Generative AI in Dutch logistics: why is implementation lagging behind?

Introduction

The Dutch transport and logistics sector is lagging behind in the adoption of generative AI (GenAI). Despite its potential to improve efficiency and reduce costs, large-scale implementation remains limited. In this blog post, we examine the reasons for this lag, how managers and employees perceive GenAI, lessons from practical examples, the main obstacles, and concrete first steps to successfully implement GenAI.

Causes of Lagging GenAI Implementation

Economic and Technical Barriers:

Logistics companies often operate on thin margins, making large investments in new technologies like GenAI a carefully weighed decision. The infrastructure required for large language models (LLMs) is a hurdle; nearly half of organizations cite the lack of suitable IT infrastructure as the main barrier to GenAI development. Costs also play a role—both direct (cloud computing, licenses) and indirect (time investment, integration with existing systems). Technically, companies struggle with limited data quality and siloed systems. Recent research shows that 25% of companies cite low data quality as a challenge for AI, and 22% report a lack of internal expertise. These factors slow the development of reliable GenAI applications because clean, integrated data and know-how are insufficient to train or guide models effectively.

Cultural and Organizational Barriers:

A significant part of the lag comes from human factors within organizations. GenAI is still relatively new and “unknown breeds mistrust.” Many employees are hesitant to use AI due to uncertainty and lack of experience. Research among 1,000 Dutch professionals shows that only 42% use GenAI tools like ChatGPT—well below the European average of 75%. Employees often associate AI with uncertainty or even fear, alongside curiosity and enthusiasm. Furthermore, there is often a lack of clarity about company policies: over a third (36%) of employees do not know whether AI use is allowed at work. This lack of clear guidelines makes staff reluctant to experiment. Organizationally, many companies lack a concrete AI strategy or management oversight. Only a small fraction (3%) have implemented AI (including GenAI) at scale; most remain in exploratory or pilot phases. Finally, the shortage of qualified personnel—specialists who can translate AI into logistics processes—further slows adoption.

Current Perception of GenAI in Transport and Logistics

Management vs. Employee Views:

There is a gap between how management and employees perceive GenAI. Dutch managers are relatively more positive and adopt AI more frequently; almost two-thirds report using AI tools, compared to about one-third of other employees. Managers are better informed about AI policies and more often recognize the strategic benefits, while employees are more hesitant. Only 8% of Dutch professionals are daily users of AI tools. Managers often emphasize that AI is meant to support, not replace, employees. This message must be communicated clearly, as there is a widespread misconception that AI can fully replace human intelligence. In reality, AI should complement human expertise. When employees understand that GenAI can make their work easier rather than threaten their jobs, they are more receptive.

Doubts, Concerns, and Misconceptions:

Several concerns about GenAI exist in the sector. A key worry is the reliability of AI-generated outputs. Logistics processes demand accuracy; an AI “hallucination” (a confidently incorrect response) in customs documents or planning advice can have serious consequences. Privacy and data security are also concerns: sensitive company and client data should not be exposed to AI systems, especially external cloud services. Sector-wide research indicates that transparency and explainability of AI models are major challenges. Without understanding why a model gives a certain recommendation, professionals tend to play it safe with traditional methods. Misconceptions also exist—some employees believe AI always requires massive datasets or is too complex to deploy. In reality, modern GenAI tools often provide ready-to-use capabilities via natural language interfaces that are useful with limited proprietary data, but this understanding is not yet widespread. Addressing these misconceptions requires education and hands-on experience.

Large vs. Small Companies:

The adoption gap between large enterprises and SMEs is clear. Like elsewhere in Europe, Dutch SMEs lag behind corporates in AI adoption. While around 40% of large Dutch companies embraced AI in 2023, only about 12% of SMEs did. This is relevant for logistics, which consists of a few large players (e.g., international carriers, mainports) and a long tail of small to mid-sized operators. Larger organizations typically have more resources to experiment with GenAI—they can hire data specialists and fund pilot projects. Smaller logistics businesses often lack this capacity and take a wait-and-see approach. SMEs are also more cautious due to limited internal AI knowledge and regulatory uncertainty. New EU AI regulations and data requirements pose complex compliance challenges for smaller companies without extensive legal teams. Ironically, GenAI promises much for SMEs (low entry barrier, many tools immediately available, no large IT projects required), yet lack of skills and confidence slows adoption in practice.

Concrete First Steps Toward Successful GenAI Adoption

Given these insights, what can Dutch transport and logistics companies do to bridge the gap? Some practical first steps and recommendations:

1. Start with a well-defined pilot use case:

   Choose a specific problem or process where GenAI can add immediate value. Start with a non-mission-critical area to limit risk. Examples:

   • Virtual assistant for customer inquiries: Many carriers receive repetitive questions on delivery times, shipment status, or inventory. A generative AI chatbot can handle these FAQs in natural language, reducing pressure on customer service and improving response time. Answers can be reviewed before going live.

