The rapid advancement of artificial intelligence has given rise to a new generation of information systems that no longer function as passive tools but instead operate as autonomous, so-called "agentic" artifacts. These Agentic AI systems are characterized by their ability to independently take on tasks, make decisions under uncertainty, and assume responsibility for complex work outcomes (Baird & Maruping, 2021). Unlike traditional automation solutions, they are capable of autonomously pursuing goals, interacting with other agents, whether human or machine, and dynamically adapting to changing conditions.

This development carries profound implications for the workplace: Traditional notions of IT use, in which humans act as the sole agents steering the system, are increasingly being called into question. Instead, delegation relationships between humans and AI agents are taking center stage—relationships in which tasks, rights, and responsibilities are not unilaterally assigned but dynamically negotiated and distributed (Baird & Maruping, 2021). At the same time, research shows that AI agents are increasingly being perceived and deployed as team members, bringing both opportunities (e.g., reduced conflict when performance is high) and challenges (e.g., lower process satisfaction, lower perceived benevolence) (Dennis, Lakhiwal & Sachdeva, 2023).

Against this backdrop, a fundamental question arises: What will the workplace of the future look like when AI agents are no longer mere tools but active actors collaborating with humans in hybrid constellations? The concept of Hybrid Intelligence, the synergistic combination of human and machine intelligence for solving complex problems, provides a first theoretical lens (Dellermann, Ebel, Söllner & Leimeister, 2019). While AI already outperforms humans in clearly defined tasks, managing ambiguous, context-dependent, and creative tasks in organizational settings still requires human judgment, the question is how these competencies can optimally interact.

References:

Baird, A., & Maruping, L. M. (2021). The next generation of research on IS use: A theoretical framework of delegation to and from agentic IS artifacts. MIS Quarterly, 45(1), 315–341.

Dellermann, D., Ebel, P., Söllner, M., & Leimeister, J. M. (2019). Hybrid intelligence. Business & Information Systems Engineering, 61(5), 637–643.

Dennis, A. R., Lakhiwal, A., & Sachdeva, A. (2023). AI agents as team members: Effects on satisfaction, conflict, trustworthiness, and willingness to work with. Journal of Management Information Systems, 40(2), 307–337.

Organizations face the challenge of managing the transition to Agentic AI not only technologically but also with respect to work design, collaboration, role definitions, and organizational structures. This is not about the macroeconomic question of whether jobs will "disappear," but rather the much more nuanced and practically relevant question of how the nature of work, collaboration, and the work experience at the individual workplace are concretely changing.

The Master thesis may focus on of the following research questions. The final research question will be determined in consultation between the student and the supervisor:

Delegation and Task Allocation Between Humans and AI Agents:

• How do delegation patterns at the workplace change when AI agents no longer merely execute tasks but autonomously pursue sub-goals and make decisions?
• What role do the agent attributes identified by Baird & Maruping (2021) (endowments, preferences, and roles) play in the decision to delegate tasks to AI agents versus retaining them?
• How do the mechanisms of delegation (appraisal, distribution, coordination) evolve in the human-AI dyad as the AI agent becomes increasingly autonomous?

Collaboration and Team Dynamics with AI Agents:

• How does the integration of AI agents as "team members" change perceived team dynamics, trust, and satisfaction in work teams?
• How do conflict emergence and resolution change in teams when one or more team members are AI agents?

Work Design and Role Transformation:

• How do job profiles, task configurations, and competency requirements at individual workplaces change through the introduction of Agentic AI?
• How does collaboration with autonomous AI agents influence the perceived autonomy, meaningfulness, and identity of one's own work (Job Characteristics)?
• What competencies do employees need to collaborate effectively with agentic AI systems, and how can organizations systematically develop these competencies?

Trust, Control, and Governance at the Workplace:

• How does trust toward agentic AI systems develop at the workplace, and what role do transparency, explainability, and perceived control play?
• What governance structures and control mechanisms are necessary when AI agents increasingly act autonomously and make decisions previously reserved for humans?

Change Processes and Adoption:

• What factors promote or hinder the adoption of Agentic AI at the individual workplace, beyond classical technology acceptance models?
• How do the experiences and perceptions of different employee groups (e.g., by function, hierarchical level, experience background) differ when agentic AI systems are introduced?

Methodology

The methodological approach will be determined in consultation with the supervisor and depending on the chosen research question. Possible approaches include:

• Systematic Literature Review (SLR) to synthesize the current state of research
• Qualitative Methods: Semi-structured interviews with employees and managers
• Quantitative Methods: Survey-based studies and Experiments (Behavioral/ Technical)
• Design Science Approaches, if the thesis aims at developing design principles

• Intensive support for your thesis with regular exchange and guidance
• Opportunity to work within a team on a common goal
• Dynamic and motivated team which is happy to share learnings
• The possibility of starting immediately, with a planned completion of the thesis within ±6 months.

Just send a short email to mahei.li@unisg.ch, and I’ll set up a virtual meeting to get to know each other and discuss the details.

I look forward to hearing from you!

Dr. Mahei M. Li