The conversation around talent shortages often starts with numbers: how many vacancies exist, how many candidates are available, and how quickly a position can be filled.
But the latest labour-market signals suggest that this is no longer enough to explain what is happening.
The emerging challenge is not simply finding more people. It is identifying candidates whose demonstrated capabilities, career intentions and transferable skills match a specific business problem.
For employers, this means that application volume is becoming less valuable than signal quality. For candidates, it means that a degree, previous employer or familiar job title may no longer communicate the full value they can bring.
From credentials to demonstrated capability
Education and professional experience remain important. They provide context, foundational knowledge and evidence of progression.
However, they do not always answer the questions employers increasingly need to resolve:
- What can this candidate actually do?
- Which problems have they solved?
- How did they apply technology, data or AI?
- What measurable result did their work produce?
- Could their capabilities transfer to another role or industry?
- What kind of opportunity are they genuinely interested in?
A recent example comes from McKinsey’s approach to AI-related talent. According to reporting published on 18 September 2026, the company is broadening its recruitment beyond traditional MBA and university pipelines to include candidates from technology companies, startups and bootcamp backgrounds.
The development should not be interpreted as evidence that degrees have become irrelevant. Nor does one company represent the entire market.
It does, however, illustrate an important direction: when employers need people who can apply technology to real business problems, conventional credentials alone may not provide enough information. The candidate’s ability to demonstrate how a skill was used, and what it achieved, becomes a stronger part of the hiring signal.
The Dutch market is not showing broad hiring expansion
At the same time, official Dutch labour-market data does not support a narrative of rapidly increasing hiring volume.
Statistics Netherlands reported on 17 September that:
- 408,000 people were unemployed in August 2026;
- the unemployment rate remained at 4.0%;
- unemployment increased by an average of 3,000 people per month over the previous three months;
- the number of people in paid employment fell by an average of 1,000 per month;
- approximately 195,000 people were receiving unemployment benefits.
CBS also reported that the number of working-age people in employment was 9.616 million in the second quarter of 2026, compared with 9.629 million in 2025 and 9.619 million in 2024. After years of almost continuous growth, Dutch employment has effectively remained flat for two years.
These figures do not mean that companies have stopped recruiting, nor that specialist-talent shortages have disappeared. They indicate that broad employment growth has stalled. Within this environment, employers are likely to be more selective about which positions they open and which candidates they progress.
The hiring requirement becomes narrower, more specific and more closely connected to an immediate business need.
More candidates do not automatically create better hiring outcomes
AI has made it easier to discover vacancies, generate application documents and submit applications at scale. But increased application volume can create a new problem for employers: more profiles to process without a corresponding improvement in candidate quality, interest or suitability.
In a selective labour market, the central challenge is therefore not:
How do we generate more applications?
It is:
How do we identify the small number of candidates who can solve this specific problem, and who are genuinely interested in the opportunity?
That requires more than keyword matching between a CV and a job description. It requires a combination of:
- verified and active vacancies;
- structured skills and capability data;
- evidence of how those skills were applied;
- career and mobility intentions;
- location and working-model preferences;
- transferable-skills analysis;
- explainable matching;
- candidate consent;
- human review.
Skills may be more transferable than job titles
This change is particularly relevant when industries are undergoing restructuring.
An embedded engineer from the automotive sector, for example, may also have relevant capabilities for semiconductor production, defence technology, industrial automation, intelligent energy systems or advanced manufacturing.
A job-title-based system may fail to identify these connections. A capability-based system can look beyond the title and examine the underlying engineering problems, tools, systems and environments in which the candidate has worked.
The same principle applies outside engineering. A candidate’s current title describes where they have worked. Their capability profile can show where they could create value next.
What this means for Ekspats and Spark
At Ekspats, we do not believe the market needs another platform designed to maximise application volume. Our direction is different.
Ekspats combines HR technology with an international talent network. Spark is being developed as the matching and controlled hiring-flow layer connecting candidates, recruiters and companies.
Verified opportunity → capability and career-intent matching → human review → a limited shortlist of relevant candidates → candidate approval → employer conversation
This approach places emphasis on three forms of quality.
1. Opportunity quality
A vacancy should be current, accessible and supported by a genuine hiring intention. Candidate work should not begin without sufficient clarity about the position, responsible decision-maker and expected process.
2. Candidate-signal quality
Matching should consider not only education and previous titles, but also demonstrated skills, problem-solving evidence, transferable capabilities, career intent and practical constraints.
3. Process quality
AI should support the process without taking accountability away from candidates, recruiters or employers. Recommendations need to be explainable, important decisions require human control, and candidate information should not be submitted without approval.
The next phase of recruitment is not about more
The available evidence does not show that open positions are simply multiplying across the market. It points towards something more nuanced: companies continue to hire, but their needs are becoming more specialised, while traditional CV signals are becoming less effective at showing whether someone can solve a particular problem.
That creates a different definition of value for recruitment technology. The winning platform will not necessarily be the one that produces the most applications or the largest candidate pool. It will be the one that creates the strongest signal between a real business need and a candidate with the capability, interest and readiness to address it.
At Ekspats, that is the direction we are building towards with Spark:
Not more CVs. Better-understood talent, verified opportunities and a more controlled path to hiring.
Learn more about Ekspats and Spark at ekspats.com.
Sources
- Statistics Netherlands – Unemployment up slightly in August, 17 September 2026
- Statistics Netherlands – Employment rate among working-age people unchanged for second year running, 17 September 2026
- Business Insider – McKinsey broadens its technology-talent recruitment in the AI era, September 2026