Knowledge Management (KM) represents a critical determinant of companies’ success in the digital era (Grant, R. M., 1996; Newell et al., 2009), focusing on the strategic use of learning mechanisms, knowledge assets and flows, to drive innovation and sustainable competitiveness (Nonaka, & Toyama, 2003; Atanasova, 2024). KM plays a pivotal role in knowledge-based value creation, enabling firms to improve innovation, optimize decision-making, and strengthen competitive advantage (Nonaka, & Toyama, 2003; Andreeva & Kianto, 2012; Cegarra-Navarro et al., 2025). When integrated with AI capabilities, KM practices gain additional transformative potential. AI-driven tools such as machine learning, natural language processing, and intelligent knowledge repositories offer new ways to capture, structure, and leverage knowledge (Russell & Norvig, 2020; Gama & Magistretti, 2023, Neiroukh et al.,2024). Recent studies confirm that the combination of KM and AI significantly contributes to green innovation and sustainable value creation (Chuang, & Huang, 2018; Abdulmuhsin et al., 2025; Vo-Thai & Tran, 2025), while reinforcing competitive advantage (Yuan et al., 2025). Furthermore, knowledge-based strategies are increasingly linked to improved sustainable performance at the organizational level (Shahzad et al., 2020). Furthermore, research indicates that firms combining KM strategies, especially in conjunction with Artificial Intelligence (AI) capabilities, are better equipped for digital transformation and sustainable competitive performance (Haefner et al., 2021; José Lopes Gomes et al, 2024; Weritz et al., 2024). AI has revolutionized knowledge governance and organizational learning by enabling predictive analytics, personalized knowledge recommendations, and automated knowledge workflows (Russell & Norvig, 2020; Kaplan & Haenlein, 2019; Davenport, 2018). However, AI adoption raises new challenges. Issues of ethics, inclusiveness, and governance, including algorithmic bias, accessibility, and responsible use of data, require organizations to design responsible AI frameworks (Kaplan & Haenlein, 2019; Davenport, 2018; Villegas-Roca et al., 2025, Vitellaro et al.,2024).  Additionally, gender and board diversity are increasingly recognized as critical factors influencing knowledge flows, decision-making quality, and innovative capacity (Terjesen et al., 2009; Zhang et al., 2017; Schifilliti & La Rocca, 2024). Their interaction with AI-driven KM systems presents a promising but underexplored avenue for research. Therefore, organizations must prioritize inclusive AI design and ethical safeguards to ensure that technological progress aligns with fundamental principles of fairness and social responsibility. This track is in line with the UN Sustainable Development Goals (SDGs) number 5, 9, and 10. Goal 5 promotes offering men and women the same chances in the workplace and an appropriate involvement at all levels, while Goal 9 attempts to assist technological advancement, research, and innovation, particularly in emerging nations, to increase market integration and increase accessibility to banking and financial services for small organizations and businesses. Reducing inequality inside and among countries is the aim of Goal 10, intending to advance and enhance people’s cultural, economic, and legal inclusion, regardless of age, gender, impairment, race, ethnicity, birthplace, religion, as well as other conditions.

Track main topics

This track invites contributions exploring the interplay between AI, KM, and sustainability, with a special emphasis on innovative strategies, emerging challenges, and ethical implications. We welcome both theoretical and empirical papers, as well as case studies that provide insights into the evolving role of KM and AI in shaping sustainable competitiveness, including but not limited to:

• KM as a driver of innovation, sustainability, and competitive advantage;
• The effect of AI on inclusion and equality;
• KM and AI integration for sustainable value creation;
• Corporate governance, ethics, and responsible AI adoption in companies;
• Artificial Intelligence challenges and threats to sustainable competitiveness;
• Gender and board diversity as enablers of innovation and knowledge-based strategies;
• AI and KM in green innovation and digital transformation.
• KM and social innovation;
• Artificial Intelligence and its contribution to economic, social, and environmental sustainability;
• KM and startups’ innovation capability.