AI in the Boardroom and the Autonomous Enterprise

Thesis (MA)

Advisor(s): Alisa Mehler (alisa.mehler@tum.de)

Introduction

Artificial intelligence (AI) is moving from operational support tasks into the strategic core of organizations. Systems that summarize, forecast, simulate, and recommend are increasingly present in executive committees, supervisory boards, and top management teams. What was once a matter of human judgment informed by staff analysis is becoming a joint process in which humans and machines shape the framing of problems, the range of considered options, and the confidence attached to each of them.

At the same time, organizations are experimenting with far-reaching automation of their internal operations. In an "autonomous enterprise", planning, resource allocation, monitoring, and adjustment run in closed loops with limited human intervention. This raises questions about which decisions should remain with people, how strategic intent is translated into machine-executable form, and how leaders retain an accurate picture of an organization that increasingly runs itself.

These developments challenge established assumptions about leadership, hierarchy, and accountability. Executive attention, experience, and cognitive limits have long been treated as central explanations of organizational outcomes. When part of the sensing and interpretation work shifts to algorithmic systems, the sources of strategic choice change as well. Boards must also decide how to oversee systems whose reasoning they cannot fully inspect, and how to remain answerable for outcomes they did not directly produce.

This topic examines what happens to strategic decision-making, governance, and organizational control as AI becomes a participant rather than a tool. It combines perspectives from information systems, strategic management, corporate governance, and AI ethics to understand how organizations can benefit from automation without losing direction, responsibility, or legitimacy.

Potential research questions:

• How does the use of AI change strategic decision-making processes in top management teams and boards?
• Which decision rights are delegated to algorithmic systems, which are retained by people, and how is this boundary negotiated over time?
• What governance and oversight mechanisms allow boards to remain accountable for AI-supported or AI-executed decisions?
• How do executives build, lose, or calibrate trust in algorithmic recommendations, and with what consequences for decision quality?
• What organizational designs, roles, and capabilities emerge in enterprises that automate large parts of their operational decision-making?
• How do transparency, explainability, and auditability requirements shape the adoption of AI at the strategic level?

If you have your own ideas, feel free to reach out too. The questions above only represent initial ideas, but research topics are not limited to this list.

Tasks:

• Literature review on AI in strategic decision-making, corporate governance, and enterprise automation
• Collect data from interviews, case studies, or surveys
• Analyze data through qualitative methods, qualitative comparative analysis (QCA), or quantitative methods
• Discuss findings and derive practical guidelines

 Requirements

• Interest in current topics of digital business models, AI technologies, and organizational design
• Interest in creating and publishing high-impact research
• High degree of autonomy and individual responsibility
• Above-average grades or other qualifications
• Structured, reliable and self-motivated work style

Further Information

The topic can be adopted according to your interests. The thesis must be written in English. If you have any further questions, do not hesitate to contact me directly.

Please send your application including our application form, "Notenauszug" from TUMonline, and CV to alisa.mehler@tum.de. Please note that we can only consider applications with complete documents.