Artificial intelligence could give even the smallest fleet access to the knowledge, processes and decision-making capability traditionally associated with the world’s largest operators, according to Geotab founder and CEO Neil Cawse.
Speaking with Fleet News Group, Cawse said the pace of change in AI has accelerated sharply, shifting the technology from an emerging capability to a core part of how software companies—and soon fleets—will operate.
“This AI world is moving so quickly, it is really like quicksand,” Cawse said. “You put your foot down and then the floor moves underneath you.”
For Geotab, the challenge is not simply adding AI features to a telematics platform. It is using AI to turn the large volume of vehicle, driver, maintenance, safety and operational data generated by fleets into practical guidance for fleet managers.
Cawse said the company has long planned to expand the role of AI in its products, including its ACE assistant, but the speed of recent advances has brought that work forward.
“We always had plans to improve ACE and to introduce more AI into the product, but I think it has just accelerated things much more,” he said.
A major shift occurred late last year, Cawse said, when AI tools became capable of autonomously producing software at a level that changed how Geotab’s engineering teams thought about their own work.
“Before, we started seeing AI help us write some software, but it was us writing it, checking it, making sure it was working,” he said.
“Suddenly in November, you could autonomously have the AI write your software, and suddenly all the engineers realised that it was better at writing software than any human could be.”
That experience has reinforced Geotab’s view that AI will develop rapidly in fleet applications. Cawse expects the company’s next Geotab Connect event to place even greater emphasis on AI, but said the more important question is how the technology can create a better operating model for fleets of every size.
“The next Connect will certainly be even more AI-heavy,” he said.
“It is really taking the best knowledge of all of our best fleet managers around the world and distilling that into the AI.”
The objective is to make proven fleet-management knowledge more accessible. Instead of relying solely on internal experience, a small business could use AI to identify risks, interpret operating data, prioritise actions and make better decisions across safety, maintenance, utilisation and cost control.
“So that even a person who is running five trucks can run the most incredible fleet,” Cawse said.
“That is really the goal moving forward, as we supercharge the AI.”
For smaller fleet operators, the promise is significant. Large fleets can employ specialist managers, safety teams, analysts, procurement experts and maintenance planners. A five-truck operator may have none of those resources, with the owner often responsible for driving, customer service, scheduling, compliance and finance.
AI has the potential to narrow that gap by presenting the most relevant information at the right time, rather than expecting a business owner to interpret multiple reports, dashboards and alerts.
However, Cawse’s comments also suggest that AI will be most useful when it is grounded in real fleet experience rather than generic technology claims. Geotab’s model has been to develop products alongside customers, using fleet problems to guide the capabilities it builds into its broader platform.
“You can’t sit in your ivory tower as a technology company and imagine what people need,” Cawse said.
“The only way you really know is by working very, very closely with fleets.”
That customer-led approach may become even more important as AI tools move from reporting on fleet performance to recommending, and potentially automating, operational decisions.
Cawse said Geotab is already hearing examples of fleets using AI to determine how they should optimise their operations.
“There are some interesting examples where decisions that have been reported to me about what they do with their fleets, and how they optimise their fleets, it was really the AI telling them what to do,” he said.
“That never happened before, and is happening now.”
The future of AI in fleet management will not remove the need for experienced people, but it could change where their time is spent. Rather than searching for issues across large volumes of data, fleet managers may be able to focus more on validating recommendations, managing exceptions and implementing improvement programs.
For Geotab, the ambition is clear: use AI to make sophisticated fleet-management capability available to organisations that have never had the scale or resources to build it themselves.
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