The changing world of work, driven by automation and digital tech-nologies, requires a workforce that continuously adapts its skills in order to be competitive and employable. This dynamic environment has fueled the demand for advanced skills management, with artificial intelligence (AI) playing a critical role in measuring and supporting individual skills development. This paper looks at AI-based competency profiles as a strategic tool for talent acquisition, reten-tion, and development and highlights their potential challenges. Using semi-structured interviews with experts from HR, education, and industry, we explore the multiple challenges of AI-based skills management from a theoretical, con-ceptual, and practical perspective. Our findings reveal complex problems at the individual, team, organizational, and systemic levels that form a basis for the de-velopment of effective AI-based skills management strategies. This research highlights the importance of using AI to foster an adaptable and skilled workforce that meets both current needs and future challenges.

This study examines the changing nature of employee data sharing in AI-driven workplaces. AI tools, such as intelligent assistants embedded in daily routines, necessitate a reevaluation of data sharing paradigms due to their continuous data generation and processing. Despite the vast body of literature on data sharing, the constant nature of data sharing in AI-driven environments is rarely investigated. This gap between the practical relevance of such tools and our limited understanding of the changes they entail highlights the need for an extended understanding of the evolving nature of data sharing and its implications. We emphasize the concept of Continuous Data Sharing, transitioning from static decision-making to recognizing data sharing as an ongoing process characterized by changes in data dynamics, employee participation, and unpredictability. In summary, this work underscores the importance of redefining employee data sharing in the context of digital workplace transformation and increasing AI integration.

It is estimated that one-fifth of the global population is neurodivergent. As remote work becomes more prevalent, there is a lack of knowledge about how neurodivergent individuals are affected by it. This paper explores how masking, a prevalent coping strategy affected by neuroableism, can be reduced through remote work. Masking is related to severe health consequences, even linked to suicide. We use a qualitative research approach and regular engagement with interviewees to deliver insights into neuromixed communication and collaboration. We show how chronic technocomfort and episodic technorelief reduce the need for masking. Emphasizing individual acknowledgment over diagnoses, this paper underscores the importance of alleviating workplace stressors induced by ableism. This paper contributes to the IS research stream of remote work by focusing on neurodivergent work experiences and interactions with video conferencing tools, employing collaboration and an intersectional perspective.