Lightweight IT encompasses flexible, user-driven technologies like robotic process automation (RPA), bring-your-own-device (BYOD) strategies, mobile applications, and no-code/low-code development platforms that operate outside traditional IT infrastructures. Using a case study/case studies, the research will explore how the organization/organizations manage(s) these technologies, addressing key challenges like compliance, security, and scalability. Depending on the organization and your research interests, the focus can be discussed together.

Lightweight IT encompasses flexible, user-driven technologies like robotic process automation (RPA), bring-your-own-device (BYOD) strategies, mobile applications, and no-code/low-code development platforms that operate outside traditional IT infrastructures. This thesis will review and compare literature on governance approaches for these technologies, identifying best practices and challenges. The focus can be on how these tools differ from traditional IT governance or how governance strategies differ between specific lightweight technologies.

IT governance ensures alignment between IT and business strategies, yet, its effectiveness and how it is realized is shaped by contextual factors such as industry, organizational size, and culture. This thesis aims to conduct a literature review to identify and analyze these factors and their influence on IT governance models. Depending on your interests, the topic can be narrowed down to specific contextual factors.

This topic can be adressed in a thesis from different perspectives, such as:

• How does (generative) artificial intelligence impact team dynamics and performance?
• How does (generative) artificial intelligence impact leadership (incl. role, style and skills)?
• How does (generative) artificial intelligence impact organizational culture?

Setting the changes in team as the fundament for this thesis topic, leadership and culture are additional topics that can be added as requested.

Next to literature analysis (which need to consist of all three questions), empirical investigations are preferred such as quantitative research designs (e.g., survey, (online)experiments) or qualitative research designs (e.g., expert interviews).

Motivational introduction into the topic:

Having understood the significant effects of artificial intelligence (AI) on operational efficiency (Cui et al., 2024), organizations strategically focus on deploying and providing artificial intelligence in their processes and structures. Organizational members across departments now routinely and increasingly interact with conversational AI to reduce highly repetitive tasks and focus more on higher-level cognitive tasks at work, with 88% of AI users coming from non-technical professions (De Smet et al, 2023). AI interactions are likely to become even more common as generative AI is increasingly used in roles and functions previously reserved for humans, such as HR, IT, finance, or customer service and support (Tey et al, 2024). Estimations of the World Economic Forum indicate that until 2020, AI-driven automation will autonomously take over one-third of all work tasks (Di Battista et al., 2025). 

The use of GenAI brings implications for the working environment that are important today and in the future in order to generate hoped-for efficiencies: the way of working through the use of AI in business processes is changing and interpersonal collaboration in teams is being influenced as AI is used alongside human colleagues for monitoring, coordination and operational work. With AI being increasingly embedded in collaborative processes, this technology challenges, the understanding of the technology itself (Larson & DeChurch, 2020), traditional notions of teamwork (Richter & Schwabe, 2025), and intragroup processes (Zercher et al. 2023). 

Sources:

Cui, et al. (2024). The effects of generative ai on high skilled work: Evidence from three field experiments with software developers. Available at SSRN 4945566.

De Smet, A., Durth, S., Hancock, B., Baldocchi, M., & Reich, A. (2023). The human side of generative AI: Creating a path to productivity. McKinsey & Company 

Di Battista, A., Grayling, S., Játive, X., Leopold, T., Li, R., Sharma, S., & Zahidi, S. (2025, January). Future of jobs report 2025. In World Economic Forum, Geneva, Switzerland. www.weforum.org/publications/the-future-of-jobs-report-2025/digest/.

Larson, L., & DeChurch, L. A. (2020). Leading teams in the digital age: Four perspectives on technology and what they mean for leading teams. The leadership quarterly, 31(1), 101377.

Richter, A., & Schwabe, G. (2025). “There is No ‘AI’in ‘TEAM’! Or is there?”–Towards meaningful human-AI collaboration. Australasian Journal of Information Systems, 29.

