Artificial Intelligence (AI) is transforming how innovation is generated, managed, and shared across organizational boundaries. Beyond its role as a technological enabler, AI increasingly functions as a strategic actor that influences how firms and institutions organize for innovation, access external knowledge, and coordinate with partners within broader ecosystems (Bahoo et al., 2023). Its diffusion challenges the traditional assumptions of open innovation (OI) (Chesbrough, 2006), calling for a re-examination of how openness, collaboration, and technological capabilities interact in the age of intelligent systems (Arias-Pérez and Huynh, 2023; Broekhuizen, 2023).

Within this broader transformation, it remains unclear whether AI acts as a catalyst or an inhibitor of OI processes. On the one hand, AI systems may reduce the need for openness by automating knowledge recombination, design, and problem-solving processes, allowing firms to internalize activities that once relied on external collaborations (Boussioux et al., 2024). By providing access to vast data repositories and predictive models, AI can help organizations generate and refine innovative ideas internally, potentially narrowing their reliance on external networks. On the other hand, effectively deploying and exploiting AI technologies requires new skills, data infrastructures, and complementary capabilities that are often scarce within individual organizations. This dependency encourages firms to collaborate with specialized partners, such as AI startups, prompt engineers, research institutions, and data providers, thereby stimulating new forms of openness and interorganizational learning (Holgersson et al., 2024). The growing integration of AI thus raises questions about how traditional mechanisms in OI contexts, such as knowledge sharing, absorptive capacity, organizational learning, social capital, and inter-firm relationships governance structures, evolve when algorithmic systems become part of the innovation ecosystems. For example, issues such as intellectual property management, user motivation in co-creation processes, and inter-firm collaboration dynamics may be fundamentally redefined by the presence of AI systems capable of autonomously generating and combining knowledge.

Understanding these transformations is crucial, and this track invites theoretical, empirical, and methodological contributions that advance understanding of how AI technologies influence the structures, processes, and outcomes of OI.

Relevant research questions include:
- Does AI reduce the need for openness by internalizing problem-solving capabilities, or does it foster interorganizational collaboration – potentially in new forms?
- What organizational capabilities and governance mechanisms enable effective AI integration in organizations and OI ecosystems?
- What microfoundations (e.g., group, user, and manager level) enable effective AI integration in organizations and OI ecosystems?
- How does AI affect the dynamics of knowledge creation and exchange, intellectual property management, and trust among partners?
- How does AI influence creativity and innovation performance in OI contexts?
- How might AI-mediated collaboration alter incentive systems, fairness, trust, engagement, and coordination among diverse actors in OI ecosystems?
- What ethical, social, and strategic implications emerge when AI participates in collaborative innovation processes?

By integrating insights from management, design, psychology, sociology, computer science, and organizational theory, this track aims to chart the emerging contours of AI-powered open innovation and its implications for the future of R&D management.