The conversation around how much the fleet industry is evolving, especially in regards to what information fleets can now access and the technology to modernize the fleet, is nothing new. But it’s the fact that there have been so many advancements and fleets have to stay ahead of the learning curve if they’re going to adopt them, that these conversations will continue. 

At Samsara’s inaugural Go Beyond Public Sector event, the idea of change and how it relates to how fleets are using data while incorporating new technology into the fleet followed a similar path, focusing more on the idea of ‘what can this do for me’ rather than ‘let’s add this because it’s new.’

So what were some of the key discussions? Below is a breakdown of some key observations around data, technology, and moving beyond the pain points that connect them to fleet. 

AI Analysis and Data Are Changing Everything

AI, much as it has for the past few years, led much of the conversation around what government fleets are trying to accomplish. Working within a public entity requires a mix of day-to-day planning and long-term improvement, and fleet leaders are being asked to make decisions with more information than ever before.

With what can only be described as a hectic workload, many fleets are adopting AI technology to get a better finger on the pulse of their operation. The areas where it can be used continue to expand, including safety, infrastructure monitoring, and road maintenance.

AI can now be used to detect driver drowsiness and identify issues such as potholes. The goal is to use that information in a way that allows agencies to act before a problem becomes larger.

This technology is still developing, but fleets should start planning for a future where smarter systems are connected across communities and where there is more real-time awareness of what is happening in the field.

But this intersection of data and AI that fleets are facing means fleets have to now incorporate the information they’re receiving in a way that is proactive rather than reactive. For fleet leaders, the value is not just in collecting more data, it's in deciding which data points should trigger action.

A camera catching a pothole, a vehicle flagging risky driver behavior, or an asset showing signs of an upcoming failure only matters if the agency has a process for using that information. That means the conversation around AI has to include everything, from workflow to staffing, and how quickly a department can respond once the system identifies a problem.

As Tim Nagy, SVP of sales engineering at Samsara, explained, it’s using that info to stay ahead of the curve, such as recognizing a pothole that a vehicle’s camera captured and reporting it so it can be filled before a civilian needs to call 311.

Understanding the Unique Needs and Missions Within the Operation

Every fleet has different pressure points and for some, the immediate need may be improving driver safety. For others, the bigger issue may be theft, maintenance costs, downtime, or reducing fuel consumption.

That means the conversation around technology has to start with the mission of the operation, not the appeal of the tool itself. Shane Burns, transportation director for Garden City Public Schools, encouraged fleets to focus on the “why” before getting caught up in the excitement of something new.

In other words, the question should not be whether a piece of technology looks useful. It should be whether it solves a specific problem for that fleet. If the goal is reducing fuel consumption, then fleets need to know how that tool will support that outcome. If the goal is lowering downtime, then the technology has to connect back to maintenance planning and vehicle availability.

This is especially important in the public sector, where budgets are limited and each investment has to be tied to service delivery. A new system may offer several capabilities, but fleet leaders still need to identify which function matters most for their operation and what result they expect to see.

Turning Safety Data Into Operational Value

Safety was another major part of the discussion, especially because small changes in driver behavior can create savings over time. Safe driving can help reduce crashes, but it can also affect fuel use, tire wear, vehicle condition, and maintenance needs.

Several suggestions focused on using safety-related data in a way that drivers can actually see and respond to. That could include allowing drivers to compete for higher safety scores, recognizing the top five driver scores each month, or creating incentives tied to safe driving performance.

The bigger point was that data needs to be visible enough to change behavior. A score buried in a dashboard may help a manager, but a score that drivers see regularly can create accountability across the operation. For fleets trying to build buy-in, that visibility can matter just as much as the technology itself.

The same idea applies when trying to secure funding. Fleets may need to show results before they can expand a program. One suggestion was to get hands on a camera system, use it to demonstrate the impact, and let the results speak for themselves. In some cases, that may also mean giving up something else in the budget to make room for a purchase.

For experienced fleet leaders, the takeaway is not that every agency needs the same tool, but rather that each investment has to be tied to a clear operational need. Whether the goal is reducing theft, improving safety, lowering maintenance costs, or cutting downtime, fleets need to understand what each system is expected to accomplish and how they will measure whether it worked.