Artificial intelligence is emerging as a “bionic arm” for trucking fleets, helping planners, dispatchers and drivers make smarter decisions that reduce wasted miles, improve fuel economy and enhance safety, panelists said during a discussion on sustainable fleet operations.

Fleet and technology leaders, speaking at the Green Tech stage during Truck World 2026 in Mississauga, Ont., on April 16, said AI is moving beyond buzzword status and into practical applications that cut manual work and improve decision-making across the business, while keeping humans firmly in control.

Zahi Mitri, vice president of innovation and technology at Challenger Motor Freight, said fleets are using AI to improve planning and make better decisions before, during and after a trip.

He said Challenger is focusing first on operations planning and fuel economy, using AI to better understand truck performance and make smarter decisions about total cost of ownership, route selection and where specific equipment should run.

“We’re able to use that data for better decision-making, better outcomes,” Mitri said.

He said the goal is not simply to assign trucks to routes, but to understand which trucks should operate in specific regions and applications. That kind of analysis, he added, can uncover efficiencies that would be difficult to find through manual review alone.

Decision augmentation

Mitri said fleets also learn quickly that AI tools must account for countless real-world exceptions. That has forced operators and vendors alike to revisit data quality, refine the tools and make sure the technology truly supports planners instead of adding another layer of complexity.

He described the best systems as a form of decision augmentation rather than automation without oversight.

Bill Cain, director of product management for the TMW suite at Trimble, said AI is most valuable when it removes friction from repetitive, manual tasks that consume time across fleet operations.

He pointed to order entry, dispatch, billing and fuel planning as examples where AI can reduce keystrokes, lower the risk of human error and speed up workflows.

Focusing on problems

Cain said dispatchers and planners make hundreds of small decisions each day, from matching drivers and loads to identifying the best fuel stops and routes. AI can help by organizing those decisions around exceptions, allowing staff to focus on the problems that require attention instead of hunting through multiple systems.

“I think that’s where AI really helps,” Cain said.

Rather than replacing employees, he said, AI can bring the most urgent issues to the surface, so workers arrive each morning with a clearer picture of what needs action. In that model, red and yellow exceptions demand attention, while routine items can move forward with less manual intervention.

Jean-Sebastien Bouchard, chief product officer and co-founder of Isaac Instruments, said telematics providers already collect large volumes of information from trucks, drivers and routes, but AI can help fleets turn that information into action.

Identifying risky behavior

Bouchard said fleets want to know which drivers are safest, which are riskiest, which trips are most fuel-efficient and where attention is needed right away. AI can help highlight those patterns without forcing managers to sift through data all day.

“Our goal is really to bring up to the surface that information so you can take action on it,” Bouchard said.

That includes using data from onboard cameras and other sensors to identify risky behaviors such as distracted driving, following too closely or rolling through stops. Bouchard said the value of AI lies not only in detecting those events, but in sorting them by severity and helping fleets decide what matters most.

He said the technology is moving toward real-time coaching, where the system can warn a driver immediately instead of only reporting the incident back to the office later. That kind of intervention, he suggested, could help prevent unsafe behavior before it leads to a collision.

Fuel economy

For fleets focused on sustainability, fuel economy emerged as one of the clearest opportunities.

Bouchard said fleets can gain meaningful savings by improving driver behavior through coaching tools that encourage smoother driving, better pedal management and stronger anticipation on the road. Even small improvements in driver scores can translate into measurable reductions in fuel consumption, he said.

Mitri said the same principle applies more broadly to planning. Better trip design, more predictable assignments and fewer wasted miles can improve both sustainability and driver productivity. He described the “greenest mile” as the one that does not have to be driven.

That philosophy extends to owner-operators and company drivers alike, he said. More efficient trip plans and better fuel decisions can improve the business case for independent operators while giving company drivers more predictable work.

Depends on trust

Still, all three panelists stressed that AI adoption depends heavily on trust.

Mitri said fleets must start with a clear pain point and be honest about employee concerns, including fears about job stability. He said executives need to support AI projects, but adoption also depends on strong buy-in from the people expected to use the tools every day.

He said the conversation cannot be framed around replacement.

“This is not about replacing, rather, this is about augmenting,” Mitri said.

Cain agreed, saying fleets should start small, look for measurable outcomes and focus on applications where the return is easy to understand. He cited order-intake automation as one example, especially for carriers still manually entering large volumes of loads.

Reducing repetitive data

He said reducing repetitive data entry can lower costs, improve accuracy and free employees to spend more time on higher-value tasks.

The panelists also warned fleets to look closely at the quality and origin of AI tools before adopting them.

Cain said fleets need strong governance, secure architecture and real-time data if they want AI to produce reliable results. Without those foundations, he said, even polished tools can become little more than reporting dashboards dressed up as AI.

Data is gold

Bouchard said fleets should ask vendors where the data is going, how it is stored and whether it is being moved offshore or used in ways the customer does not fully understand. He warned that in a rush to adopt new tools, fleets can overlook serious questions about confidentiality, ownership and how their information may be reused.

“Your data is your gold,” Bouchard said.

Mitri added that fleets also need to be wary of vendors who simply wrap a large language model in a new interface and present it as a complete solution. The key question, he said, is whether the tool is truly adding value or just repackaging an existing capability with new branding.

He said poor governance and weak security practices could eventually trigger a major industry wake-up call if companies move too fast without understanding the risks.

Infrastructure connections

Looking ahead, Mitri said one promising area is infrastructure-to-vehicle communication, including the ability for trucks to interact more intelligently with traffic signals and other road systems. While he said fully autonomous trucking remains far off, smarter infrastructure connections could improve safety and traffic flow sooner.

For now, the panel agreed that successful AI in trucking depends on clean data, strong integrations and disciplined implementation.

Bouchard said AI improves as it learns from outcomes, which means fleets must connect the right data sources if they want better predictions. Cain said that includes bringing together information from the TMS, ELD, fuel systems, traffic tools and other platforms in real time. Mitri said the long-term goal is a decision layer where fleets can effectively “talk” to their data and uncover patterns faster.