Global Artificial Intelligence Adoption Survey: Perceptions of Public Sector Employees

The Global Artificial Intelligence Adoption Survey is a large-scale international research initiative coordinated by the University of Ljubljana (Slovenia) under the lead of Prof. Dr. Aleksander Aristovnik. The consortium includes researchers from over 80 countries, all dedicated to exploring how public-sector employees perceive, experience, and adopt Artificial Intelligence (AI) in their professional environments.

The project examines the ways in which AI is transforming public organizations, focusing on employees’ readiness, perceived benefits and risks, and the broader implications for public management and service delivery.

In Germany, the study is coordinated by Prof. Dr. Jens Weiß (Harz University of Applied Sciences), with Prof. Dr. Anna Steidle (HVF Ludwigsburg) serving as Deputy National Coordinator. At HVF, the research contributes to understanding the organizational, ethical, and human-centred dimensions of AI adoption in public administration and related sectors of public service.

• International project lead: Prof. Dr. Aleksander Aristovnik, University of Ljubljana, Faculty of Public Administration (Slovenia)
• National coordination (Germany): Prof. Dr. Jens Weiß, Harz University of Applied Sciences
• Deputy national coordination (Germany): Prof. Dr. Anna Steidle, Ludwigsburg University of Applied Sciences for Public Administration and Finance (HVF)

• To map global trends in AI adoption and perception among public-sector employees.
• To identify drivers and barriers to successful AI implementation in public organizations.
• To generate evidence-based recommendations for ethical, inclusive, and effective AI integration.
• To foster international knowledge exchange and collaboration across public-sector contexts.

The project provides one of the first cross-national empirical datasets on AI adoption in the public sector. Its findings will support policymakers, administrative leaders, and practitioners in designing responsible AI strategies that enhance efficiency while maintaining trust, fairness, and employee well-being.

Results will be disseminated through international publications, conferences, and dialogue with public-sector stakeholders worldwide.