since perceived unfairness, and low workloads are a key factor in driver churn.
Conversely, one of the ways where AI can be detrimental for driver retention is the automation of calls. Especially in the private fleet segment, drivers like to know their dispatcher and to talk to them. Replacing those calls with a machine can take away one of the positive aspects of their job, decreasing satisfaction.
Matias Oberpau is Director of Business Development at BeyondTrucks, where he leads growth in AI-native fleet and logistics solutions. He holds a degree in Industrial Engineering from Universidad Católica de Chile and an MBA from Stanford University. Matias excels in AI-first operations, cloud-native TMS integration, and fleet automation—helping clients cut costs, boost utilization, and build resilient operations