Let’s be real: as a fleet owner or manager, you’ll be hard-pressed to think of a modern fleet management software that doesn’t have “AI-powered” written all over it. But how do you know it isn’t all smoke and mirrors? What does it do exactly and how does it solve your fleet’s problems? These are questions we hear every day, and they deserve concrete answers.
Let’s try and break it all down in simple, straightforward terms — so without the usual fluff and jargon.
Fleet management has changed dramatically in recent years, and at the center of this transformation is an unprecedented influx of data. Think about it: every truck, bus or trailer generates about 25 gigabytes of data per hour. That’s a goldmine of untapped opportunity and insight, waiting to be transformed into efficiency gains and cost reductions.
The problem? Today’s fleets are running leaner than ever. Fleet managers and dispatching teams are expected to do more with fewer resources. Margins are razor thin. Who has the luxury of employing massive teams to sift through terabytes of data every day?
“What makes AI so powerful in a fleet management context is that it can interpret vast amounts of data with a speed and efficiency that would likely be impossible for human teams to achieve on their own,” says Ashish Pathak, our Head of Data & Platform Business Line. “This gives fleets a proactive edge — from anticipating breakdowns before they happen and preventing costly accidents, to reducing fuel consumption and more.”
As margins continue to tighten and operational complexity increases, this won’t be a matter of option, but one of necessity. More and more fleet operators will rely on AI applications to scale up their operations, control costs, and stay ahead of the competition.
Before we dive into real examples of how AI can improve fleet operations, let’s address a common concern: how can you tell whether a solution can deliver on its AI promises or is simply using the term to ride the marketing buzz?
Hjalmar suggests you ask a lot questions: “If a fleet management software provider can’t tell you what benefits and challenges you can expect or refuses to outline what their AI actually does and what data it uses, you might be looking at a red flag. Quality AI providers do not hide behind claims of complexity or confidentiality and would be happy to describe their approach in terms you can understand, without revealing company secrets.”
What if you could anticipate failures before they happen and schedule maintenance when it’s most convenient to you? What if you could prevent common issues from putting your drivers at risk, depleting your maintenance and repair budgets, and delaying your deliveries?
AI systems monitor and analyze readings from various vehicle sensors and compare them to historical failure data, looking for patterns that indicate upcoming failures. For example, SCALAR’s brake performance monitoring solution uses thousands of trailer braking events and a big data algorithm to estimate braking performance over time and detect issues before they become major problems. To give you another example, a new capability which we’re now developing for the SCALAR trailer health monitoring system analyzes braking patterns, supply pressure events, load weights, driver behaviors, and other data to predict an air leakage in the trailer’s pneumatic system and pinpoint its location before the trailer arrives at the workshop.
This means you can:
With fuel costs accounting for up to one-third of operational expenses, fuel consumption optimization directly impacts profitability. Not to mention the pressure of meeting sustainability goals.
But what makes one trip more fuel-efficient than another? Is the shortest route always the most economical option?
AI-based energy optimization tools dig deeper in the data to uncover hidden correlations and fuel-saving opportunities that conventional systems might miss altogether. “Our innovation teams are developing AI capabilities in SCALAR to support our customers’ fuel efficiency and sustainability goals,” says Ashish. “For example, we’re building machine learning models that assess the impact of driving behavior, load, and environmental factors on fuel consumption. This allows us to recommend specific operational actions within our product to help customers reduce their carbon footprint.”
“The rise of mixed and all-electric fleets adds a new layer of operational complexity,” he adds, “so we aren’t just focusing on conventional fuel, but on energy optimization as a whole. For electric vehicles, we’re using machine learning to predict driving range, address consumption-heavy driver behaviors, and plan charging stops.”
Using computer vision algorithms, these systems can instantly recognize visual data from the road (vehicles, people, lane markings, driver behaviors, and more) to understand context and make decisions.
