The research, conducted among more than 230 supply chain and logistics executives in Europe and North America, paints a clear picture. AI applications are shifting from experiments to structural deployment within transport management systems (TMS), with direct impact on planning, pricing, and execution.
From analysis to real-time decision-making
In the current generation of logistics IT landscapes, AI plays an increasingly active role. Shippers use AI models to improve transport planning and network optimization, while carriers deploy the technology for dynamic pricing, route optimization, and tracking. The focus is no longer on reports afterwards, but on decisions during execution.
This shift requires systems that can process large amounts of operational data and combine it with external sources, such as traffic information, weather data, and capacity data from the network. AI acts as a layer on top of existing TMS and visibility platforms, where algorithms continuously calculate scenarios and propose alternatives.
Planning, pricing, and execution as primary AI domains
According to the report, transport planning, pricing, and operational execution are the domains where AI will have the greatest impact in the coming three to five years. Shippers especially expect gains in planning logic and network design, where AI helps to understand complex dependencies and disruptions more quickly.
Carriers emphasize pricing models and corridor optimization. In a market with fluctuating volumes and margin pressure, it becomes increasingly important to dynamically align rates with demand, capacity, and operational costs. AI-driven models make it possible to continuously recalibrate pricing rather than periodically.
Agentic AI: systems that act, not just advise
A notable development is the rise of so-called agentic AI. These are autonomous software agents that not only generate insights but can also independently execute actions within predefined frameworks. Think of automatic ETA recalculations, sending alerts for deviations, or proposing rerouting in case of disruptions.
In logistics environments, AI thus shifts from decision support to decision execution. However, full autonomy remains limited for now. Most organizations consciously opt for a human-in-the-loop architecture, where AI prepares or executes decisions, but human operators can intervene when necessary. This increases trust in systems and limits operational risks.
Data quality as a technical bottleneck
Despite the rapid adoption, data quality remains a structural problem. Inconsistent data, different data standards, and silos between systems limit the effectiveness of AI models. Both shippers and carriers cite this as the main technical barrier to further scaling.
AI models are, after all, only as reliable as the data on which they are trained and fed. Without uniform data definitions, real-time integrations, and robust data governance, AI remains stuck in local optimizations. The report therefore emphasizes the importance of integration architectures and data flows that work across organizations and supply chains.
Network-based TMS as an accelerator
The combination of AI with network-based TMS platforms proves crucial. In a connected ecosystem, algorithms can recognize patterns across multiple parties, leading to more accurate ETA predictions, better risk management, and more efficient matching of loads and capacity.
For carriers, this translates into higher asset utilization and fewer empty kilometers. For shippers, it means more predictability and control over the supply chain. The added value lies not only in the AI algorithms themselves but in the scale and connectivity of the underlying platform.
Changing role of logistics IT teams
The deployment of AI also changes the role of logistics IT and operations teams. Planners and dispatchers are increasingly evolving into managers of automated decision systems. Instead of manually planning, they monitor exceptions, direct AI agents, and evaluate output for reliability and compliance.
This requires different competencies: understanding data models, system integrations, and AI logic becomes at least as important as operational knowledge. At the same time, this shift makes it possible to manage more complex networks without linear growth in personnel.
AI becomes infrastructure in logistics
The Transportation Pulse Report 2026 shows that AI in logistics is quickly shifting from innovation project to infrastructure layer. The technology increasingly determines how systems plan, price, and execute. Organizations that invest in data quality, integration, and scalable architectures thus create a technical foundation for further automation.
AI is therefore no longer a standalone tool but a structural part of the logistics IT landscape. The question for IT and logistics professionals is not whether this development will continue, but how quickly their systems are ready to work with it.
