AI in freight forwarding software and commercial vehicles: What are the real benefits?
Artificial intelligence is making its way into dispatch, fleet management and the driver’s cab. But what about it is actually intelligent – and what actually brings a freight forwarder hard cash at the end of the day?
The good news is that AI is no longer just a pipe dream in the transport industry. The less good news is that not every application marketed under the ‘AI’ label actually justifies an investment.
AI as a digital dispatch assistant
The greatest potential currently lies in freight forwarding software. Modern TMS systems can link together orders, vehicles, drivers, capacities, time slots, driving and rest periods, as well as up-to-date traffic information, and automatically generate route suggestions. If the situation changes – for example, due to a traffic jam, a vehicle breakdown or an additional order – the software can recalculate the schedule.
What is interesting here is not so much fully autonomous dispatch as the digital assistant. The AI makes a suggestion, identifies conflicts and provides alternatives. The dispatcher makes the final decision. This makes sense, not least because, in practice, a great deal of information is not neatly stored in databases: a dispatcher knows from experience, for example, which driver gets on particularly well with which customer or at which loading bay.
Tangible benefits are now evident in route optimisation. The Association of Freight Forwarders and Logistics (VSL), for instance, points to AI-supported route optimisation, which is designed to allow for additional stops or orders within the existing time budget.
Automated documentation and communication
Less spectacular applications can pay for themselves even more quickly: AI can automatically parse emails and transport orders, capture freight documents and CMRs, respond to status enquiries or consolidate information from different systems. For medium-sized haulage firms in particular, this can be more appealing than a highly complex ‘fully automated dispatching’ system.
This is because, in this context, AI does not replace decision-making, but primarily automates manual data entry and routine communication. The benefits can therefore be measured relatively easily: less processing time per order, fewer errors and more orders per dispatcher.
What AI is already achieving in lorries
AI has also made its way into the vehicle itself. Cameras and sensors, for example, recognise road markings, vehicles, pedestrians or other obstacles and support emergency braking, lane-keeping and adaptive cruise control systems.
AI is primarily intended to overcome the limitations of traditional driver assistance systems. Conventional lane-keeping assistants can reach their limits, particularly at roadworks or where the road layout changes. AI-based systems promise a better interpretation of the traffic situation in such cases.
Added to this are applications such as driver monitoring, fatigue and distraction detection, and predictive maintenance. Using vehicle, engine and telematics data, the software can detect when a technical problem is looming or when a visit to the workshop is advisable.
However, the same applies here: AI is no oracle. A driver assistance system may misinterpret a situation, a prediction may be off the mark, and an automatically calculated route may be thwarted by the reality of a loading bay.
How reliable is AI?
The quality depends more on the data set than many marketing promises would suggest. Incorrect addresses, incomplete vehicle data, outdated time slots or missing information about customer requirements lead to poor results, even with excellent AI. That is why the most important prerequisite for AI in the haulage industry is not necessarily the most powerful algorithm, but a clean digital data set.
Driver acceptance is also crucial. This was revealed by a survey commissioned by the German Road Safety Council (DVR) in October and November 2025. The study on the use of driver assistance systems shows that 59 per cent of the lorry drivers surveyed do not use their assistance systems consistently. A technically outstanding system is of little use if drivers find it disruptive or regularly switch it off.
Is it worth the investment?
For fleet operators and hauliers, the answer is increasingly: yes – but not by investing in ‘AI’ per se, but rather in a specific economic benefit. Applications where success can be measured directly are particularly interesting: fewer kilometres, better vehicle utilisation, fewer empty runs, reduced planning effort, lower fuel consumption, less downtime or faster processing of transport orders.
On the other hand, projects in which large amounts of data are first collected and complex AI systems installed without first defining a specific bottleneck are not convincing. The rule of thumb could therefore be: first identify the process, then deploy the AI.
For a haulage company with 20 lorries, intelligent route planning alone can already make a significant difference. For a larger fleet, predictive maintenance, automatic order entry, driver assistance, capacity forecasting and intelligent customer communication may also be of interest.
AI will therefore not replace dispatchers and drivers in the short term. However, it will increasingly ensure that both spend less time on routine tasks and more time on those tasks that require experience, communication and human judgement.
