Track description
n°6

Generative Artificial Intelligence (GenAI) is rapidly reshaping how work is designed, experienced, and governed within contemporary organizations (Budhwar et al., 2023). Unlike earlier technologies, which primarily supported or mediated human tasks, GenAI introduces a new paradigm: systems capable of undertaking tasks traditionally exclusively related to humans, through simulating reasoning (Benbya et al., 2021), engaging in co-creation (Randazzo et al., 2024), being creative (Chamakiotis & Panteli, 2024) and autonomously generating decisions and recommendations (Benbya et al., 2021). This shift fundamentally problematizes the traditional balance between human judgment and algorithmic automation (Randazzo et al., 2024).

As GenAI adoption accelerates, its integration into the workplace becomes increasingly complex. For example, Jaser and Petrakaki (2023) highlight the rise of new Artificial Intelligence (AI)-mediated forms of interaction in the workplace, such as AI-assisted and -led interviews, and present different types of challenges associated with them. However, the applications of GenAI go beyond interviewing and hiring and are said to compel a broader rethinking of human-machine interactions and human-machine reconfigurations – particularly in ethically sensitive and socially embedded domains, including performance management, workplace inclusion and precarious contract work such as crowdwork on Mechanical Turk (MTurk) and GitHub (Chowdhury et al., 2024; Yeverechyahu et al., 2024); thus, providing crucial empirical contexts for researchers to study this inevitable transformation by including time-space (Baygi et al., 2021; Carlstein et al., 1978) of design, development (including training and testing), and deployment of GenAI based IT artifacts in the workplace.

Despite the growing interest in GenAI applications in fields such as education, healthcare, and public administration (Dwivedi et al., 2021), there is a notable knowledge gap when it comes to Information Systems (IS)-specific studies that capture the full implications of this technology for the workplace. Addressing this gap is especially timely, given mounting evidence that GenAI tools are already being used across a range of workplace functions, including human resources, management decision-making, product development, budgeting, and digital collaboration (e.g., Budhwar et al., 2022; Jaser & Petrakaki, 2023). Further to the organizational benefits of GenAI, IS may help to unpack the social value (Chamakiotis & Petrakaki, 2025) associated with GenAI uses at work, aligning with call in the IS literature for research that contribute to make the world—and organizations—a ‘better place’(Davison et al., 2023).

Preliminary studies in IS indicate that such technologies are likely to reshape work and workplace relations in complex and unexpected ways (Mayer et al., 2020), posing unique socio-technical challenges—including the loss of critical thinking, knowledge outsourcing, moral burden on employees, and systemic exclusion of certain user groups. Therefore, studying how GenAI may transform work practices, organizational structures, and decision-making processes provides valuable insights into the socio-technical dynamics at the core art of the IS discipline.

Moreover, in line with the ECIS 2026 theme and the IS discipline’s broader commitment to addressing grand challenges and creating positive impact (Davison et al., 2023), this track aims to contribute to an improved understanding as to how the constantly changing GenAI tools can enable more inclusive, adaptive, and data-driven workplaces. At the same time, it foregrounds the risks of unintended consequences—such as disengagement, dehumanization, and the institutionalization of bias—emphasizing the need for responsible, value-driven design and governance (Davison et al., 2023; Kelan, 2023; Lazazzara et al., 2023). Prospective authors could therefore focus on topics ranging from personalization and predictive analytics to bias mitigation and ethical governance, from AI-augmented leadership to algorithmic control and surveillance.

Finally, we are interested in moving beyond a dichotomic understanding (good vs. bad) into a more holistic appreciation of the implications of GenAI in the workplace. Submissions may address GenAI’s impact across the employee lifecycle, organizational workflows, and offer critical, empirical, design-oriented, or theoretical perspectives grounded in IS research.

This track is supported by AIS SIG AI.

• How GenAI technologies reshape the balance between human discretion and machine-driven decision-making.
• Applications of GenAI in candidate sourcing, screening, and automated interviews; opportunities and challenges in fairness and transparency.
• Applications of GenAI in organizational workflows such as product development, financial planning.
• Algorithmic bias in GenAI-enhanced workplaces.
• Performance management and algorithmic control.
• The role of managers in GenAI-enhanced workplaces.
• Impacts of GenAI on inclusive workplace.
• Impacts of GenAI on distributed work and working such as crowdsourcing platforms.
• Impacts of GenAI on creative work, automation work, and managerial decision making.
• Design of accountable, transparent, and explainable GenAI systems for workforce decisions.
• Design and management of GenAI-assisted virtual teams or GenAI tools as team members.
• Trust, resistance, and behavioural responses to GenAI integration in the workplace.
• How GenAI alters competencies, roles, agencies, identities and meta-cognitive skills.
• How GenAI-enhanced workplaces could evaluate economic and non-economic value of innovations by workforce.
• Enabling or constraining employee-driven adaptations to work in GenAI-enhanced workplaces.
• GenAI applications in proactive retention strategies and worker sentiment analysis.
• Impact of GenAI-based tools and products (such as AI agents) on employee wellbeing and psychological safety.
• Cross-cultural and global implications of GenAI-enhanced workplaces.

