Advanced Journal of Management, Humanity and Social Science

Agentic Artificial Intelligence as a Digital Employee: Redefining the Boundaries of Delegation, Accountability, and Control in Information Systems-Enabled Organizations

Document Type : Original Article

Authors

Seyyedeh Zahra Naseri 1
Reza Salimi Beni 2
Hamid Reza Hanif 3
Masoumeh Cheraghi 4

1 Master of Project and Construction Management, Faculty of Architecture and Urbanism, University of Art, Tehran, Iran.

2 Master of Science in Information Technology Management, Specialization in Advanced Information Systems, Islamic Azad University, Electronic Campus, Tehran, Iran

3 M.Sc. in Mathematics, Department of Mathematics and Computer Science, Iran University of Science and Technology, Iran

4 M.A. in Psychology and B.A. in Business Administration, University of Applied Science and Technology, Ilam Branch, Ilam, Iran

10.5281/zenodo.23862457
Abstract
Background and Purpose: The rapid transition from deterministic automation and passive algorithmic decision-support systems to agentic artificial intelligence (Agentic AI) represents an unprecedented ontological shift in enterprise information systems (IS). Unlike antecedent generative models that merely synthesize conversational text, agentic AI operates with persistent internal memory, autonomous multi-step cognitive reasoning, goal decomposition, and self-directed tool and API execution. In contemporary digital organizations, these autonomous systems are progressively onboarded, provisioned, and treated not merely as passive software artifacts, but as synthetic workers or "digital employees." However, this conceptual elevation fractures foundational organizational assumptions rooted in classical agency theory, sociotechnical systems theory, and managerial control architectures. This monograph investigates how enterprise deployment of digital employees redefines the boundaries of delegation, moral and organizational accountability, and managerial control within IS-enabled organizational environments. Design, Methodology, and Approach: To rigorously interrogate this emergent socio-algorithmic reality, an extensive explanatory mixed-methods research design was executed across a 24-month longitudinal investigation (2024–2026). The quantitative phase collected and analyzed empirical survey data from 482 senior enterprise leaders, Chief Information Officers (CIOs), Chief AI Officers (CAIOs), Enterprise Architects, and line managers across four knowledge-intensive industry verticals (Financial Services & FinTech, Healthcare & HealthTech, Global Supply Chain Logistics, and Enterprise Cloud/SaaS). Structural Equation Modeling using Partial Least Squares (PLS-SEM) was employed to empirically test the structural dependencies between perceived agentic autonomy, algorithmic trust, delegation depth, operational agility, and accountability drift. The qualitative phase comprised 38 semi-structured, in-depth key-informant interviews and four comprehensive, longitudinal multi-case studies examining end-to-end organizational deployments of multi-agent architectures (including LangGraph, AutoGen, CrewAI, and proprietary enterprise agent meshes). Rigorous thematic analysis and qualitative comparative synthesis were conducted to deconstruct operational failures, behavioral drift phenomena, and governance adaptations.

Graphical Abstract

Agentic Artificial Intelligence as a Digital Employee: Redefining the Boundaries of Delegation, Accountability, and Control in Information Systems-Enabled Organizations

