Master Thesis with 3DSE Management Consultants.
📌 Key facts
- 📌 Key facts
- 💡 Background
- 🦾Who We Are
- 🎯 Goals
- 🎓 Profile
- 📚 Further Reading
- ➕ Additional Information
- 📝 How to Apply
- 📬 Contact
💡 Background
Companies are investing in AI for product development, engineering, software development, testing, and knowledge management. Yet reported time savings do not necessarily translate into higher output, better quality, lower costs, or shorter time-to-market. The effects depend on the task, workflow integration, data, user capabilities, and organizational changes. Development organizations need a practical way to connect AI investment and adoption with operational and financial outcomes.
🦾Who We Are
3DSE Management Consultants is a specialized management consultancy focused on product and service development. We help industrial companies make their development organizations more effective by improving strategy, processes, structures, and collaboration. Our work combines rigorous methods with a strong focus on people and behavior to turn complex challenges into measurable results.
The Chair for Strategy and Organization is focused on research with impact. This means we do not want to repeat old ideas and base our research solely on the research people did 10 years ago. Instead, we currently research topics that will shape the future. Topics such as Agile Organisations and Digital Disruption, Blockchain Technology, Creativity and Innovation, Digital Transformation and Business Model Innovation, Diversity, Education: Education Technology and Performance Management, HRTech, Leadership, and Teams.. We are always early in noticing trends, technologies, strategies, and organisations that shape the future, which has its ups and downs.
🎯 Goals
Develop and empirically test a benchmark model for measuring the business value of AI in development organizations.
Research question: How can AI value be measured systematically, and how are investment, adoption maturity, productivity, and financial outcomes related?
🎓 Profile
Suitable for students in business, information systems, industrial engineering, economics, technology management, data science, or a related field. Applicants should be interested in AI and performance measurement and have knowledge of quantitative research and statistical analysis. Experience with interviews, surveys, productivity measurement, or tools such as R, Python, or SPSS is helpful.
📚 Further Reading
- Noy and Zhang (2023), Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence.
- Brynjolfsson, Li, and Raymond (2025), Generative AI at Work.
- Mikalef and Gupta (2021), Artificial Intelligence Capability and Its Impact on Organizational Creativity and Firm Performance.
- Jöhnk, Weißert, and Wyrtki (2021), Ready or Not, AI Comes—An Interview Study of Organizational AI Readiness Factors.
➕ Additional Information
A mixed-methods design is envisaged: derive relevant KPIs and maturity dimensions from literature and expert interviews, then conduct a survey or benchmark study with development organizations. Measures may include AI spending, process integration, usage, cycle time, throughput, rework, quality, development cost, and time-to-market. Statistical analyses should distinguish correlation from causality and account for differences in company size, industry, and development complexity.
📝 How to Apply
Email the thesis title you are interested in and a short introduction to your background and research interests.
📬 Contact
Prof. Dr. Clemens van Dinther
3DSE Management Consultants GmbH