Artificial Intelligence (AI) has rapidly evolved into a general-purpose technology with disruptive implications for entrepreneurship, innovation, and knowledge work (Magistretti et al., 2019; Agrawal et al., 2022; Gruetzemacher & Whittlestone, 2022; Jobstreibizer et al., 2025). Generative models, predictive analytics, and intelligent automation are reshaping how entrepreneurs identify opportunities, design ventures, and mobilize resources (Noy & Zhang, 2023; Brynjolfsson et al., 2025). In parallel, entrepreneurship research has increasingly embraced ecosystem perspectives, emphasizing that outcomes emerge from the interplay of actors, infrastructures, and institutions (Isenberg, 2010; Stam, 2015; Fernandes & Ferreira, 2022). In periods of disruptive change, startups’ dynamic capabilities – sensing, seizing, and transforming – show a strong positive association with entrepreneurial ecosystem performance, while the integration of artificial intelligence operates as a system-level catalyst that amplifies these effects and drives ecosystem-wide success (Cimino et al., 2025). Moreover, in this context, peer innovation constitutes a strategy that allows start-ups to acquire new necessary knowledge to speed up the innovation process (Primario et al., 2024). Within this systemic view, AI acts as both a venture-level enabler – enhancing ideation, financing, and scaling – and an ecosystem-level transformer, reshaping orchestration, governance, and knowledge flows (Bereznoy et al., 2021; Battisti et al., 2022; Secundo et al., 2025). Despite its potential, scholarship remains fragmented: most studies focus on firm-level adoption, leaving underexplored how AI-driven changes scale into ecosystem outcomes such as diversity, resilience, legitimacy, and knowledge spillovers (Stam, 2015; Spigel, 2017). What is still missing is a systematic understanding of the systemic implications of AI-enabled entrepreneurship (Truong et al., 2023).
This track invites theoretical, empirical, and methodological contributions exploring the intersection of AI, entrepreneurship, and ecosystems, with particular attention to:

• Opportunities and threats of AI-enabled entrepreneurship;
• Entrepreneurial Ecosystem orchestration through AI;
• AI-based knowledge flows and democratization;
• AI capabilities, skills, and entrepreneurial work;
• AI-based processes for venture creation;
• Generative AI for entrepreneurial discovery of opportunities;
• Human–AI collaboration in entrepreneurial processes;
• Innovative methods for researching AI-powered entrepreneurship;
• Decision-making and AI within organizations and ecosystems;
• AI-based academic entrepreneurship.