The Netherlands Has the AI Talent and Tools — But Is Failing to Turn Them Into Economic Growth
The Hague, Friday, 5 June 2026.
The Netherlands debates AI endlessly, yet struggles to profit from it. A June 2026 VNO-NCW report reveals that slow SME adoption and poor data sharing are costing the Dutch economy dearly.
A Nation Rich in AI Ambition, Poor in AI Returns
The Netherlands finds itself in a paradoxical position in June 2026: a country that has spent years building a robust artificial intelligence research ecosystem and attracting digital talent, yet one that is failing to convert that intellectual capital into measurable economic output [1]. According to a report published by VNO-NCW, the Dutch employers’ federation, the core problem is not a lack of awareness or ambition — it is a structural failure to scale AI adoption across the broader economy [1]. The report describes this as an ‘ordeningsprobleem,’ or ordering problem, where the country has an abundance of AI programmes, roadmaps, and pilot initiatives, but lacks the coordination and execution discipline required to turn them into widespread, value-generating deployment [1].
The SME Gap: Where Dutch AI Value Is Being Lost
At the heart of the problem is the Netherlands’ small and medium-sized enterprise sector — the backbone of the Dutch economy [GPT] — which is struggling to keep pace with larger, more technologically agile companies [1]. The VNO-NCW report explicitly warns that without targeted government support, knowledge sharing, and room to experiment, SMEs risk falling dangerously behind in AI adoption [1]. This is not merely a technology problem; it is an economic urgency. The report frames AI as a critical lever for addressing three structural challenges the Netherlands faces simultaneously: stagnating productivity growth, a persistently tight labour market, and the mounting pressures of an ageing population [1]. Each of these challenges, left unaddressed, carries significant long-term costs for the Dutch welfare state and its global competitiveness [GPT].
Data Sharing: The Missing Infrastructure
One of the most concrete bottlenecks identified in the VNO-NCW analysis is the absence of functional, secure data-sharing infrastructure between companies and institutions [1]. Without the ability to pool and exchange data across organisational boundaries, AI systems — which depend on large, diverse datasets to deliver meaningful insights — are starved of the fuel they need to function effectively [GPT]. The report points to existing models as proof that such frameworks can work when properly constructed. JoinData, a data-sharing initiative designed for agricultural entrepreneurs, and digiGo, a similar concept for the built environment, are cited as concrete examples of sector-specific data ecosystems that provide the secure frameworks necessary to enable meaningful collaboration between businesses [1]. The challenge, according to VNO-NCW, is to replicate and scale these models across additional sectors of the Dutch economy through what the report calls ‘sectorale dataruimtes,’ or sectoral data spaces [1].
From Fieldlabs to Full Scale: What Policy Action Is Required
The VNO-NCW report does not stop at diagnosis — it sets out a clear direction for policy intervention [1]. To accelerate AI deployment among established businesses, the report calls for three specific operational changes: a higher tempo of testing cycles within fieldlabs, greater regulatory freedom within closed testing environments, and the mandatory inclusion of scalability as a hard criterion when awarding public innovation funding [1]. This last point is particularly significant. By attaching scalability requirements to public grants and subsidies, the Dutch government could effectively filter out pilots that are never designed to grow beyond a single company or region — a pattern that has long diluted the impact of innovation spending in Europe [GPT]. The report further argues that businesses must treat AI adoption not as an incremental IT upgrade, but as a strategic transformation of their entire value chain, supported by guaranteed access to AI computing capacity and a reliable energy supply [1].
A Real-World Example: AI Care Assistant NOA Shows What Is Possible
While the macro-level debate about AI scaling continues, individual Dutch companies are already demonstrating what structured AI deployment can look like in practice. On 27 May 2026, Mentech — a Dutch technology company — announced it had received an Innovatiekrediet from the Rijksdienst voor Ondernemend Nederland (RVO), acting on behalf of the Dutch Ministry of Economic Affairs and Climate Policy, to develop NOA, an AI-driven care assistant designed to support people living with dementia or intellectual disabilities [2]. NOA works by recognising emotions and tension in users, providing real-time responsive support that is intended to reduce pressure on the long-term care sector [2]. Mentech’s plan is to bring a fully deployable version of NOA to market within two years — targeting both home care settings and residential care institutions, with a development deadline of no later than May 2028 [2]. The project represents precisely the kind of sectoral AI application the VNO-NCW report advocates for: a concrete, scalable tool deployed in healthcare, one of the sectors the report specifically identifies as ripe for AI-driven transformation through diagnostics support and assisted care [1][2].
International Recognition and the Competitive Stakes
The potential for Dutch AI innovation to reach international recognition is already being demonstrated. In late April 2026, ‘Noah’ — a digital AI buddy developed by Philadelphia Zorg in collaboration with clients, Hogeschool Rotterdam, and Virtually Human — won the CDI Foundation Award in the category ‘Outstanding Impact in Inclusive AI’ at a ceremony held in Barcelona [2]. Noah was developed as a digital companion for people with intellectual disabilities, and the Barcelona award underscores that when the Netherlands does execute on AI in care settings, the results are globally competitive [2]. These developments arrive against a broader geopolitical backdrop in which the Netherlands is increasingly positioning itself as a strategic technology partner on the world stage. On 22 May 2026, Indian Prime Minister Narendra Modi’s diplomatic visit to the Netherlands brought into sharp focus the country’s role in global technology supply chains — with the bilateral agenda covering ten areas of cooperation including AI and innovation, semiconductors, and digital partnership, with specific attention to Dutch photolithography manufacturer ASML [4]. The visit signalled that the Netherlands is viewed internationally as a critical technology node — a status that makes the domestic AI scaling failure all the more costly [4].
One Roadmap, Not Many: The Path Forward
The central recommendation emerging from the VNO-NCW analysis is one of consolidation and execution [1]. The Netherlands, the report argues, must convert its fragmented landscape of AI initiatives and strategies into a single, unified roadmap — one backed by genuine government coordination and clear incentives for cross-sector collaboration [1]. The prescription is not to invent new programmes, but to impose order on the many that already exist, and to ensure that public innovation resources are directed toward applications that can actually scale [1]. The concept of AI as an economic tool dates back to the 1955 Dartmouth proposal — co-authored by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon — which first articulated the ambition to ‘make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves’ [1]. Seventy years on from that founding vision, the Netherlands has the talent, the research base, and the international standing to be a leader in applied AI. The question, as June 2026 makes abundantly clear, is whether the country can finally close the gap between talking about AI and profiting from it [1].