Prospects of AI supported innovation management: a systematic expert analysis

The rapid integration of Artificial Intelligence (AI) into innovation management processes is transforming how organizations conceive, develop, and implement new ideas. While AI is widely perceived as a driver of creativity, efficiency, and data-driven decision-making, many organizations adopt AI initiatives amid a phase of technological hype, often without a clear understanding of their long-term implications for innovation management. This thesis investigates how AI is used to support innovation management and how organizations may evolve in response to its increasing adoption. Grounded in innovation management and technology assessment literature, the study combines a systematic literature review with semi-structured interviews with industry experts involved in innovation, technology, and managerial roles. The interview findings are used to identify key drivers, uncertainties, and organizational challenges related to AI-supported innovation management. Based on these insights, the thesis develops a scenario analysis to explore alternative future configurations of AI integration in innovation management practices. The scenarios aim to highlight potential strategic paths, managerial implications, and organizational outcomes under different conditions. By integrating empirical evidence with scenario-based analysis, this research provides both conceptual insights and practical guidance for managers seeking to navigate AI adoption beyond short-term hype and align technological opportunities with long-term innovation objectives.