AI adoption in the Netherlands is booming. AI value isn’t.

That’s the paradox this article tackles: why so many Dutch companies have “gone AI” on paper, yet only a small group are actually turning it into measurable advantage – and what Dutch SMEs can do in 2026 to join that small group.

Why AI adoption in the Netherlands isn’t the same as AI advantage

On any European ranking, the Netherlands shows up as one of the most active AI markets: strong digital infrastructure, a highly educated workforce, and multiple government programs that explicitly fund AI innovation for SMEs (for example, the MIT R&D AI scheme that supports SME AI projects). (business.gov.nl)

At the same time, global research from McKinsey and BCG keeps repeating the same pattern:

  • Most organizations say they are using AI somewhere in the business.

  • Only a small minority see clear financial impact at enterprise level.

  • A tiny group of “high performers” or “future-built” companies capture the lion’s share of AI value and pull away from competitors. (McKinsey & Company)

In other words: adoption is now table stakes. The question is no longer “Are you using AI?” but “Can you prove AI is improving your revenue, margins, and resilience?”

As Dutch regulators implement the EU AI Act, the bar is getting higher. The Act explicitly requires organizations that develop or deploy AI to ensure a sufficient level of AI literacy among staff, with obligations already in force from February 2025. (Digital Strategy EU)

So Dutch companies face a double challenge:

  1. Close the AI value gap (move from pilots to real business impact).

  2. Raise AI literacy across the workforce under regulatory pressure.

That is where AI adoption in the Netherlands becomes an advantage for a few, and a risk for everyone else.

The real problem: a three-layer value gap

From conversations with Dutch executives, workshops with leadership teams, and ongoing research, three barriers come up again and again. None of them are about “more tools.”

1. Skills: from tool users to T-shaped AI talent

Most leaders say “we lack AI skills,” but what they usually mean is deeper than prompt skills or coding.

High-performing companies build T-shaped capabilities:

  • People with deep expertise in their domain (sales, operations, finance, HR…).

  • A broad, practical understanding of how AI can reshape processes, decisions, and products.

In practice, this looks like:

  • A sales leader who understands how AI can reshape lead scoring, outreach sequences, and pricing experiments.

  • A supply-chain manager who can think in terms of human-AI workflows across forecasting, inventory, and logistics.

  • A CFO who can ask, “If we invest in this AI project, what business metric must move, and how will we measure it?”

Global surveys show that organizations with structured training and AI literacy programs are far more likely to use AI consistently and report business impact. (McKinsey & Company)

The Netherlands now has a legal reason to act: under the EU AI Act, companies must ensure AI literacy for everyone involved in operating or using AI systems, not just technical staff. (Digital Strategy EU)

That means AI literacy is no longer a “nice to have training.” It is now:

  • A compliance requirement.

  • A precondition for meaningful AI value.

If your people don’t understand how AI changes their work, they will never redesign that work.

2. Workflows: from “automation theatre” to new ways of working

Research from McKinsey and BCG is clear: the companies that see the strongest impact from AI do something very different from the rest. They redesign workflows, instead of sprinkling AI onto existing processes. (McKinsey & Company)

Typical low-value pattern in Dutch organizations:

  • Add a chatbot to customer service.

  • Add AI text generation to marketing.

  • Add an “AI assistant” to office tools.

These initiatives can reduce small pockets of friction, but they rarely change the economics of the business.

High-value pattern:

  • Rethink how the entire customer journey is handled when AI can pre-qualify, score, and route leads.

  • Rethink planning when AI can continuously simulate demand, constraints, and scenarios.

  • Rethink product development when AI can generate and test thousands of variations before humans review the top candidates.

Instead of asking “Where can we automate?”, high performers ask:

“If we designed this workflow today, assuming AI is available by default, how would it look?”

That question is at the heart of closing the AI value gap. (First AI Movers)

3. Measurement: from AI activity to business outcomes

Most companies can tell you:

  • How many AI pilots they are running.

  • Which tools they bought.

  • How many employees have access.

Far fewer can answer simple questions like:

  • “Which revenue line is most positively affected by AI today?”

  • “Which cost line would increase if we turned off our AI initiatives?”

  • “How has customer lifetime value or churn changed because of AI?”

BCG’s research on “future-built” companies shows that this small group consistently tracks AI impact with business metrics, not technology metrics, and reviews them at senior-leadership level. (BCG Global)

Without this discipline, organizations fall into pilot purgatory:

  • Many experiments.

  • Weak or anecdotal impact.

  • No clear basis to scale or stop.

If you cannot name the business metric your AI project must move, you are not doing AI strategy. You are doing AI experimentation.

What AI high performers do differently

Across studies and real-world work with clients, AI leaders share a consistent playbook: (McKinsey & Company)

  1. They lead with strategy, not tools

    • AI initiatives are anchored in clear business objectives: growth, margin, new products, new markets.

    • Efficiency is important, but it is not the only goal.

  2. They invest seriously in AI literacy and workforce planning

    • AI training is not a one-off webinar; it is a continuous program.

    • Leadership is visibly involved, not delegating AI to “the IT team.”

  3. They adopt an AI-first operating model

    • AI is embedded in core processes, not isolated in an “innovation lab.”

    • Business and IT share ownership of AI initiatives.

  4. They redesign workflows end-to-end

    • Processes are rebuilt around human-AI collaboration, not just automation.

    • Agents and autonomous systems are introduced where they can truly own tasks, with humans in control of decisions and exceptions.

  5. They measure relentlessly and scale fast

    • Each initiative has clear, outcome-based KPIs.

