What do the figures say about the driver shortage in 2026?
Timocom states in its transport market analysis for 2026 that the shortage of qualified drivers and planners remains one of the greatest challenges in the transport sector. New entrants are insufficient to offset the outflow. At the same time, the Dutch transport and logistics sector is growing by approximately one percent in 2026, according to ING figures from February 2026, though differences by activity are considerable. The net result: greater demand for transport capacity, fewer people to serve that capacity. Pressure on the planning department is increasing while headcount is not keeping pace.
Why does this affect planners, not just drivers?
The reflex is to view the driver shortage purely as an HR problem. That is too narrow. Every trip that does not run, or runs late, lands as manual work on the planner's desk. Orders are redistributed, customers are called, systems are updated. Evofenedex noted in December 2025 that companies need to invest in supply chain visibility to absorb disruptions. But that visibility starts internally: knowing what is planned, what is deviating, and which decision needs to be made first. When a planner tracks all of this manually, the planning is not the problem - the data flow is.
How does structured data make a difference when capacity is tight?
When people are scarce, the only sustainable solution is for every available employee to work more effectively. That starts with eliminating manual retyping and searching. Orders arriving by email, PDF, or portal must reach the planning system without human intervention, complete with the correct customer code, address, and special requirements. Deviations during a live day - a late load or a driver who has dropped out - must be immediately visible without anyone having to compare five screens. What AI does here in concrete terms: it handles the preparatory work. The planner still makes the decisions, but does so on the basis of a complete and current picture, not on the basis of what could be looked up just in time.
When is an AI layer over existing systems enough, and when is it not?
Many transport companies run a TMS that was purchased ten years ago. The system does its job, but it was not built to process external data signals or to automatically read orders from emails. In that case, an AI layer around it is a realistic first step: document processing, email interpretation, and status updates can be connected to the existing system via AI Workers, without replacing the system itself. For companies where the TMS itself is the bottleneck - where the system is slowing down, reports are compiled manually, and integrations no longer work correctly - it makes more sense to rebuild the core system. Not as a multi-year project, but as a phased replacement with go/no-go decision points. The right answer depends on where the pain is: at the input level or at the architecture level.
What can you do right now?
Start by measuring. How much time does a planner spend each day manually processing orders and chasing status updates? How many orders arrive through a channel that does not feed directly into the system? Those two questions alone provide an initial picture of where capacity is being lost. The tightness in the labour market will not resolve itself. New flexible labour legislation effective 1 January 2026, cited by Logistiek.nl, also makes it more expensive to offset capacity shortfalls with temporary workers. The path to greater resilience does not run through more people - it runs through better systems for the people already there.