Unlocking fleet efficiency: How agentic AI transforms transportation management for heavy-duty truck fleets

Organizations with transportation fleets are increasingly hearing about the benefits of artificial intelligence—but not all AI is created equal. Two distinct types of AI are currently in use: generative AI and agentic AI. And while generative AI excels at creating content and predictions, agentic AI takes it a step further by acting on insights, automating decision-making, and performing tasks with minimal human input.

For organizations managing heavy-duty truck fleets, the distinction isn’t just technical—it’s operationally critical. Agentic AI can autonomously monitor vehicle health, reroute deliveries in real-time, and optimize load planning without requiring human direction. This level of automation and intelligence could modernize logistics, reduce downtime, and improve overall fleet efficiency.

As the transportation industry seeks smarter, faster, and more adaptable solutions to address rising costs and supply chain challenges, understanding and adopting agentic AI may give organizations with heavy-duty truck fleets a significant competitive edge.

Generative AI vs. Agentic AI

Generative AI, like ChatGPT, or image-based tools such DALL-E are engineered to generate new content based on historical data. It’s great at producing documents, marketing materials, or predictive insights when given the right prompts, but it’s reactive by nature and dependent on human direction.

Agentic AI, on the other hand, is built for action. Unlike generative models, agentic AI can independently assess real-time inputs—like telematics data, weather conditions, or delivery delays—and then execute decisions based on organizational goals. Whether it’s rerouting trucks to avoid bottlenecks, scheduling maintenance before a breakdown, or dynamically adjusting delivery plans, agentic AI operates with autonomy and purpose.

For organizations with transportation fleets navigating complex logistics, labor shortages, and razor-thin margins, the difference is this: generative AI can help you plan; agentic AI can help you do—faster, smarter, and with far less manual oversight.

What agentic AI means for companies with transportation fleets?

For organizations with transportation fleets, such as private fleets, the emergence of agentic AI represents a major leap beyond traditional analytics and dashboard-driven insights. Private fleets often face complex logistical challenges, including route optimization, fuel efficiency, maintenance scheduling, and real-time decision-making in response to changing road conditions or unexpected events, on top of their retail operations, for example.

Companies with transportation fleets have increasingly utilized data analytics, incorporating some form of AI, in various aspects of their operations. For example, a fleet recently consolidated five separate platforms into a single, internally developed solution powered by AI to optimize route planning. By analyzing historical traffic patterns, weather data, and delivery schedules, the system reduced fuel consumption and improved on-time deliveries—all without manual intervention at every decision point. This is the kind of operational lift agentic AI makes possible.

Beyond logistics, predictive maintenance is another key area where agentic AI is gaining traction. These systems continuously monitor telematics data to forecast component failures and schedule proactive maintenance, reducing unplanned downtime and extending asset life.

Agentic AI is also influencing strategic areas like truck procurement, leasing, and financing, in the areas of analyzing market trends, assessing vehicle depreciation rates, and optimizing fleet composition. However, this does not include negotiations with OEMs, finance partners, custom fleet specs, etc. Those components are still best discussed as a team, backed by data-driven decisions.

Today’s organizations are still hesitant to go all-in on the use of AI for procurement decision-making, as only 19% said they are very confident in this area. This is most likely because approximately 24% of respondents expressed concern about data accuracy from AI systems.

Trusted asset management partners today are combining machine learning with proprietary, gated fleet data to predict the total cost of ownership. This includes the procurement of different vehicle makes, models, types, and specs, and helping these companies make informed decisions about whether to buy, lease, or finance their fleet assets and when it is the most optimal time to do so.

As an example, these asset management partners are constantly scrutinizing gated data from machine learning models and analytics that first process extensive data from various sources, including:

Vehicle specifications (make, model, year, engine type)
Operational data (mileage, fuel consumption, route information)
Maintenance and repair records (repair history, part replacements)
Financial data (purchase price, interest rates, tariffs, depreciation rates)
External factors (fuel prices, market conditions, government regulations)

These partners and their analysts then leverage data analytics and algorithms to process this gated data to identify key findings that influence TCO:

Truck specs based on safety, fuel efficiency, and utilization
Maintenance and repair frequency and costs
Depreciation rate and resale values
Local utilization patterns (e.g., long-haul vs. short-haul routes)

Using this processed data, the models are then trained on gated historical TCO information for various truck models and operational scenarios.

How agentic AI is redefining fleet management

The introduction of agentic AI isn’t just another layer of automation; it’s a fundamental shift in how companies process and utilize data for their fleet operations. Agentic AI has the power to autonomously manage many aspects of fleet operations in real time. For instance, an agentic AI system could continuously monitor and adjust routes based on real-time traffic conditions, weather changes, and even unexpected road closures, making instant decisions to reroute vehicles for optimal efficiency.

Yet despite its potential, adoption is still early. While 95% of companies consider AI critical to operations, only 19% are currently using agentic AI systems, according to a recent survey on the use of AI in the transportation industry.

For maintenance and vehicle health, agentic AI can leverage data from multiple sources—including onboard sensors, historical maintenance records, and even external factors like road conditions and weather patterns. Better yet, these systems can auto-schedule service, order parts, and coordinate downtime—ensuring repairs happen with minimal disruption to delivery timelines. This is also important since 62% of survey respondents said they would like to utilize agentic AI for their maintenance operations.

Why the correct data still matters

As powerful as agentic AI may be, its effectiveness hinges on the quality and reliability of the data it processes. Today’s leading organizations have quickly realized the value and importance of “gated data,” which refers to high-quality, verified information that has been carefully curated and protected.

The importance of data quality becomes evident when considering the potential consequences of using bad or unreliable data, even leading to hallucinogenic AI outcomes. For organizations with transportation fleets, inaccurate or outdated data could lead to poor decision-making with far-reaching implications. For example, if an organization is leveraging agentic AI with inaccurate fuel consumption data, it might lead to suboptimal route planning, increased fuel costs, and potentially missed delivery deadlines—insight that is also identified by human expertise.

The same goes for financial planning and asset management, as having computers rely on inaccurate data without any human oversight could lead to misguided procurement decisions, inefficient resource allocation, and ultimately, a negative impact on the company's bottom line. If the data feeding those decisions is flawed—whether from incorrect vehicle specs, outdated market conditions, or incomplete maintenance histories—fleets could find themselves stuck with underperforming assets or poorly timed capital investments.

Organizations with transportation fleets would be wise to explore the opportunities that both generative and agentic AI can offer them. However, in the long term, the potential for data-driven mistakes can erode a company's competitive advantage and financial stability. It is critically important to have access to trusted partners that can continue to oversee the impact these machines have on decision-making, as well as access to only the most trusted gated data for extreme accuracy.