Marc Benno Bürger

Titel

Ph.d.-stipendiat, M.Sc Technology-Based Business Development and Engineering

Primær tilknytning

Marc Benno Bürger

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Forskning

Topic of PhD project

Artificial Intelligence in Project Management: adoption, opportunities, and implications

Research background/interests

It is well-known that projects often exceed time and cost estimastions or fail to deliver expected outcomes because of poor decision-making, low maturity in project management technologies, and projects becoming more complex and knowledge intensive. Across the project life cycle, project entities like scoping, time, resources, budget, quality, procurements, documentation, monitoring, reporting, communication, risk, and stakeholders generate large volumes of data, which organizations struggle to effectively utilize. However, the capabilities of artificial intelligence (AI) and its subfields, including generative AI (genAI), natural language processing (NLP), and other related analytical tools have the potential to address some of these challenges. Even though these technologies contain significant potential, organizations and practitioners struggle to effectively utilize AI. This project examines how AI can be adopted within organizations and utilized in the context of project management. It aims to understand the implications of AI on practices, roles, and structures, and how AI is fundamentally different from other “classic” technologies.

Theoretical background/interests

The research adopts a sociotechnical perspective and draws on theories from e.g., systems thinking, digital transformation, and decision-making under uncerntainty to understand how AI reshapes human-technology interaction, organizational routines, and governance structures in project environments.

Methodological background/interests

The project will primarily apply a qualitative research approach with a focus on case studies, interviews, and document analysis. Additionally, the projects aims to eastblish a long-term study on based ethnographic and action reasearch methods within a selected case organization. This will enable close observation of practices, value, and interactions as AI technologies are developed and integrated.

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