Navigating the New Norm: Employees’ Continuous Data Sharing in AI-Driven Workplaces

Mena Teebken (), Thomas Hess () and Alexander Pretschner ()
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Mena Teebken: Bavarian Research Institute for Digital Transformation
Thomas Hess: LMU Munich School of Management
Alexander Pretschner: Technical University of Munich

A chapter in Digital Innovation and Organizational Transformation, 2026, pp 233-246 from Springer

Abstract: Abstract 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.

Keywords: Digital work; Employee data sharing; Artificial intelligence; AI-based assistants (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-032-08483-5_16

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DOI: 10.1007/978-3-032-08483-5_16

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