    • Successful projects are quickly rolled out across regions, units, and functions.

    • Projects that do not move metrics are stopped, even if the technology works.

This is the mindset Dutch SMEs need if they want to turn AI adoption in the Netherlands into AI advantage for their own business.

The Dutch SME context: high pressure, real opportunity

Dutch SMEs operate in a very specific environment:

  • Strong support: Dutch and EU programs like AiNed and MIT AI fund SME AI projects and collaborative R&D. (business.gov.nl)

  • Tight regulation: The AI Act phases in from 2025, with explicit rules on prohibited systems, AI literacy, and high-risk use cases. (Autoriteit Persoonsgegevens)

  • Intense competition: Many larger players are already rewiring their organizations around AI, not just adding tools.

For SMEs, this mix means:

  • You cannot compete on AI budget or internal data-science headcount.

  • You can compete on clarity, speed, and focus.

Low-code platforms, cloud-based AI, and agentic tools are bringing advanced capabilities within SME budgets. The competitive edge will belong to the companies that:

  • Choose the right problems.

  • Build AI-literate teams.

  • Move from pilot to scale faster than peers.

A 2026 roadmap: how Dutch SMEs can close the AI value gap

Here is a practical playbook I use with clients who want to move from AI experiments to AI advantage. It is tailored to AI strategy for SMEs in the Netherlands.

1. Start with three concrete business problems

Forget “we need a gen-AI strategy.” Instead, answer:

  • Where are we losing money?

  • Where are we leaving money on the table?

  • Where are customers clearly frustrated?

Examples:

  • Customer acquisition too expensive.

  • Inventory tying up working capital.

  • Response times hurting customer satisfaction.

Only after listing problems do you ask:
“Where can AI change this equation?”

2. Build AI literacy where value is created

Don’t start with a generic training for “everyone.” Start with:

  • Leadership teams.

  • Owners of the three core processes you chose.

Focus AI literacy on:

  • How AI changes decision-making in their domain.

  • What good human-AI workflows look like.

  • How to ask the right questions of vendors and internal teams.

This is exactly the space where my company runs AI literacy workshops and in-company trainings: helping leadership teams and key operators understand how AI changes their specific business, and what to do in the next 90 days — not in some abstract future.

3. Redesign one workflow end-to-end

Pick one process and commit to a real redesign, for example:

  • Lead generation to closed-won deal.

  • Purchase order to invoice.

  • Customer complaint to resolved case.

For that workflow:

  1. Map the current steps, decision points, and handovers.

  2. Identify which steps AI can automate, which steps AI can augment, and which decisions must remain fully human.

  3. Design the new workflow assuming AI assistance is available by default.

  4. Implement with a small, cross-functional team that owns the outcome.

This becomes your flagship AI value case.

4. Define outcome metrics before you build

For each initiative, set:

  • 1–3 primary business KPIs (for example, conversion rate, churn, average handling time, working capital).

  • A baseline (where you are now).

  • A target and timeframe (what improvement you expect and by when).

Also track:

  • Adoption metrics (who is actually using the AI-enabled workflow).

  • Qualitative feedback from staff and customers.

Make it impossible to say “we think it’s working” without evidence.

5. Create “AI governance lite”

You don’t need a full corporate governance framework, but you do need clarity on:

  • Who owns each AI initiative (by name).

  • What data is used, from where, and with which safeguards.

  • When human review or override is mandatory.

  • How you comply with AI Act requirements for transparency, risk, and literacy.

Appoint AI champions in major functions (sales, operations, finance, HR). Their job is not to be the most technical person, but to:

  • Translate between business and technical teams.

  • Escalate blockers quickly.

  • Keep the focus on outcomes, not experiments.

6. Partner for speed, but keep strategy in-house

Use external partners and platforms for:

  • Implementation.

  • Specialized models or infrastructure.

  • Change management support.

Keep inside your company:

  • Problem selection.

  • Workflow design.

  • Definitions of success.

The goal is not to “outsource AI,” but to accelerate your learning curve while owning your strategic direction.

7. Scale what works, kill what doesn’t

Set 90-day checkpoints:

  • If an initiative shows clear momentum on the right metrics, double down and expand.

  • If not, stop or redesign it, even if the technology is impressive.

This discipline is one of the big differences between future-built companies and everyone else. It prevents AI portfolios from turning into expensive proof-of-concept museums.

Why 2026 is the decision point

2026 is not just “another year in AI.” It is the year when three curves intersect for Dutch SMEs:

  • AI adoption is already mainstream in the Netherlands.

  • Regulation is tightening, especially around AI literacy and risk.

  • A small group of “future-built” companies are compounding their advantage, quarter after quarter.

If you build AI literacy, redesign at least one core workflow, and implement outcome-based measurement in the next 6–12 months, you position your company in the small group that turns AI into durable advantage, not just cost.

If you don’t, you risk facing competitors whose cost base, speed, and product capabilities you simply cannot match.

Your competitors are already testing, scaling, and learning. The real question now is:

Will you be part of the small group closing the AI value gap in the Netherlands, or part of the majority that only “uses AI” without ever feeling the impact?

If you want help designing that roadmap, training your teams in AI literacy, and turning abstract AI talk into a concrete 90-day plan, that is exactly what I do through First AI Movers: workshops, leadership sessions, and ongoing advisory for Dutch and European SMEs ready to move from hype to execution.

Dr. Hernani Costa
Founder & CEO of First AI Movers

SOURCES

Open Tabs

Looking for more great writing in your inbox? 👉 Discover the newsletters busy professionals love to read.

Reply

or to participate

Keep Reading

No posts found