A tailored AI workshop for transport, freight forwarding, 3PL and courier organisations, focused on the place where your planning, telematics and customs document flows already come together every day. No abstract AI demo, no vendor pitch: a session run by people who build transport software themselves and know how a Transics shipment, an eCMR and a customs declaration relate to each other.
An AI workshop for transport and logistics is a half-day or full-day session that takes a transport, freight forwarding or 3PL organisation through the practical, legal and operational side of AI in goods flows. We deliberately work with examples from your own field: route optimisation between Rotterdam and the German hinterland, ETA prediction in just-in-time chains, dynamic dispatching for couriers, capacity forecasting around Schiphol cargo, customs automation for Brexit flows, eCMR. We do not stick to generic corporate use cases.
The Netherlands is Europe's logistics hub: the Rotterdam and Schiphol mainports, the transit corridor via Venlo and Eindhoven, and the last-mile density of the Randstad. That makes our sector a natural candidate for AI, but also one where mistakes are costly. A misclassified customs declaration causes delays at the terminal, an unreliable ETA breaks a just-in-time appointment, and an algorithm that allocates routes touches directly on the Working Hours Act (ATW), collective labour agreements (CAO) and, under the AI Act, often the high-risk classification. We design every workshop around your own reality, not around a TMS vendor's roadmap, and we are honest about where AI is not the answer, for example when a TMS simply does not have clean master data.
The second distinguishing point: we come from software development, not management consultancy. We build custom software for transport and logistics ourselves, integrate with Transics, Carrierweb, i-teq, Squid and the common TMS vendors, and we know how an EDI flow or customs declaration system works in practice. Which modules we cover, and in what proportion, depends on the audience: planning and customer service focus on hands-on work and prompts, while management and compliance lean more heavily on governance, the AI Act and employment law.
What we do not do: pretend that AI makes your planners redundant. Planning in a transport company is a tightly woven interplay of customer commitments, available vehicles, driving times, customs windows and exceptions the system does not know about. AI does not change the nature of that work; it makes some parts more manageable and other parts more subtle to steer.
Since February 2025, Article 4 requires that all staff working with AI systems have sufficient AI literacy. For transport organisations this goes beyond the IT department: planners, freight forwarders, customer service, fleet managers and management are all covered as soon as they use AI in decisions that affect drivers, customers or shipments. A sector-specific workshop is a pragmatic way to build that literacy for an entire department at once, and to demonstrate it on record. A more general variant can be found in our broad AI business training.
The Dutch and European transport sector structurally faces a shortage of drivers. That puts pressure on planning, driving and rest times, and margins. AI can help, with ETA reliability, capacity forecasting and smarter dispatching, but only when the choices are well founded. We address the question of when an AI dispatch system touches on ATW rules or CAO agreements.
Management is seeing competitors and customers experiment with AI in route optimisation, capacity forecasting and customer service. A management team or board session gives you the framework to make strategic choices without the board first having to work out what an ETA prediction model actually does in practice, and what it means legally and operationally.
Brexit, third-country shipments, sanctions regimes, the rollout of eCMR: paperwork keeps growing, and AI looks like a logical lever. At the same time, this sits within a strict legal framework (Dutch Customs Act, Union Customs Code, sanctions screening) where an incorrectly classified declaration has immediate consequences. For the construction logistics variant, see the AI workshop for construction logistics & construction hubs.
We build custom software for transport organisations and know the difference between a generic SaaS pitch and a real EDI flow with a shipper, a Transics integration or an AGS customs declaration. Our examples come from real transport work, not a generic corporate sample set.
No partnership with a TMS vendor, AI platform or consultancy firm. We give you an honest picture of what ChatNL, Copilot, Claude or a specialist transport AI means for your situation, including data residency and the question of whether an EU-hosted or self-hosted solution makes more sense.
What we advise is in line with how we work ourselves: code ownership stays with your organisation, no vendor lock-in, transparent data flows. For customers, insurers and regulators that is an easier story than an AI black box, especially when a driver, customer or trade union asks for an explanation of an algorithmic decision.
These blocks form the building blocks. We combine them depending on the target group, the format (half day, full day or multi-day) and the scoping conversations beforehand. No fixed script, but a solid substantive foundation.
