AI in fleet operations: Unlocking efficiency and lower costs

Artificial intelligence (AI) slowly has been winding its way into many aspects of day-to-day life and, recently, the waste and recycling industry has been incorporating AI into fleet operations and waste hauling. By using AI, hauling companies aim to increase operational efficiency and reduce operating costs.

At WasteExpo 2025, Battle Motors, a New Philadelphia, Ohio-based heavy-duty vehicle manufacturer, launched Fortris AI, a data engine and AI platform for refuse trucks. While in production for refuse trucks, Battle Motors plans to scale the technology to other vehicles and fleets in the future.

Fortris AI entails the Fortris Control Hub and Fortris tablet that are installed inside the vehicle, consolidating essential tools into a dashboard system for operators. The hub and tablet pass data to the cloud, providing operators with information about vehicle health, speed and location.

“Fortris AI was built with one thing in mind, turning every truck into the smartest, most reliable team member in the fleet,” Battle Motors Chief Technology Officer Kelleigh Shankel says. “For refuse and recycling industries, daily operations are intense, unpredictable [and] heavily regulated, and Fortris AI acts as a digital copilot.”

Fortris AI helps drivers and operators diagnose truck issues in real time and can flag unsafe behaviors, such as repeated hard stops, or indicate when brake systems show signs of heat stress.

The platform also includes a vision system with various cameras that allow the driver to have eyes on active parts of the vehicle. While other refuse trucks have a number of screens showing different parts of the vehicle, Fortris AI consolidates redundant screens into one center touchscreen. The camera feed changes based on what the truck is doing. If the truck is going in reverse, it shows the back. If the driver turns on the turn signal, the screen shows the blind spot. The software system determines which part of the truck is relevant for the driver to see and focuses on that part, rather than having multiple camera feeds for the driver to search through.

The software also offers insights into the performance of the vehicle and the driver.

“We generate around 2,000 messages a second from the truck scan bus, and we’re able to consume all that data and then take the stuff that is important for uptime,” Battle Motors Chief Software Officer Paul Marsolan says. “We have the capability to detect if the driver slams on the brakes or hits the gas too much, [for example], for driver coaching.”

The feedback Fortris AI provides to the driver also is supplied to the fleet manager in real time. For example, if the vision system detects a potential fire, the driver and the fleet manager both are notified. Even if it is something as simple as a route change, the fleet manager is notified so every member of the team is aware and able to react.

Battle Motors is not the only one using AI to improve fleet operations.

Routeware, a Tigard, Oregon-based developer of technology for the waste and recycling industry, has been using machine learning to optimize algorithms for fleets. It uses data through agentic AI (which operates with a higher level of autonomy, perceiving the environment, making decisions and taking action with little human intervention) to be more proactive and responsive.

Routeware’s software mainly focuses on detection and computer vision. Its partner, Waste Vision, can detect pedestrians a truck driver cannot see. AI is built into the camera feeds so potential hazards are detected and the driver is alerted. This computer vision also can be used to see into the truck’s hopper and detect contamination or overflow.

Refining route optimization

AI and machine learning also are used to help improve route optimization on waste fleets.

Traditional route optimization does not take into account factors such as historical traffic patterns, weather or other significant events. With AI, however, route optimization can continue to be refined, according to Routeware.

“We can build into the algorithm things like school zones, bridge capacity and bridge height to make sure the truck can safely traverse the route and stay away from things or places that might add to risk or danger,” Routeware Chief Technology Officer Brent Glover says.

Routeware says its system continually improves as different inputs are included in the algorithm.

According to Glover, without AI, route optimization is static and outdated, and fleets are forced to be reactionary instead of anticipating an event.

“We’re always reacting after a route was missed, after the truck failed, after the incident occurred,” Shankel says. “Let’s be honest, it’s tough to manage that kind of variability when you’ve got 50-plus trucks on the road before sunrise. So, AI gives us a way to standardize excellence, and that turns guesswork into playbooks.”

Understanding challenges

While AI can make aspects of fleet operations much easier, its incorporation comes with challenges.

The filtering of data is one limitation technology designers face. Computer vision and machine learning can take in an excessive amount of data. Coding algorithms to ensure that the system is capturing correct and helpful information can be difficult.

Marsolan says the challenge lies in identifying “good” data from “bad” and making educated decisions.

“Things like the vision system, things like tire pressure monitoring, things like oil pressure—how can we use all this data, but not feed too much of it into the AI to allow it to still make educated decisions for us?” he asks.

According to experts, AI in fleet operations still has room to grow as it is young and in the experimental stages in some cases. It’s possible that in the future, AI will handle tasks like routing, schedule planning, municipal compliance reporting and automated fault resolution without human intervention.

“From an industry shift standpoint, I think we’ll see AI shift from helpful assistants like it is today to a strategic partner,” Shankel says. “Let’s say regulations tighten, staffing challenges grow and you know, fleet alternative power trains ramp up, AI will be critical to staying competitive for the fleets. I think we’ll wonder how we ever did our business without it.”

The author was a summer intern with the Recycling Today Media Group.