- Region and Language
- Region and language
Fleet technology is entering a new phase driven by physical AI, where intelligence is embedded directly into vehicles, robotics, and infrastructure. This shift enables fleets to operate more autonomously, learn continuously across assets, and reduce reliance on manual processes. Machine learning, robotics, and connectivity are transforming fleet operations. From long-haul transport to yard management, fleets are becoming more efficient and cost-effective. For fleet leaders, understanding and adopting these emerging technologies is critical to staying competitive.
Physical AI enables fleets to learn and improve in real time across all vehicles and assets.
Autonomous and robotic systems are expanding beyond driving into yard, safety, and facility operations.
Labor cost reduction and efficiency gains are major drivers of fleet technology adoption.
Infrastructure, connectivity, and regulation will determine the pace of adoption across regions.
Physical AI enables fleets to learn and improve in real time across all vehicles and assets.
Autonomous and robotic systems are expanding beyond driving into yard, safety, and facility operations.
Labor cost reduction and efficiency gains are major drivers of fleet technology adoption.
Infrastructure, connectivity, and regulation will determine the pace of adoption across regions.
Fleet leaders have heard plenty about AI over the past few years, but what’s coming next goes far beyond software.
In the latest episode of The Fleet podcast, futurist and innovation strategist Colin Dhillon touches on the era of physical AI. This is where intelligence moves from the cloud into machines, vehicles, and robotics operating in the real world.
For fleets, that shift goes well beyond the theoretical as Colin explains. “It’s a fundamental change in how work gets done, decisions get made, and goods move.”
At its simplest, physical AI is the combination of artificial intelligence with hardware such as vehicles, robots, and machines that can sense, learn, and act in real environments. Unlike traditional systems that rely on rules-based programming, these systems are increasingly powered by neural networks that learn and improve over time. “There are no more lines of code going into some of these new physical AI devices,” Colin explains. “If you’ve got a fleet of autonomous vehicles, that intelligence suddenly goes directly to every member of that fleet.” For fleet operators, that means:
Vehicles that continuously learn from each other.
Faster performance improvements across entire fleets.
Reduced reliance on manual updates or reprogramming.
In other words, fleets are becoming adaptive systems, not just assets.
One of the most important takeaways for fleet leaders is just how quickly this space is evolving. What seemed advanced six months ago is already outdated.
That acceleration is driven by three converging forces:
Advances in AI and machine learning
Improvements in computing power (including edge and autonomous computing)
Breakthroughs in robotics and sensor technology
The result is a rapid shift from experimental deployments to real-world applications, especially in logistics, transportation, and industrial operations.
Autonomous fleets are closer than you think Autonomous vehicles have been discussed for years, but now the conversation is changing from if to how soon. According to Colin, the fleet technology itself is largely ready, especially for controlled or semi-controlled environments like:
Long-haul trucking routes
Ports and intermodal yards
Mining and private road networks
Distribution and fulfillment centres
“The technology in 2026 is ripe and ready to be let loose,” he notes. What’s holding things back is infrastructure, regulation, and edge-case challenges.
Navigating northern roadways in the winter can be challenging in the best of times. Fleet leaders operating in regions like Canada or the northern U.S. understand this well.
Snow, poor visibility, and inconsistent infrastructure create challenges for autonomous vehicles (AVs) that go beyond controlled test environments.
But solutions are emerging:
3D mapping enables vehicles to “see” beyond visible lane markings.
Smart infrastructure can support navigation and decision-making.
Simulation environments allow systems to train for edge cases.
Autonomous operations may not arrive evenly, but they will arrive.
One of the most immediate impacts of physical AI on fleets goes beyond driving into other day-to-day operations. From yard operations to safety compliance, robotics are starting to take on more dangerous tasks that often put humans at risk.
“There are talks of these small autonomous mobile robots (AMRs) that would drop down and place cones. Or even humanoids traveling with the truck,” Colin says.
One of the biggest implications in this transformation is economic. Labour is one of the largest cost drivers in fleet operations. Physical AI introduces a fundamentally different model. “Imagine removing that cost of labour and replacing it with something that’s a fraction of the cost,” Colin explains. For fleet leaders, this means:
New operating models
Different asset utilization strategies
Reimagined workforce structures
Fleet technology alone won’t define success in this transition. Infrastructure will play a key role. One of the strongest points Colin makes is that fleets don’t operate in isolation. They depend on:
Road networks
Connectivity (including true 5G and beyond)
Smart city infrastructure
Colin highlights a critical gap in that many regions still lack the connectivity required for fully autonomous operations at scale.
At the same time, some global markets are moving faster by deploying advanced infrastructure and even “lights-out” manufacturing environments with no human presence.
For fleet leaders, this creates a strategic question:
Do you wait for infrastructure to catch up or partner and pilot in environments where it already exists?
Not all industries will adopt physical AI at the same pace. Fleet and logistics operations are uniquely positioned to move early because they face ongoing labour shortages, especially in long-haul driving, operate in structured and repeatable environments, and have a clear return on investment from automation.
“I think the fleet sector’s probably going to be one of those to convert the quickest,” Colin notes.
That makes this less of a distant trend, and more of an imminent competitive shift.
The biggest risk right now isn’t adopting too early, it’s getting caught unprepared. Here are a few practical steps fleet leaders can take today:
1. Start with controlled environments
Focus on areas where autonomy and robotics are already viable such as:
Yards
Warehouses
Dedicated routes
2. Invest in digital infrastructure
Data, connectivity, and system integration will be just as important as vehicles themselves.
3. Rethink your operating model
Begin exploring how automation changes things like:
Cost structures
Asset utilization
Workforce roles
4. Monitor regulatory and infrastructure developments
Adoption will vary by region. Staying informed is critical to timing investments.
5. Build internal awareness and alignment
This transition is more of a strategic shift than a technology shift. Leadership teams need a shared understanding of what’s coming.
Physical AI represents a turning point for the fleet industry. It’s all about smarter systems, new economics, and a fundamentally different way of operating. There are still challenges to solve, from infrastructure to regulation to workforce transition. But the direction is clear.
For fleet leaders, the question isn’t whether this transformation will happen. It’s whether you’ll be ready when it does.
Talk to our trusted experts about preparing your fleet for AI.