The state of LLMs, RAG, agents and multimodal models, translated to transport, freight forwarding and 3PL. What is hype and what is genuinely deployable in the logistics chain.
Risk classification for planning algorithms, dispatch AI, ETA prediction and exception routing. Which applications fall under high risk, and what that means for documentation and human oversight.
What you need before AI gets started: clean master data, reliable telematics feeds, structured TMS shipment data, and the quality of GPS and on-board computer data.
Hands-on work with real transport documents: improving customs descriptions, interpreting EDI errors, drafting customer emails about deviations, and writing eCMR explanations.
How to connect AI to Transics, Carrierweb, i-teq, Squid, in-house TMS, eCMR platforms and customs systems. When to choose an API, a data lake or a dedicated transport planning API.
Driver location and on-board computer logs are personal data. What an employer may do with them, how this relates to the ATW, and what distance AI should keep from disciplinary decisions.
To close, we design a first roadmap together: pilots, integrations, governance framework and investments, in a logical sequence per department.
A conversation with operations, the IT lead and planning. Which departments, which sensitivities, which TMS and telematics stack. Based on that, we draw up the playbook.
Transport and logistics organisations are diverse: from a family transport firm to a 3PL with international subsidiaries, from a bicycle courier to a customs forwarder. We have designed the modules so that we can shift the emphasis per role group, sometimes one session per group, sometimes a mixed day with sub-sessions.
Strategic leaders who set the organisation's AI direction. Focus: market developments, competitive position, governance, AI Act classification, investment choices and the relationship between the AI roadmap and the customer portfolio.
The backbone of a transport organisation. Focus: where AI strengthens existing planning tools and where it falls short, how ETA prediction and dispatch suggestions relate to human judgement, and what a TMS integration realistically offers.
People who liaise daily with customers, shippers, customs and drivers. Focus: hands-on prompting on real documents, exception handling, writing emails about delays, drafting customs descriptions and eCMR explanations.
Those responsible for vehicles, drivers and operational deployment. Focus: cold-chain monitoring, predictive maintenance, capacity forecasting, and bias evaluation of AI in driver allocation.
Specialists in AGS, EORI, Brexit, sanctions and third-country trade. Focus: where AI responsibly supports classification, descriptions and risk screening, and where the Union Customs Code sets firm boundaries.
Architecture, integration, data quality. Focus: integrations between TMS, telematics, on-board computers, BI and the AI layer; when your own transport planning API is wiser than an ad hoc integration.
During the workshop we work with use cases that will be recognisable to transport organisations: concrete situations to apply the legal, technical and operational analysis to. Participants discover for themselves where the line falls between a useful assistant and a high-risk system.
A first classic is route optimisation. A carrier wants to plan multi-stop tours more intelligently, making better use of loading capacity and respecting time windows at terminals. AI components can add value here, such as traffic pattern models and multi-stop optimisation enriched with historical pickup times, but the real lever often lies in the quality of shipment data and the agreements between sales and planning. For the broader process, see our transport planning API practice.
A second is ETA prediction and capacity forecasting. Customers want more reliable arrival times; shippers and 3PL operators want to anticipate peaks and troughs around Schiphol cargo, the Rotterdam port or seasonal patterns. Models that combine historical journeys, traffic data, bookings, terminal throughput times and external signals can narrow the spread of ETAs and support capacity discussions with sales and subcontractors, provided they do not create false certainty. Overconfidence is dangerous: a window that is too narrow and regularly exceeded is worse than a wide window that holds. We practise with evaluation metrics that also measure tail behaviour.
A third is dynamic dispatching. AI helps planners assign incoming jobs in real time to available vehicles and drivers, taking into account driving times, certifications and ongoing shipments. Under the AI Act, this is precisely the kind of application that becomes high-risk once it directly affects drivers' working conditions. We cover which human-in-the-loop choice is responsible and how to prevent unintended discrimination based on seniority, part-time status or place of residence.
A fifth is customs document automation and eCMR support. AI helps improve goods descriptions, match HS codes, spot inconsistencies and summarise customs correspondence. With eCMR, it opens up room for automatic status updates and suggested wording when deviations occur. The framework is strict: incorrect classification has consequences for import duties and sanctions screening, and CMR liability remains with the carrier. We cover when AI responsibly provides support, and when a rule engine or a human double-check remains necessary.
