Integrating and Adopting Artificial Intelligence for Knowledge Management and Organizational Learning in Projects

Sammanfattning

In today’s project-based organizations, the ability to learn continuously and manage knowledge effectively is critical for innovation and long-term success. However, because of time constraints, brief team life cycles, and disjointed processes, project teams frequently find it difficult to gather, retain, and share knowledge. Artificial intelligence (AI) offers promising support for these challenges by enabling real-time documentation, intelligent retrieval, and cross-project learning. Nevertheless, there is still a lack of research on how to successfully integrate and adopt AI tools into project teams' everyday tasks. This thesis explores how AI tools are integrated and adopted to support knowledge management (KM) and organizational learning (OL) in project environments. Guided by the SECI model, Organizational Learning Theory, and Innovation Diffusion Theory, this thesis is based on seven semi-structured interviews with professionals from IT and consulting firms of varying sizes. The thematic analysis reveals that while AI can enhance knowledge practices, especially when it comes to capturing tacit knowledgea nd supporting reuse, its effectiveness depends on contextual factors such as perceived usefulness, workflow compatibility, and opportunities for trial and feedback. And this thesis contributes to both theory and practice by offering a nuanced understanding of the human and organizational conditions that shape AI adoption, and by identifying what enables these tools to become part of sustainable learning systems in fast-paced project settings.