TY - JOUR A1 - Füller, Johann A1 - Hutter, Katja A1 - Wahl, Julian A1 - Bilgram, Volker A1 - Tekic, Zeljko T1 - How AI revolutionizes innovation management – Perceptions and implementation preferences of AI-based innovators JF - Technological Forecasting and Social Change N2 - The application of AI is expected to enable new opportunities for innovation management and reshape innovation practice in organizations. Our exploratory study among 150 AI-savvy innovation managers reveals four different clusters in terms of how organizations may use and implement AI in their innovation management ranging from (1) AI-Frontrunners, (2) AI-Practitioners, and (3) AI-Occasional innovators to (4) Non-AI innovators. The different groups vary not only in their strategy, organizational structure, and skill-building but also in their perceived potential, understanding of the required changes, encountered challenges, and organizational contexts. Our study contributes to a better understanding of the current state of AI-based innovation management, its impact on future innovation practice, and differences in organizations’ AI ambitions and chosen implementation approaches. KW - AI-based innovation management Innovation process Organizational setup Organizational context Cluster analysis Y1 - 2022 U6 - https://doi.org/10.1016/j.techfore.2022.121598 SN - 0040-1625 VL - 178 PB - Elsevier BV ER - TY - RPRT A1 - Füller, Johann A1 - Bilgram, Volker A1 - Wahl, Julian A1 - Hutter, Katja T1 - Autonomous Innovation – How AI is implemented in Innovation Management N2 - Success stories about the groundbreaking impact of artificial intelligence (AI) technology on business are omnipresent, and the benefits that might emerge for innovation management are the talk of the town. However, managers are still struggling to find the appropriate approach for applying AI in their innovation projects. At this early stage in the age of AI, companies differ in how they leverage the opportunities afforded by AI and seek guidance. Our study among 162 tech-savvy managers highlights the current state of AI usage in innovation management, reveals different implementation patterns, and provides guidance in finding the right approach to increase innovation performance using AI technology. We find that companies follow distinct implementation approaches that differ regarding strategy, organizational setup, capability building, and scaling, which results in three user groups: 39% of participants consider themselves “AI-Leaders”, the most progressive group when it comes to dealing with the technology and breaking ground in AI-driven innovation management. 30% belong to the “AI-Pragmatists” user group that is ambitious in applying AI, while 31% are „AI Aspirants” only rarely using AI for their innovation processes. A vast majority of the participants anticipates overall improvements in innovation performance by more than 50%, as they expect the effectiveness and efficiency of new product development to increase significantly. However, companies still face severe implementation challenges, such as missing data access or a lack of technical expertise. Depending on their individual characteristics, companies should take deliberate strategies to advance in the autonomous innovation game. Y1 - 2022 UR - https://www.hyve.net/en/autonomous-innovation-study/ ER -