AI in trucking

By Frank Pietras, Daniel Rohrhirsch and Wilfried Aulbur

The trucking industry faces compounding structural pressure. Tight margins, persistent driver shortages, and total cost of ownership focus are forcing operators, OEMs, dealers, and service providers to look for new performance levers. Artificial intelligence is increasingly positioned as one of those levers, but experience across industries shows that technological potential does not automatically translate into bottom-line impact. To move from potential to performance, companies need a clear-eyed view of where AI genuinely delivers, what it costs to implement, and who in the ecosystem stands to benefit.

To answer these questions, we surveyed approximately 60 industry insiders across Europe in summer 2026, representing the full breadth of the trucking value chain. More than three-quarters of respondents were either decision makers on AI use cases or directly involved in implementation. The findings point to a clear near-term imperative, but also to significant variation in where value is created and who is positioned to capture it.

Maturity varies sharply across the value chain

Not all AI use cases in trucking are at the same stage of development. Our analysis organizes applications into three dimensions: "Acquire," "Operate," and "Maintain & Repair" – each with a distinct deployment profile.

"Operate" is the most mature dimension, building on established data availability and well-tested applications in routing, load matching, and driver coaching. "Maintain & Repair" shows strong deployment readiness, with predictive maintenance emerging as one of the most reliable early value generators across the industry. "Acquire" – which spans vehicle configuration, pricing, and residual value assessment – is at an earlier stage overall, though specific use cases already offer near-term potential.

This uneven maturity profile has a direct implication for investment decisions: the priority order for AI deployment should reflect both the potential of individual use cases and the current state of data readiness and solution quality in each area.

Where AI is already delivering results

Within each dimension, specific applications are already generating measurable outcomes. In "Maintain & Repair," AI-driven component failure prediction – drawing on engine, transmission, and brake system sensor data – enables maintenance to shift from a reactive to a proactive model, with direct implications for vehicle uptime and asset lifespan.

In "Operate," AI-powered load matching addresses one of the industry's most persistent cost drivers: empty miles. By continuously matching available carrier capacity with freight demand and adjusting pricing dynamically, machine learning algorithms can recover efficiency that manual processes routinely leave unrealized. The improvement in coverage speed is equally significant, reducing a process that can take hours to minutes.

In "Acquire," AI-based used truck valuation offers a more immediate value capture opportunity than most early-stage applications. Real-time valuations that account for vehicle condition, comparable market data, mileage, and regional demand can materially improve pricing accuracy. The direct profit impact per transaction is measurable, and integration into dealer and leasing workflows is already under way among early adopters.

Who captures the value and how

Positive AI impact extends across much of the trucking ecosystem, but the nature of that impact differs considerably by player. Our analysis distinguishes between players positioned for direct AI monetization and those for whom AI is primarily an efficiency and margin-defense tool.

Truck OEMs and independent telematics and FMS providers are best placed for direct monetization. Their data positions – spanning multiple customer relationships and, in the case of FMS providers, cross-OEM fleets – give them a structural advantage in developing and scaling AI-powered products and services. For truck OEMs, this means translating vehicle data into uptime services and predictive maintenance subscriptions. For telematics and FMS providers, it means building fleet intelligence as a standalone product.

For fleet operators, freight forwarders, authorized dealers, and other players, the opportunity differs in character but not in significance. These players can use AI to reduce operating costs, improve service reliability, and compete more effectively, but the path to value runs through organizational readiness: data architecture, redesigned workflows, clear decision rights, and a workforce equipped to act on AI outputs.

The implementation gap and what it means for timing

More than 90 percent of our survey respondents expect AI to have a significant or transformational impact on trucking within the next three to five years. Among independent telematics and FMS providers, the share expecting transformational change is even higher. Yet the gap between expectation and realized value remains wide across the industry. The most frequently cited barriers – software compatibility, solution quality, and cost – are implementation challenges, not technological limitations.

The strategic conclusion is clear: companies that have moved beyond isolated pilots and invested in robust data foundations are already beginning to define a new cost and service benchmark for the industry. For those still in the experimentation phase, the window to lead that shift – rather than respond to it – is narrowing. Our full study sets out the specific actions different players should prioritize to close the implementation gap and convert AI's potential into a durable competitive advantage.

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