AI Adoption in Open Innovation Partnerships: Trends, Challenges, and Strategic Implications

Abstract

Artificial intelligence (AI) is transforming innovation partnerships and clusters across diverse sectors, yet its adoption remains uneven, with numerous challenges hindering effective implementation. This study examines the adoption patterns of various AI technologies among open innovation partnerships, analyzing the motivations, challenges, and success metrics associated with AI integration. Drawing on open innovation paradigms, and emerging research on AI in collaborative contexts, we investigate how partnerships navigate the complex tensions between automation and augmentation when implementing AI across organizational boundaries. Our analysis of survey data from 45 European innovation partnerships across multiple industries reveals significant sectoral variations in AI readiness and implementation approaches. We identify four distinct adoption patterns—AI Leaders, Specialized Adopters, Early Experimenters, and Non-Adopters—each characterized by specific implementation approaches, technology preferences, barrier profiles, and ethical considerations. Knowledge gaps emerge as the most significant implementation barrier, showing a negative correlation with adoption levels, while efficiency improvement and innovation enhancement serve as primary adoption drivers. The findings highlight the transformative potential of AI in accelerating collaborative innovation processes and the role of partnership characteristics in shaping implementation strategies. By categorizing and evaluating different patterns of AI utilization, this study provides a comprehensive framework for partnerships to strategically devise and execute AI initiatives aligned with their innovation objectives. The research offers valuable insights for partnership coordinators and policymakers seeking to design effective strategies for fostering AI-driven innovation ecosystems and addressing sector-specific implementation barriers.