DBIS

Enterprise Software has become a critical pillar in global digital transformation. In China, for example, next-generation platforms such as DingTalk and Feishu not only integrate office automation functionalities but also play a central role in project management, team collaboration, and workflow optimization, which enable efficient cross-department collaboration, task transparency, and workflow automation, thereby enhancing organizational efficiency and accelerating digital transformation.

In Germany, however, despite government and industry efforts to promote “enterprise digitalization”, the adoption and application of enterprise software remains relatively limited. Particularly among small and medium-sized enterprises (SMEs), high procurement and maintenance costs, complex system integration, and limited intelligence hinder widespread adoption. As a result, many companies still rely on traditional tools (e.g., email, paper-based approvals, or spreadsheets) and manual operations.

With the rapid development of artificial intelligence (AI), especially large language models (LLMs)-based agents with autonomous decision-making and execution capabilities, enterprise software is expected to evolve from a “passive tool” to an “active collaborator”. AI Agents can understand user needs, automate repetitive tasks, coordinate cross-department workflows, and continuously improve adaptability through learning, which has the potential to significantly enhance efficiency and user experience, offering new opportunities for upgrading enterprise software in Germany.

The healthcare and long-term care (LTC) sector is experiencing severe workforce challenges. Nurses and caregivers are frequently confronted with excessive workloads, mandatory overtime, and the constraints of complex union agreements. These pressures contribute to burnout, high turnover rates, and rising labour costs, while simultaneously undermining care quality and patient satisfaction.

Conventional workforce management (WFM) tools are largely focused on record-keeping and compliance auditing, which lack predictive and optimization capabilities, making them inadequate for the highly dynamic and complex nature of healthcare scheduling. In this thesis, we aim to develop a next-generation, AI-driven, compliance-aware, and human-centered intelligent scheduling platform, that can satisfy regulatory and union requirements while also respecting staff preferences and improving organizational outcomes.

The goal of this thesis is to design and prototype a serious game that simulates stakeholder engagement challenges in MLOps lifecycles. The game will make use of LLM-based agents to represent typical stakeholder roles (e.g., data scientists, ML engineers, domain experts, operations managers) and allow players to interact with them in different lifecycle stages.

The aim is to both explore the dynamics of stakeholder collaboration in MLOps and provide an educational tool for students, practitioners, and researchers to better understand the complexities of human and organizational factors in MLOps.

This thesis aims to explore and prototype a system for participatory governance and transparent decision-making in the context of modernizing legacy software systems. Using Large Language Models (LLMs), the system will analyze and extract decisions from various project artifacts (e.g., commit messages, meeting transcripts), represent them in a structured decision domain model, and create an audit trail that supports stakeholder engagement, accountability, and traceability throughout the modernization process.

The overall goal is to support more inclusive, explainable, and well-documented modernization strategies by making both decisions and their rationales visible and verifiable to all relevant stakeholders.