Implementing artificial intelligence in organizations : competencies and emerging roles

Artificial Intelligence (AI) is widely regarded as a key technology of the digital age, with the potential to reshape value creation, decision-making, and competitive dynamics across all industries. However, many organizations still struggle to translate this potential into strategic and operational value. AI initiatives are often reactive, fragmented, and poorly aligned with business strategy or internal structures. As a result, companies face organizational resistance, unclear responsibilities, and governance gaps. In this context, the thesis addresses the critical question of which organizational competencies and roles are necessary for AI implementation. The findings demonstrate that AI transformation goes beyond technology, emphasizing organizational challenges involving people, processes, and alignment. A comprehensive competency perspective emerged, spanning interdependent domains: technical and data capabilities, strategic competencies, governance and ethics, change management with emphasizes on coordination and process expertise. In addition, five role clusters were identified: operational, strategic, hybrid, enabling, and governance-related roles. Especially hybrid roles such as AI translators or AI product owners, which bridge business and technical perspectives and foster cross-functional collaboration, were highlighted. The study offers both theoretical and practical contributions. Theoretically, it refines and extends existing frameworks such as Dynamic Capabilities Theory and Resource Orchestration Theory by grounding abstract concepts in empirically derived competencies and dynamic role structures. Practically, it provides actionable guidance for organizations by outlining which capabilities and roles are needed, how they evolve, and how they can be embedded in specific organizational contexts. The findings stress the importance of strategic intentionality and organizational agility: AI success is not a one-time milestone, but an ongoing process of learning, adaptation, and structural development. Organizations must build a human-centered and context-sensitive capability architecture to ensure that AI becomes a transformative force, rather than an isolated experiment. Ultimately, this thesis contributes to turning the fragmented and hype-driven discourse on AI into a more structured, practice-oriented, and sustainable transformation process.