Data, AI and the future of fleet decision-making

Fleet managers don't have a data shortage. They have a decision-making challenge.

Telematics, fuel and charging transactions, maintenance records, downtime, compliance and driver data already generate an enormous amount of information. The opportunity now isn't simply to collect more data, but to connect it, understand it and turn it into action.

This was a common theme running through the data and AI discussions at the AFP Expo 2026. While the conversations covered different topics, a consistent message emerged: organisations are seeing the greatest value when they move beyond gathering information and focus on turning insights into practical decision-making.

It's easy to become overwhelmed by dashboards, reports and multiple supplier platforms. But data only becomes valuable when it's connected to a clear objective.

The question shouldn't be "what can this data tell me?" but "what decision am I trying to make?" That could be deciding which vehicles to replace, identifying where downtime is eroding productivity, assessing whether an EV transition is delivering the expected savings, or determining where policy intervention may be required.

Whether the priority is improving driver safety, reducing operating costs, strengthening compliance or supporting decarbonisation goals, fleet teams need a clear understanding of what they're trying to achieve before deciding which data matters most.

That focus helps cut through complexity and ensures reporting supports decision-making rather than becoming an administrative exercise.

Many organisations are dealing with data from multiple internal and external sources, often spread across different systems and providers.

The challenge is rarely that the information doesn't exist. More often, the relevant information sits across different suppliers, systems and datasets, each providing only part of the picture.

The real value comes when those sources can be brought together consistently enough to create a trusted view of what is happening across the fleet.

Without that visibility, insights become harder to act on and recommendations become more difficult to defend. As fleet operations become increasingly data-driven, the ability to connect and interpret information from multiple sources is becoming just as important as collecting the data itself.

The rapid adoption of AI reflects a simple reality: fleet managers are constantly looking for ways to spend less time on administration and more time on strategic activities.

Across the discussion, examples ranged from streamlining reporting and analysing large datasets to identifying operational trends, highlighting exceptions and surfacing risks that may otherwise go unnoticed.

The first wave of AI will undoubtedly remove administration. The bigger opportunity is what comes next: using AI as a decision-support layer across the fleet.

Rather than asking a fleet manager to interrogate five dashboards, AI could identify the exception, bring together the relevant information, explain what may be driving it and suggest the actions worth investigating.

The real opportunity isn't replacing fleet expertise. It's enabling teams to work more efficiently, helping them prioritise attention where it can have the greatest impact.

Historically, reporting has focused on telling organisations what happened. Increasingly, businesses are looking for tools that can help explain why something happened and what should happen next.

A future fleet platform shouldn't simply show that downtime has increased. It should be able to identify the vehicles, manufacturers or repair types driving the change, quantify the operational and financial impact, and surface the interventions most likely to improve the position.

As data becomes better connected and AI capabilities continue to evolve, fleet teams will be able to move beyond static reports towards more proactive decision-making.

The direction of travel is from fleets searching for answers to the technology surfacing the questions that need attention.

The objective isn't to remove the fleet professional from the decision-making process. It's to give them a dramatically better starting point.

Data provides the evidence. AI can accelerate analysis and surface potential actions. But experience, commercial judgement and operational context determine what should actually happen.

As AI capabilities continue to evolve, the organisations that see the greatest value are likely to be those that combine technology with human expertise. The ability to interpret insights, balance competing priorities and make informed decisions remains firmly in the hands of fleet professionals.

Many assume that AI helps with data analysis. It is much more than this, AI fundamentally changes the interface between fleet data and fleet decisions. This is completely aligned with the sort of TCO+, connected-data and decision-support work we have been offering to customers for the past decade. With the introduction of AI, we can now do this at greater depth and greater scale.

This article reflects industry perspectives and does not constitute financial or investment advice.