Conference Agenda

During the last decades, the public management literature has often highlighted the growing importance of skills-based management in public organizations (Horton, 2000). Nowadays, public organizations at different governance levels are called to assess and understand the digital skills their employees need, and to upskill their current workforce or hire expertise through outsourcing or lateral hiring to deal with their digital transformation (DT) (Edelmann et al., 2023). Especially, during the recent modernization waves initiated by technology, civil servants and public managers alike need to upgrade their skills and competencies to navigate the DT of public organizations.

However, there is still no agreement on what specific skills are required of civil servants and public managers beyond those required of the workforce who are faced with achieving an organization's goals with the most efficient and effective use of available resources (Van Deursen & Van Dijk, 2009). The lack of attention to digital competencies is somewhat surprising since digital technologies have played a significant role in the public sector (Dinglestad et al., 2023). As a result, it remains unclear what specific skills are needed to implement new technologies and switch between appropriate systems. While civil servants will not all need to be digital specialists, they must understand the latest technological tools' potential (and associated risks) and develop skills complementary to these new tools for engaging with citizens, analyzing policy problems, and leveraging digital opportunities. At a minimum, they must be comfortable in an increasingly digital workplace and using digital tools (OECD, 2017).

While this might seem to pose a technological question at the surface, we believe it is a managerial problem that touches many different aspects of public service. The topic itself is underexplored in the existing literature. Therefore, we set out to conduct a problematizing literature review (PLR) to understand how the concept has been addressed in neighboring disciplines of sociology, management, and information management. The ambition of a PLR is to re-conceptualize existing thinking and derive new ideas and theories following the four core principles defined by Alvesson and Sandberg in their work (2020). Namely, the ideal of reflexivity, reading more broadly but selectively, not accumulating but problematizing, and the concept of less is more. However, problematizing reviews often lack robustness regarding data collection and analysis (George et al., 2023). To address this gap, we incorporate bibliometric techniques in our review to systematically navigate our sample and derive a conceptual framework for future research (Ni & Chen, 2016).

Our preliminary findings confirm the interdisciplinarity of the topic and call for clarification of terminology and concepts often used interchangeably, such as skills, competences, capabilities, and abilities. Using thematic mapping techniques and qualitative coding, we isolate four main clusters of articles from different disciplines that inspire the proposed conceptual framework where specific digital skills – such as digital literacy, big data analytics, user-driven design, and digital socio-emotional skills – relate to work practices and administrative routines, policy domains, organizational functions and impacts. Thus, this review concludes with the definition of a new research agenda.

This paper addresses the urgent need for a structured approach to defining and integrating generative Artificial Intelligence (genAI) competences within the public sector. As genAI technologies rapidly transform traditional desk-bound workplaces, public sector organisations face increasing pressure to develop new skills, attitudes, and knowledge among civil servants to ensure successful implementation and interaction with these systems. Despite emerging competence frameworks for artificial intelligence, there remains a significant gap concerning the categorisation and contextual integration of genAI-specific competences tailored to distinct roles within public sector teams. Adopting a collaborative innovation perspective, this study develops a role-based competence framework that identifies and categorises the key competences required for public servants engaging with genAI. Based on a review of scientific publications, existing frameworks in the grey literature, and empirical data from more than 20 experts and practitioners we uncover the complexity of genAI competences and highlight the heightened need for cross-functional collaboration. Our findings demonstrate that effective integration of genAI demands not only new technical skills but also adaptive mindsets and collaborative knowledge-sharing across new team structures. The proposed framework supports holistic organisational development by aligning competence development with knowledge management, awareness initiatives, and access to essential personnel and technological resources. Our framework categorises these competences by roles enabling decision-makers, technical experts, and general users to identify their approach to generative AI in a given project and define the collaboration needed to make this a success.

The use of artificial intelligence (AI) in public‐sector recruitment has grown in response to demographic change, skills shortages, and digitalization mandates (Desouza et al., 2020). Yet, little is known about how HR professionals in municipalities perceive this shift under strict legal norms (e.g. merit‐based selection, equal‐treatment obligations) and forthcoming EU “high‐risk” rules. Drawing on 120 interviews and paired questionnaires, each presenting two of three randomized scenarios (human‐only, hybrid AI–human, fully automated), we employ a convergent mixed‐methods vignette design to explore expectations, concerns, and trust in both human-based and algorithmic decision making. Our findings reveal a fundamental tension: while intuition is valued for contextual sensitivity and experiential knowledge, it conflicts with legal requirements for transparency and equal treatment. Conversely, employees identify AI as an opportunity to ensure consistency and efficiency, yet they worry about its opacity and loss of human judgment. We discuss whether and how these dimensions can be balanced to foster legitimate, trust-based human-AI collaboration in public recruitment that ensures and enhances quality and efficiency.