Tey, K. S., Mazar, A., Tomaino, G., Duckworth, A. L., & Ungar, L. H. (2024). People judge others more harshly after talking to bots. PNAS nexus, 3(9), pgae397-

Zercher, D., Jussupow, E., & Heinzl, A. (2023). When AI joins the Team: A Literature Review on Intragroup Processes and their Effect on Team Performance in Team-AI Collaboration. ECIS 2023 Research Papers. 307. https://aisel.aisnet.org/ecis2023_rp/307

Durch die enorm gestiegene Bedeutung von künstlicher Intelligenz in zahlreichen gesellschaftlichen Bereichen, der Arbeitswelt und immer mehr auch hoch sensiblen Branchen und Unternehmensbereichen, werden negative Effekte und Limitationen von künstlicher Intelligenz zunehmend diskutiert. Dies resultiert auch in regulativen Bestrebungen wie beispielsweise der deutschen KI-Normungsroadmap oder des EU AI Acts auf europäischer Ebene.

Vor diesem Hintergrund stehen Organisationen vor der Herausforderung, interne Regulierung und Richtlinien für den Einsatz von künstlicher Intelligenz zu definieren und KI Governance-Modelle zu implementieren. Durch den aktuellen Hype um generative KI, dem hohes disruptives Potenzial zugesprochen wird und durch besondere Charakteristika und Limitationen von generativer KI, gewinnt KI Governance an Bedeutung.

Im Rahmen dieser Abschlussarbeit soll eine systematische Literaturanalyse durchgeführt werden, um den aktuellen Forschungsstand zum Thema AI Governance im Kontext von GenAI zu analysieren und zu aggregieren. Darauf aufbauend sollen aktuell relevante Forschungsfelder für weitere gezielte Forschung hergeleitet werden.

This topic can be adressed in a thesis from different perspectives, such as:

• When does charisma uplift digital transformation? An analysis of charismatic behavior in different processes of digital transformation
• After vision comes implementation: What are the different leadership qualities/requirements between executives and middle managers for digital transformation success?

Empirical investigations are preferred such as quantitative research designs (e.g., survey, (online)experiments) or qualitative research designs (e.g., expert interviews).

This topic can be investigated from different specifications, such as: 

• The impact of daily stand-up meetings on project success and project team dynamics (quantitative research design, e.g., survey or experiment)

• The impact of retrospectives on project success and project team dynamics (qualitative, e.g., expert interviews or quantitative research designs, e.g., survey or experiment)

• The quantifiable impacts of agile ceremonies/practices (e.g., Scrum ceremonies) on project success and team dynamics (literature review or quantitative research designs, e.g., survey or experiment)

Diese Abschlussarbeit, die in Zusammenarbeit mit einem Unternehmen erstellt wird, befasst sich mit der Prozessoptimierung bei der Herstellung und Lackierung von Kunststoffteilen. Das Unternehmen produziert Kunststoffrahmen und Taster-Abdeckungen, von denen einige in Sonderfarben lackiert werden. Dabei entstehen an mehreren Punkten der Produktionskette Abfälle: fehlerhafte Spritzgussteile vor der Lackierung, misslungene Lackierungen (z.B. durch Blasenbildung) und Transportbeschädigungen an lackierten Teilen. Die Fehlteile müssen im SAP-System zur Nachproduktion (im entsprechenden Produktionsschritt) nach-gebucht werden, was derzeit händisch erfolgt.

Ziel der Arbeit ist es, zunächst einen Überblick über den Abschreibungsprozess der fehlerhaften Teile im Bestandssystem SAP zu schaffen und anschließend den Prozess zu optimieren. Dabei soll bewertet werden, ob eine Automatisierung in SAP oder per Robotic Process Automation (RPA) sinnvoll ist. Zusätzlich wird eine prototypische Automatisierung per RPA erstellt.