   • Auto-generation of quotes or transport documents: GenAI can draft CMR waybills or quotes from minimal input (route, cargo, conditions) for staff to refine. Such “co-pilot” use cases increase efficiency quickly and help staff adapt to AI collaboration.

2. Check data and AI readiness:

   Inventory the data and resources needed for the use case. Is the required data available and of sufficient quality? If not, prioritize collecting and cleaning the data. For a delivery FAQ chatbot, historical shipment data, status codes, and a track-and-trace system connection are essential. Map the current IT environment: is cloud AI service access available? Are there developers familiar with GenAI APIs? Starting small with an existing tool (e.g., OpenAI GPT via Microsoft Azure or a Dutch GPT-NL/TNO variant) can be prudent before building complex integrations. This preparation increases the chance of success and avoids missing critical components mid-project.

3. Set guidelines and involve employees early:

   Clearly communicate why the organization is adopting GenAI and what is expected. Formulate a brief AI policy for the pilot: what data can AI use, who can access results, how is feedback handled? Create a safe sandbox for a small team to experiment without fear of errors. Include diverse roles—IT/data, operations, planning, customer service—to ensure a cross-functional perspective. Let employees suggest improvements during the pilot to build buy-in. Research shows 80% of employees experiment with AI regardless of policy, so guiding it properly reduces risk while leveraging curiosity.

4. Choose the right partner(s):

   Collaboration with AI-specialized tech companies or startups can bring existing tools or expertise, saving time and resources. Many logistics software providers now integrate AI features—check with your current IT partners. Knowledge-sharing initiatives like the Dutch AI Coalition’s Taskforce on Mobility, Transport, and Logistics provide insights and access to experts. Universities and vocational schools offer talent through internships or thesis projects, often at low cost, as seen in PostNL’s collaboration with the Vrije Universiteit. Industry associations (e.g., TLN, Evofenedex) can help with pilot subsidies or peer knowledge exchange.

5. Start small, learn fast, scale up:

   Implement in a limited scope—one department, process, or client segment. Monitor results and actively gather feedback from employees and customers. Continuously measure and evaluate impact. Communicate successes (“proof by example”) to motivate wider adoption. If challenges arise, iterate: for instance, a chatbot may handle 80% of questions correctly, while 20% are routed to humans or retrained. Celebrate milestones and maintain a culture of experimentation as adoption grows gradually.

6. Quick-win technology applications:

   Some GenAI use cases can quickly add value in logistics:

   • AI-generated reports and summaries: weekly transport performance summaries, inventory reports, or customer emails condensed into actionable points.

   • Planning support: describe alternative scenarios in text or extract insights from historical planning data, supporting decision-making for transport planners.

   • Predictive maintenance content: convert machine and vehicle sensor logs into alerts or forecasts (“Forklift in warehouse 3 shows rising temperatures over 5 days, maintenance may be needed soon”), aiding fleet managers.

   • Translation and multicultural communication: translate work instructions, safety rules, or customer correspondence quickly across languages.

   • Sales & marketing content creation: generate blog posts, newsletters, or LinkedIn content around market trends, case studies, or services. Human review ensures accuracy and tone.

Starting with targeted use cases allows logistics organizations to quickly realize value and create momentum for broader GenAI adoption. Measuring and sharing successes, addressing obstacles, and keeping humans central is key. The future of AI in logistics lies in the combination of humans and machines: GenAI automates routine work, accelerates insights, and unlocks creativity, while humans make decisions, build relationships, and manage exceptions.

Conclusion

The lagging implementation of generative AI in the Dutch transport and logistics sector has multiple causes, ranging from technical and economic barriers to cultural hesitation and organizational uncertainty. Managers and employees still have mixed feelings about this technology, but as familiarity and experience grow, perceptions are shifting toward greater positivity. Practical examples—both in the Netherlands and abroad—show that GenAI can indeed provide substantial benefits in logistics processes, if implemented correctly.

By proactively addressing existing obstacles (data quality, costs, expertise, regulation, and change management) and starting small but purposefully, the sector can gradually reap the rewards of GenAI. The recommendation is clear: become familiar with this technology, learn from small successes and failures, and build an AI-ready logistics chain. Those who take the first steps now—no matter how modest—create a competitive advantage for the future. In an era of labor shortages, high customer expectations, and complex global supply chains, generative AI can make the difference between falling behind and staying ahead. The logistics sector has proven its ability to innovate before; with the right approach, GenAI can drive the next leap toward a more efficient, flexible, and competitive logistics landscape in the Netherlands.