For instance, our AI-powered video cameras continuously analyze this data to detect safety incidents during driving — things like harsh braking, sharp turning, near forward collisions, or tailgating. These events trigger the camera to automatically record and upload the relevant footage to the cloud. The camera’s built-in AI awareness models automatically recognize and blur human faces and other privacy-protected data — simplifying compliance with GDPR or other data protection laws while respecting drivers’ right to privacy.
Why AI risk detection matters? For one, by alerting drivers, the system can help reduce accidents. Something your drivers may appreciate. Your insurance provider surely will.
In addition, you can use the footage to create personalized coaching sessions, provide evidence in the event of insurance claims, or protect your drivers and cargo from false liability claims.
AI automates administrative tasks that require basic reasoning and decision-making — tasks that, until now, couldn’t be done without human intervention.
One such example is fuel card data mapping. Using Large Language Models (LLMs), AI can read and interpret fuel card transaction data coming in many different formats and languages, extract all relevant information, and automatically convert it into a standardized format that can be easily processed by our fuel apps for tracking fuel consumption, monitoring spending patterns, and preventing fraud.
This shift to intelligent automation has immediate efficiency gains. It frees up your team from mundane yet necessary manual tasks — saving significant amounts of time and preventing errors and inconsistencies.
In a nutshell: it’s data. But that’s only part of the story.
This involves a lot more than building a model that can find patterns in data. It means investing in a powerful data quality and monitoring framework. It means knowing what data to capture and how often, understanding what these measurements mean and how they interact with one another, and designing purpose-built models tailored to the specific circumstances of fleet operations.
Being part of ZF, SCALAR has access to in-depth, first-hand knowledge of how vehicle components behave and interact with each other (just think: every new truck has at least one component in it manufactured by ZF — from braking systems to transmission kits to trailer technology). This means our AI models do not just learn from fleet data, but from combining that data with the unique insights derived from all the ZF components operating on roads across the world.
“Our data scientists work directly with the experts at ZF who design and manufacture this technology. Together, they understand better than anyone how vehicles operate in the real world, the interrelations between different parameters and the role each data point plays when it comes to building quality models. This kind of insight would be nearly impossible to derive just by looking at the data,” explains Hjalmar.
Fleet operators have good reasons to be excited about the evolution of AI, and so do we. SCALAR is about orchestrating road transport — an entirely new way of looking at fleet management — and AI forms the backbone of that vision. Building on our advanced optimization algorithms and unmatched vehicle intelligence, we’re working towards unifying planning, routing, and dispatching under one intelligent platform that simultaneously optimizes everything automatically and in real-time.
Picture a system where customer orders are instantly turned into optimized trips based on shipment requirements, fuel-efficiency metrics, and real-time conditions. Where maintenance timings automatically align with repair severity, driver schedules, or part availability to minimize costs and maximize uptime.
Imagine a fully automated platform that dynamically shifts things around in case of unexpected changes or delays, to ensure goods always arrive as scheduled.
“With every new AI capability we build, we aren’t just offering an immediate solution — we’re laying the path for fleets to transition to a fully orchestrated future where information silos, roadside surprises, and last-minute crisis management are a thing of the past,” Hjalmar says. “Our teams are committed to pushing the boundaries of what’s possible when artificial intelligence and human strategic direction work together as one to deliver exceptional results.”
The AI revolution in fleet management is no longer a dream of the future — it’s already making an impact in the here and now. And, as the industry prepares to take on the challenges that lay ahead, the competitive edge is shifting to those who make the most of what it has to offer. If you haven’t yet considered implementing AI to future-proof your fleet operations, now is a good time to do so.
SCALAR helps you run your fleet today and makes it easy to navigate the shift when you’re ready. With AI-driven insights, we’ll help you adapt at your own pace — staying ahead without the stress. To learn more about how we use AI and how SCALAR helps make your fleet safer, greener, and more profitable, get in touch with us today for a free consultation.
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