Generative Artificial Intelligence (GenAI) is ushering in a profound transformation in the way organizations create, manage, and leverage knowledge (Nguyen et al., 2025a), knowledge assets and processes critical for realising human-AI advantage through people management processes. Beyond automating routine tasks, GenAI applications, using large language models (LLMs), intelligent search engines, and adaptive knowledge platforms, are revolutionizing how knowledge is discovered, synthesized, and applied across all levels of decision-making (Nguyen et al., 2025a). Yet, little is known of the core processes and human and AI capital that is needed to deliver value and reduce the gap between investments in these technologies and the returns they generate. As work becomes more digitalized, distributed, and data-rich, knowledge management (KM) processes must evolve to ensure that organizations remain innovative, agile and deliver competitive advantage (Kemp, 2024; Malik et al., 2022; Nguyen & Malik, 2022).

This track explores the pivotal role of GenAI in reshaping AI-based knowledge assets, processes and ecosystems that get embedded within diverse organizational contexts. It seeks to understand how GenAI can enable real-time knowledge curation, foster more inclusive and equitable knowledge-sharing practices, and enhance cross-border and cross-functional collaboration/substitution for sustained competitive advantage (Krakowski et al., 2023; van Riel et al., 2025). Acknowledging the critical challenges related to algorithmic bias, data privacy, ethical use, and the risk of over-reliance on machine-generated insights, managers and employees must not only know how to navigate through these tensions, develop robust governance frameworks, responsible AI practices, but also employ strategies that prioritize human creativity and judgment alongside AI augmentation, which leads to new insights for delivering process efficiencies and new and untapped markets and ideas.

We invite research that examines the opportunities and risks of GenAI from multiple perspectives, including technological, managerial, ethical, and sociocultural. But we are most interested in exploring how human and machine interactions in LLMs and other technologies can influences knowledge flows and decision quality, which it transforms employee roles, engagement, and the implications this has for workforce development and consequently organizational learning and performance. Scholars and practitioners are encouraged to share theoretical advances, empirical evidence, methodological innovations, and case studies that address questions such as:

Strategic and Organizational Perspectives

• How can GenAI be strategically aligned with corporate knowledge management systems to enhance competitiveness, agility and deliver value?
• In what ways can GenAI support organizational resilience and adaptation to respond to rapid market changes and growth opportunities?
• How do different governance structures (centralized vs. decentralized KM) affect the success of GenAI integration?
  What are the implications of GenAI for open innovation ecosystems and inter-organizational knowledge networks?

Human–AI Collaboration/Substitution and Workforce Dynamics

• How do employees develop trust in GenAI-generated knowledge, and what factors influence adoption or skepticism?
• What leadership styles and change management strategies are most effective for promoting GenAI-enabled knowledge sharing?
• How does GenAI impact psychological ownership of knowledge and employees’ sense of agency in decision-making?
• How can organizations balance efficiency gains from GenAI with the need to preserve tacit knowledge and human expertise?

Ethics, Equity, and Governance

• What frameworks can ensure fairness, transparency, and accountability in GenAI-mediated knowledge exchange?
• How can organizations prevent knowledge distortions or biases introduced by GenAI algorithms?
• What policies should be implemented to protect privacy, intellectual property, and data sovereignty when using GenAI for KM?
• How can GenAI be designed or deployed to promote inclusion, accessibility, and equitable participation in knowledge sharing?

Future of Work and Learning

• How does GenAI affect the development of future-ready skills, lifelong learning, and career progression?
• In what ways can GenAI facilitate adaptive learning organizations and knowledge retention amid high employee turnover?
• How might GenAI-driven KM systems reshape roles in HR, marketing, or R&D to prioritize knowledge-based value creation?
• What are the potential unintended consequences (e.g., deskilling, over-automation) of GenAI on knowledge workers and organizational learning cultures?

By fostering dialogue across disciplines such as marketing, human resource management, information systems, and organizational behavior, this track aims to build actionable insights for both academia and industry. Participants will gain a deeper understanding of how to harness GenAI’s transformative power responsibly, ensuring that knowledge processes not only drive performance and innovation but also support ethical, sustainable, and inclusive workplaces.