According to Peter Golding, CEO of the fleet software specialist, the rise of large language models (LLMs) such as Claude and ChatGPT has led industry professionals to make direct requests rather than interrogate data using traditional methods.

Golding said: “We have gone through a period of at least two decades when fleet managers have been deluged with an ever-increasing amount of data, with a growing emphasis on finding ways to mine that information to generate understanding.

“Fleet software user interfaces have become ever more sophisticated as a result, with approaches ranging from highly complex systems to our own design, which emphasises key metrics. These can be highly effective, but the new technology offers an opportunity to rethink how we work.”

Instead of using a dashboard to drill down into data and identify relevant findings, LLMs allow users to ask questions in natural language and generate meaningful results.

Golding added: “In a simple example, a fleet manager may currently use a custom dashboard within their fleet software to list all drivers whose fuel economy falls more than 25% short of their WLTP figures. This is relatively easy, but not as easy as asking Claude.

“As the insight required becomes deeper and the questions more technical, the use of AI should offer easier access to a wider range of insights, limited only by the fleet manager’s curiosity.

“In particular, there are likely to be benefits where different functions within a company – not just fleet, but perhaps finance and human resources, too – want to use the available information for different purposes.”

Golding added that it was increasingly likely fleet managers would want to use the LLMs they were accustomed to as the front end of their fleet software, with the system itself effectively becoming invisible in the background.

“This is one direction AI could take in fleet management that offers clear benefits, and we are examining ways to make this happen as seamlessly as possible.”