Transport and logistics is not a sector that has to let AI in from outside. Planning software, track-and-trace, route optimisation and warehouse management systems have been running here for decades. That gives an appearance of a head start: there is data, there are systems, there is a culture of measuring and optimising. But the maturity measurement from hybridresourcing shows that this is something different from AI readiness. An organisation can be excellent in operational systems and yet still be at baseline or foundation level on the fundamental dimensions — organisation, IT infrastructure, data management. The question is not whether systems exist, but whether those systems talk to each other and whether the organisation around them is set up to build something new on top.
Within transport and logistics, the ratio between companies varies strongly by type of activity. A party that mainly does planning and coordination, with its own software development and an IT department that can link systems together, often sits closer to activation or insight. A party that mainly executes — driving, loading, unloading — and works with a collection of subsystems from different suppliers, is often still at foundation. The largest part of the sector is somewhere between those two positions, with a fundamental layer that is better developed than in many other sectors, but with data management that lags behind: data often sits in silos per system, per location, per client, without anyone owning the whole.
That places transport in a distinct position relative to sectors where the organisational structure itself is the bottleneck. In professional services, it is often data collection itself that is the problem, because the work largely consists of unstructured communication. In transport, there is in fact a lot of structured data — locations, times, weights, temperatures — but that data is spread across systems that were not built to communicate with each other. That is a different kind of lag than a lack of data; it is a lack of coherence.
In a plot round where different people score independently of each other, a recognisable pattern often stands out in transport companies. Operational management scores the IT infrastructure highly, because the systems they use daily work well for their own process. IT or a CIO scores that same dimension lower, because they know how much manual work is needed to transfer data between systems. That spread is not a measurement error. It is the difference between a system that works for the task it was built for, and an infrastructure that is suitable for building new functionality on top. For AI applications, it is mainly that second question that is relevant, and it is rarely asked during daily operations.
The seven dimensions build on each other, and in transport that build-up is clearly visible. Without a data management layer that brings together information from planning systems, telematics and customer portals, an AI application has nothing consistent to train on or respond to. Companies that skip this step and immediately start an AI pilot on one subprocess — for example predictive maintenance on one type of truck — often find that the result does not scale to the rest of the fleet, simply because the underlying data was not recorded in the same way. That is not a technical flaw in the AI, but a sign that the fundamental layer is not yet complete.
This order does not only apply to transport. In the agricultural sector, the same problem occurs with sensor data that is recorded differently per crop or per company, and in retail you see it reflected in inventory and sales data that sits separately per retail chain or channel. Transport distinguishes itself by the scale of the structured data that already exists, which makes the fundamental step both easier and more tempting to skip — easier because the building blocks are there, more tempting because it feels as though you are already far along.
The maturity measurement from hybridresourcing sets out this pattern across five levels, from baseline to intelligence, spread over the seven dimensions. For a transport company, this typically produces a profile with a strong operational layer and a weaker data and organisation layer, with differences that can be large per business unit or location. That profile says nothing about whether AI will or will not work, and nothing about a timeline. It shows which dimension is currently holding back the others, and that is precisely the information needed before an organisation decides where to start. The tool that performs this measurement is under construction; anyone who wants to work with it once it becomes available can sign up for the waiting list.
This page is about the question of whether the organisation can carry AI — whether the foundations of organisation, infrastructure and data are in order before anything is built on top. That is a different question from which part of the work itself can be taken over by AI. For that question, at the level of individual tasks within planning, transport and logistics administration, there is the work scan from FTE TO AI: it calculates per task which part of it AI can take over, regardless of the level at which the organisation around it finds itself. Anyone who wants to know where the organisation stands starts with this measurement; anyone who wants to know what happens to the work itself will find that in the work scan.
Vraag maar wat er moet staan voordat AI in uw organisatie kan landen.
Answers come from this site’s knowledge base. Not tailored advice, and not a scan of your company.