Keywords

Agentic Artificial Intelligence
Digital Employees
Autonomous IS Agents
Delegation Dynamics
Algorithmic Governance
Subjects
[1]    Bant, E., & Paterson, J. M. (2024). Algorithmic corporate agency and the boundaries of liability in enterprise systems. Journal of Business Ethics, 191(2), 245-263.
[2]    Bedford, D. S. (2015). Management control systems and innovation: A resource-allocation perspective. Accounting, Organizations and Society, 41, 12-28.
[3]    Besta, M., Blach, N., Kubicek, A., Gerstenberger, R., Gianinazzi, L., Gajda, J., ... & Hoefler, T. (2024). Graph of thoughts: Solving elaborate problems with large language models. Proceedings of the AAAI Conference on Artificial Intelligence, 38(16), 17682-17690.
[4]    Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
[5]    de Laat, P. B. (2024). Algorithmic accountability and the moral crumple zone: Shifting responsibilities in autonomous enterprise systems. Ethics and Information Technology, 26(1), 8.
[6]    Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2024). Exploring the future of agentic AI in business and management: Synergies, opportunities, and systemic threats. International Journal of Information Management, 75, 102758.
[7]    Edge, D., Trinh, H., Cheng, N., Bradley, J., Chao, A., Mody, A., ... & Larson, J. (2024). From local to global: A graph RAG approach to query-focused summarization. ACM Transactions on Information Systems, 42(4), 1-28.
[8]    Eisenhardt, K. M. (1989). Agency theory: An assessment and review. Academy of Management Review, 14(1), 57-74.
[9]    Elish, M. C. (2019). Moral crumple zones: Cautionary tales in human-robot interaction. Engaging Science, Technology, and Society, 5, 40-60.
[10] Faraj, S., Pachidi, S., & Sayegh, K. (2025). The agentic turn in artificial intelligence: From cognitive tools to synthetic colleagues in enterprise systems. Information Systems Research, 36(1), 12-33.
[11] Floridi, L. (2023). The ethics of artificial intelligence: Principles, challenges, and enterprise governance. Oxford University Press.
[12] Gefen, D., Benbasat, I., & Pavlou, P. A. (2008). A research agenda for trust in online environments. Journal of Management Information Systems, 24(4), 275-286.
[13] Hadfield-Menell, D., Russell, S. J., Abbeel, P., & Dragan, A. (2024). Inverse reward design and the alignment of enterprise synthetic agents. Artificial Intelligence, 328, 104068.
[14] Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). SAGE Publications.
[15] Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.
[16] Hong, S., Zheng, X., Chen, J., Cheng, Y., Zhang, C., Wang, Z., ... & Zhou, M. (2024). MetaGPT: Meta programming for a multi-agent collaborative framework. Nature Machine Intelligence, 6(3), 284-296.
[17] Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305-360.
[18] Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366-410.
[19] Leana, C. R. (1987). Power relinquishment versus power sharing: An exploration of delegation and participation in managerial decision making. Journal of Applied Psychology, 72(2), 228-233.
[20] Lee, M. K., Jain, R., & Shen, C. (2023). Algorithmic management and human autonomy: Designing socio-technical interfaces for transparent delegation. MIS Quarterly, 47(3), 1121-1148.
[21] Leonardi, P. M. (2011). When flexible routines meet flexible technologies: Affordance, constraint, and the imbrication of human and material agencies. MIS Quarterly, 35(1), 147-167.
[22] Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., ... & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. NeurIPS, 33, 9459-9474.
[23] Lu, Y., & Ramamurthy, K. (2011). Understanding the link between information technology capability and organizational agility: An empirical examination. MIS Quarterly, 35(4), 931-954.
[24] Malone, T. W. (2004). The Future of Work: How the New Order of Business Will Shape Your Organization. Harvard Business School Press.
[25] Markus, M. L., & Robey, D. (1988). Information technology and organizational change: Causal structure in theory and research. Management Science, 34(5), 583-598.
[26] McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13(3), 334-359.
[27] Mumford, E. (2006). The story of socio-technical design: Reflections on its successes, failures and potential. Information Systems Journal, 16(4), 317-342.
[28] Orlikowski, W. J. (2000). Using technology and constituting structures: A practice lens for studying technology in organizations. Organization Science, 11(4), 404-428.
[29] Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics, 30(3), 286-297.
[30] Park, J. S., O'Brien, J. C., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023). Generative agents: Interactive simulacra of human behavior. Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST), 1-22.
[31] Rai, A., Constantinides, P., & Sarker, S. (2023). Next-generation digital platforms: Toward human-AI symbiosis in enterprise systems. Journal of the Association for Information Systems, 24(5), 1189-1208.
[32] Sarker, S., Chatterjee, S., Xiao, X., & Elbanna, A. (2019). The past, present and future of sociotechnical systems theory in information systems research. Information and Organization, 29(4), 100261.
[33] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., ... & Scialom, T. (2024). Tool former: Language models can teach themselves to use tools. NeurIPS, 36, 68539-68551.
[34] Shrestha, Y. R., He, V. F., Puranam, P., & von Krogh, G. (2025). Delegating decisions to artificial agents: A conceptual framework and empirical investigation of algorithmic management. Strategic Management Journal, 46(2), 341-368.
[35] Simons, R. (1995). Levers of Control: How Managers Use Innovative Control Systems to Drive Strategic Renewal. Harvard Business School Press.
[36] Trist, E. (1981). The evolution of socio-technical systems. Occasional Paper, Ontario Quality of Working Life Centre, 2, 1-67.
[37] Wang, G., Xie, Y., Jiang, Y., Mandlekar, A., Xiao, C., Zhu, Y., ... & Anandkumar, A. (2024). Voyager: An open-ended embodied agent with large language models. Transactions on Machine Learning Research (TMLR), 1-32.
[38] Weber, M. (1978). Economy and Society: An Outline of Interpretive Sociology. University of California Press.
[39] Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., ... & Wang, C. (2023). AutoGen: Enabling next-generation LLM applications via multi-agent conversation. arXiv:2308.08155.
[40] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023). ReAct: Synergizing reasoning and acting in language models. International Conference on Learning Representations (ICLR).
[41] Zuboff, S. (1988). In the Age of the Smart Machine: The Future of Work and Power. Basic Books.
Advanced Journal of Management, Humanity and Social Science
Volume 2, Issue 5
September and October 2026
Pages 517-526

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