One sixth is exception handling and cold-chain monitoring. Most of the work in freight forwarding lies in exceptions: a delay at a terminal, missing documents or, for temperature-sensitive shipments, a deviation in cold transport. An AI assistant that distils a coherent picture from the mailbox, telematics feed and TMS status helps you switch faster and makes alerts actionable.
These cases serve as material for analysis. We work through each case against the AI Act classification, GDPR position, data quality and the practical feasibility within your stack. By the end, your team has a shared view of what is sensible to investigate first, and what is not (yet).
For each application discussed, we begin with its classification under the AI Act. Planning algorithms that directly determine driver deployment fall into the high-risk category much sooner than an AI that only summarises customer emails.
Driver location, on-board computer logs, driving times and loading-party data sit within a complex legal landscape. We treat it as a single governance question: data flows, authorisations, retention, mobility data spaces and the relationship with collective labour agreements and employment law.
Many AI projects fail not on the model but on the data. We walk through the master data hygiene, telematics quality and TMS integrations that need to be in place before a prediction model or agent makes sense.
For transport and logistics there is no separate "AI law", but applying general and sector-specific regulation to AI is no formality. Every serious application brings several frameworks to the table at once.
The AI Act is the European framework that classifies AI systems by risk level. For transport, three aspects are crucial: Article 4 (AI literacy for everyone who works with AI, not only IT); the high-risk classification for systems that affect employees (dispatch AI); and transparency when interacting with customers. The GDPR applies to driver location, on-board computer logs and customer data: a GPS feed is personal data about the driver. Added to this is the Working Time Directive: driving and rest times are mandatory, and an AI system must not, directly or indirectly, encourage a driver to exceed their limits.
For customs, the Union Customs Code, sanctions regimes and, since Brexit, an extensive third-country practice apply. AI support for classification or risk screening can help, but the declaration remains legally the responsibility of the declarant. For international road haulage, the CMR Convention plays a role: with eCMR and AI, it must be clear where AI suggestions stop and carrier liability begins. At European level, mobility data spaces and related data-sharing frameworks are emerging, with active regulation on who may access which data and how AI models are permitted on shared datasets.
We do not treat these frameworks as a closed book but as a toolkit: when you bring in legal counsel, and when the works council or trade union. For structural AI implementation, this connects to our AI development practice.
We come to your office or branch, such as the Schiphol cargo area, Maasvlakte, Venlo, Eindhoven or the Randstad, with a team of two. A room with a projector and one laptop per participant is sufficient.
Live video session for distributed teams, also useful for international organisations with subsidiaries in Belgium, Germany or the UK. Hands-on blocks run in your own browser; a recording is available for colleagues who could not attend.
For larger organisations, sometimes better than one long day: a series of half-day sessions in which we serve the target groups separately, with the board and management team, planning and freight forwarding, and fleet and operations each in their own session.
It starts with an intake session, usually with the operations lead, an IT or data representative, and someone from planning or dispatch. We discuss the target group, existing AI activities, the TMS landscape, telematics provider(s) and eCMR status. Based on that, we propose a programme with modules, format and schedule, sent to you in advance.
The workshop follows. We work in clear blocks: a short introduction, a longer hands-on or analysis exercise, and at the end the outline of a follow-up roadmap. For the hands-on blocks, we ask in advance which documents you would like to use (anonymised EDI errors, customer emails about exceptions, examples of customs descriptions) and build the exercises around them. Afterwards, you receive a written wrap-up: which modules were covered, who attended, which priorities came up and which next steps we agreed. This material is useful as documentation for the AI literacy obligation under Article 4 of the AI Act.
In our view, the workshop is not an end goal but a starting point. For some organisations a single session is enough: a shared picture and a set of follow-up questions for the management team. For others it becomes the trigger to go further: a pilot on ETA prediction, a TMS integration, a dedicated transport planning API, or extending existing transport software with AI. Both outcomes are valid.
A half-hour introductory call in which we go through which departments will take part, what your TMS and telematics stack looks like and where the first need lies. We then send a concrete proposal with modules, format and schedule.
Appfront uses cookies and similar technologies to keep the website working properly, for analytics and for marketing. You choose what you allow. Read more in our privacy policy.
