The cost of inefficiency in the supply chain is simply too big to ignore, and the stakes are high.
According to Siemens, the world’s 500 largest companies lose almost $1.4 trillion annually from unplanned downtime, representing a whopping 11% of their revenue. Logistics operations, with their tight delivery windows and high asset utilization, acutely feel this impact.
As supply chains stretch and demand for fast, flexible delivery keeps rising, the pressure is only mounting.
From underserviced fleets to empty mileage and poor routing, the industry suffers from breakdowns in planning and execution that waste time and chip away at profits.
The good news is, these challenges can be optimized.
Optimized maintenance activities
AI is already delivering real efficiency gains across two critical areas that one wouldn’t necessarily consider at first sight: fleet maintenance and transport operations. By enabling quicker decisions, streamlined processes and smarter systems, it’s allowing logistics to move faster, without compromising performance.
Fleet maintenance cannot be ignored when it comes to optimizing operations; it’s a critically impactful area. Vehicles that are overserviced waste resources—not just materials but time. Those underserviced are prone to breakdowns, costly repairs and early replacement by other vehicles. Either way, it’s bad for both business and the supply chain.
Standardized maintenance data for vehicle health
AI offers a more connected path forward, starting with standardized maintenance. Predictive and optimized maintenance are gaining traction, with new industry standards that are pushing AI-driven approaches to the forefront.
At the heart of this evolution is the need for standardized data. Without it, fleets rely on inconsistent or proprietary codes to track service intervals, making it almost impossible to train AI models at scale or share insights across systems.
New frameworks like the Vehicle Maintenance Reporting Standards (VMRS) key code, developed through the American Trucking Association’s Technology and Maintenance Council, are changing the siloed, dated approach. By creating a universal language for tracking maintenance items, they lay the foundation for adaptive, AI-powered decisions, such as when to change oil based on real-world engine load and usage, not arbitrary intervals.
However, to unlock AI’s full potential, the industry needs a shared data foundation: code key standards that act as a common language across fleets, platforms and regions. Some platforms are already building toward that future by developing open, interoperable data models designed for global adoption.
The impact is tangible. AI can identify the “sweet spot” for servicing, reducing wasted time, product and profit from premature oil changes while avoiding unnecessary wear and tear.
Today, maintenance often relies on a dashboard light, but AI enables a future where the vehicle doesn’t just alert the driver: it books its own appointment, sends performance data to a third party and rolls into the shop at exactly the right moment.
Fewer empty miles
Beyond the vehicle itself, AI is transforming how freight is planned, routed and executed. One of the biggest challenges in logistics today is empty mileage: trucks that travel without cargo, burning fuel and time. While some inefficiencies are structural—rooted in geography or how the freight network is organized—many can be addressed with the right technology.
AI-powered systems now analyze real-time and historical data to recommend the most efficient routes, plan multi-stop loads and continuously recalculate in transit to adapt to delays, traffic or weather.
Dynamic load planning, procurement and visibility
Cloud-based platforms are already putting these capabilities into practice, using AI to dynamically match loads with carriers and minimize waiting times at docks. They’re also reducing the strain of just-in-time logistics, where tight delivery windows leave little room for error.
Autonomous procurement tools can now handle transport sourcing with minimal human input, using statistical and symbolic AI to analyze unstructured requests, identify suitable partners and select the best fit across time and cost criteria.
Combined with intelligent load planning tools that maximize truck space and reduce the total number of journeys required, these systems help optimize operations over every mile travelled.
An AI-enabled collaboration
When applied across maintenance, execution and operational processes, AI can help drive significant efficiency gains in the logistics sector.
AI is already showing transformative potential in building an optimized future for the industry.
However, this efficiency depends on shared data, interoperable systems and collaboration between carriers, shippers, OEMs and tech providers. Whether it’s maintenance schedules or routing algorithms, AI only works when it can access reliable data and apply it across a broad enough sample to generate meaningful insights.
That’s why standardization is so important. We’re not just building tools, we’re shaping a smarter ecosystem, one where every decision, whether on the road or in the yard, contributes to a more efficient whole.
AI won’t transform logistics in a single leap. But by focusing on the fundamentals, it’s already reshaping how goods move and how fleets are managed. Because when the industry moves together, we lay the groundwork for a brighter, more resilient future.