• Baygi, R. M., Introna, L. D., & Hultin, L. (2021). Everything flows: Studying continuous sociotechnological transformation in a fluid and dynamic digital world.MIS Quarterly, 45(1), 423–452.
• Benbya, H., Pachidi, S., & Jarvenpaa, S. L. (2021). Special issue editorial: Artificial intelligence in organizations: Implications for information systems research. In Journal of the Association for Information Systems (Vol. 22, Issue 2, pp. 281–303). https://doi.org/10.17705/1jais.00662
• Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., Boselie, P., Lee Cooke, F., Decker, S., DeNisi, A., Dey, P. K., Guest, D., Knoblich, A. J., Malik, A., Paauwe, J., Papagiannidis, S., Patel, C., Pereira, V., Ren, S., … Varma, A. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3), 606–659. https://doi.org/10.1111/1748-8583.12524
• Budhwar, P., Malik, A., De Silva, M. T. T., & Thevisuthan, P. (2022). Artificial intelligence–challenges and opportunities for international HRM: a review and research agenda. International Journal of Human Resource Management, 33(6), 1065–1097. https://doi.org/10.1080/09585192.2022.2035161
• Carlstein, T., Parkes, D., & Thrift, N. J. (1978). Human activity and time geography.E. Arnold.
• Chamakiotis, P., & Panteli, N. (2024). Unpacking the Relationship between Creativity and GenAI: The Role of Knowledge and Expertise. ACIS 2024 Proceedings. https://aisel.aisnet.org/acis2024
• Chamakiotis, P., & Petrakaki, D. (2025). Exploring Alternative (Non‐Economic) Forms of Value Engendered by Digital Platforms.Information Systems Journal, 35(4). https://doi.org/10.1111/isj.12576
• Chowdhury, S., Budhwar, P., & Wood, G. (2024). Generative Artificial Intelligence in Business: Towards a Strategic Human Resource Management Framework. British Journal of Management, 0, 1–12. https://doi.org/10.1111/1467-8551.12824
• Davison, R. M., Majchrzak, A., Hardin, A., & Ravishankar, M. (2023). Special Issue on Responsible IS Research for a Better World. Information Systems Journal, 33 (August 30)(1), 1–7.
• Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57(August 2019), 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
• Jaser, Z., & Petrakaki, D. (2023). Are You Prepared to Be Interviewed by an AI? Harvard Business Review, February 7https://hbr.org/2023/02/are-you-prepared-to-be-interviewed-by-an-ai
• Kelan, E. K. (2023). Algorithmic inclusion: Shaping artificial intelligence in hiring. Human Resource Management J, 1–14. https://doi.org/10.5465/ambpp.2021.11338abstract
• Lazazzara, A., Za, S., & Georgiadou, A. (2023). Toward inclusive algorithmic decision-making? A taxonomy framework on the effect of AI on workplace diversity and inclusion. 39th EGOS Colloquium, Cagliari, Italy.
• Mayer, A. S., Strich, F., & Fiedler, M. (2020). Unintended Consequences of Introducing AI Systems for Decision Making. MIS Quarterly Executive, 19(4), 239–257. https://doi.org/10.17705/2msqe.00036
• Randazzo, S., Lifshitz-Assaf, H., Kellogg, K., Dell’Acqua, F., Mollick, E. R., & Lakhani, K. R. (2024). Cyborgs, Centaurs and Self Automators: Human-Genai Fused, Directed and Abdicated Knowledge Co-Creation Processes and Their Implications for Skilling. The Wharton School Research Paper .
• Yeverechyahu, D., Mayya, R., & Oestreicher-Singer, G. (2024). The impact of large language models on open-source innovation: Evidence from GitHub Copilot. ArXiv Preprint ArXiv:2409.08379(4).

We will explore publishing opportunities in relevant journals, such as the European Management Journal (EMJ) and the International Journal of Information Management (IJIM), with which we are affiliated.