2026 UvA ZODIAC project awarded KHMW Stimulus

Funding body: KHMW

UvA applicant: Corine Boon & Tom van Engers (FdR, UvA)

The Royal Holland Society of Sciences and Humanities (KHMW) has awarded the first Stimulus ‘Human and Organisation’ to ZODIAC. This is a University of Amsterdam project focusing on a new type of organisational structure in which artificial intelligence (AI) runs an entire department without human staff: the ‘zero-person department’. The interdisciplinary research will be conducted by UvA Economics and Business, the Faculty of Science and the Amsterdam Law School. The award, presented for the first time in 2026, includes €400,000 in funding and is supported by the Stichting De Quay Rutten. The UvA’s proposal was selected from 87 submissions from universities across the Netherlands.

The research team

ZODIAC (Zero-person Organisational Departments in Interaction, Accountability and Collaboration) is led by Corine Boon, Professor of HRM & People Analytics at the Amsterdam Business School, and Tom van Engers, Professor of Legal Knowledge Management at Amsterdam Law School. Other members of the team include Economics and Business researchers include Almasa Sabari, Neris Güven and Marco Rapp, and Faculty of Science researchers professor Sander Klous and Thomas van Binsbergen.

Opportunities and risks

The researchers believe organisations are approaching a turning point where large parts of organisations may be run by relatively autonomous agent-based systems. It is not a question of if this will happen, but when.
The project looks beyond automating individual tasks and instead studies what happens when entire departments are run by AI systems. In such departments, AI takes on decision-making, communication with employees, and collaboration with other units.

This shift could bring clear advantages. Automating routine processes can improve efficiency, reduce workload, and increase productivity. This is because AI systems can operate continuously and potentially lower operational costs.

At the same time, the team highlights important challenges. Working alongside AI instead of human colleagues may change team dynamics and how people experience their jobs. Roles are likely to shift towards monitoring and evaluating AI output, requiring new skills. There is also a risk that certain expertise may decline if it is used less frequently. In addition, entry-level roles  that are often essential for gaining experience could become less common.

ZODIAC aims to improve the understanding how zero-person departments will work in practice. Despite the increasing autonomy of such systems, responsibility remains with humans. There are ethical, legal and societal considerations that still need to be explored.

2024 Understanding the effects of employee sourcing on gender diversity outcomes (NWO)

Funding body: NWO

UvA applicant: Almasa Sarabi

Sarabi will explore sourcing and identifying candidates for job openings and how this impacts the hiring process. Sourcing and identifying candidates takes place in the early stages of the hiring process. It can have a significant effect on outcomes for the entire organisation. Specifically, the ABS researcher will explore the impact on diversity remains. To date, very little research has been done in this area. She will examine how different sourcing channels—such as using job advertisements, employee referrals, or social media—affect workforce gender diversity. By comparing different sourcing channels across organisations in an American, her research will provide novel insights into how sourcing strategies influence demographic compositions within organisations. The findings of this project contribute to developing more equitable hiring practices and help address gender disparities in the workplace.

2023 – 2027 AI-Powered next Generation of VET

Funding body: Erasmus Lump Sum Grants

UvA (co-)applications: Stefan Mol, Jarno Vrolijk and Alan Bergs

Science is clear: Artificial Intelligence will be the defining development of the 21st century. Experts estimate that due to the rise of AI within only 2 decades aspects of daily human life will be unrecognisable. The influence of AI is about to challenge the very organising principles of our economic and social order. It can generate unprecedented wealth, revolutionise medicine and education, but it can bring existential perils for life as we know it. This makes the EC2020 report that the EU is lagging behind USA and Asia in AI adoption and development all the more worrying. Among many reasons behind that, the lack of skilled workforce is definitively one of the more prominent ones. The objective of AI4VET4AI is to contribute to the digital transformation of the EU labour market by adding new innovative teaching content and methods to VET curricula across 11 European countries and 18 EU NUTS2 regions, in order to support the growth of AI-skilled workers.

2019 – 2022 Applied data science educational ecosystem (ADSEE)

Funding body: Erasmus+ Cooperation for innovation and the exchange of good practices

UvA (co-)applicants: Stefan Mol

The socio-economic aspect of data and data related industries are crucial for the further development of the European Union and its competitiveness capacities in the global economy. However, there is an existing gap between total demand and supply of data workers of 420.000 in EU in 2016, with a forecast to face a data skills gap corresponding to 769.000 unfilled positions by 2020. The main objective of the ADSEE project is to deliver useful educational and training programs in data science through the following: the development of educational modules; the adaption of contents and methods according to the envisioned needs of target groups; the creation of interactive didactic tools and the production of guidelines and recommendations for the innovative educational approaches in DS. Special attention will be paid to data science in non-technical universities and its application in non-technical business, where previous knowledge in this area is not mandatory.

2018 – 2019 Redefining nursing skills for AI and robotisation in health care

Funding body: Erasmus+ Cooperation for innovation and the exchange of good practices

UvA (co-)applicants: Stefan Mol and Sofija Pajic

In rapidly changing health care systems, digitalisation, e-health and robotisation are gaining influence. Due to the existing global nurse shortage in Europe, a demand for healthcare and therewith nurses will continue to grow, whilst the supply of available nurses is projected to drop. Therefore, it is expected that the shortages will accelerate in the coming decade and will be more serious than the cyclical shortages of the past. This nursing shortage will ultimately constrain health system reform and innovation, and contribute to escalating costs. ICT, AI and robotisation are one way to support health care professionals, enhance interprofessional cooperation and patients` safety. The NursingAI project will analyse and forecast the types of skills and competencies needed by health care professionals, especially nurses. By gaining insight of needed competencies and skill, curriculums for trainings and education programs can be enhanced to the actual needs concerning ICT competencies.

2013 – 2017 Eduworks

Funding body: EU FP7 Framework Program – Marie Curie Multi-Partner Initial Training Network

UvA (co-)applicants: Stefan Mol, Vladimer Kobayashi, Sofija Pajic, Hannah Berkers, Eloisa Federici, Gabor Kismihok and Kea Tijdens

The objective of EDUWORKS is to train talented early-stage researchers in the socioeconomic and psychological dynamics of the labour supply and demand matching processes at aggregated and disaggregated levels. Training involves a broad range of skills and competences required to pursue their career in academic and industrial settings.

2014: Sidney A. Fine grant for research on job analysis

Funding body: The Society for Industrial and Organizational Psychology

UvA (co-)applicants: Stefan Mol, Gabor Kismihok, Hannah Berkers and Vladimer Kobayashi

The project is aimed at providing a fresh perspective on job analysis by leveraging the potential of big data analytics. In this interdisciplinary project job data will be automatically extracted from a vast number of online vacancies and existing occupational taxonomies and structured through semi-supervised machine learning and text mining techniques.