The integration of Artificial Intelligence (AI) into organisational contexts is profoundly reshaping how knowledge and skills are developed and managed. The shift towards competence-based learning (Rychen & Salganik, 2003; Tahirsylaj, 2025) is being accelerated by AI-driven personalisation, adaptive learning systems, and the rise of micro-credentialing and lifelong learning ecosystems. Similar dynamics are emerging within organisations, where AI supports employees in filtering information, accessing just-in-time knowledge and creating personalised learning paths embedding continuous learning into the flow of work (Jarrahi et al., 2023). In this view, AI fosters mutual learning processes between humans and machines, enabling a co-evolution of algorithmic intelligence and human judgment that drives new organisational capabilities (Jarrahi et al., 2024). “I learn therefore I am”, as a contemporary reinterpretation of Descartes’ dictum “Cogito, ergo sum”, encapsulate the epistemic shift underpinning both human and artificial intelligence Machine learning is foundational to AI, the key lies in the word itself: learning.

AI systems learn rapidly, processing and integrating vast bodies of knowledge that far exceed individual human capacities. With the pursuit of artificial general intelligence, this trajectory reflects an accelerating quest toward exponential cognitive expansion.

Nevertheless, while AI systems and robotic agents may execute tasks with superior speed and precision, they remain devoid of the intrinsic value and depth derived from shared human experience and meaning; ultimately, the one dimension AI cannot replicate is humanity itself. As Blake’s metaphorical “bow of burning gold” (Blake, 2008) reminds, the challenge lies not merely in resisting technological dominance but in harnessing it toward a higher human purpose. The question, therefore, is whether organisations and societies are prepared, both internally and externally, to help humanity adapt at pace, shaping AI into a trusted ally so that human and artificial intelligence can co-create a more just, hopeful, and compassionate society. In this sense, building a new narrative for humans and humanity becomes integral to the responsible integration of AI within organisational and learning systems.

A growing body of research investigates AI’s role in upskilling and reskilling workers to align with rapidly changing work ecosystems (Morandini et al., 2023). Technological progress is transforming workforce capabilities, demanding new competencies and adaptability in AI-augmented workplaces (Santana & Díaz-Fernández, 2023). Beyond individual skills, organisations require leadership that combines AI literacy, digital adaptability, and strategic foresight to navigate hybrid human–machine environments (Giraud et al., 2021; Giraud et al., 2023).

While AI-driven automation has transformed business processes, its impact on learning and development remains underexplored (Bhatt & Muduli, 2023). Existing studies have examined AI in training from three main perspectives. First, the learner experience, where AI-powered platforms reshape engagement, motivation, and knowledge retention (Pavitra & Agnihotri, 2023). Second, the role of trainers, as AI redefines knowledge transmission and raises questions about integrating technology while preserving a human-centred approach (Maity, 2019; Dixit & Jatav, 2024). Third, the changing role of HR professionals, who are shifting from managing training logistics to strategic workforce planning (Pavitra & Agnihotri, 2023). AI also enhances training personalisation and efficiency through adaptive content, real-time feedback, and data-driven performance monitoring (Bhatt & Muduli, 2023; Chen, 2023).

In parallel, knowledge management (KM) is undergoing a conceptual transformation. Foundational models (Nonaka & Takeuchi, 1995; Davenport & Prusak, 1998) are being reinterpreted through the lens of generative AI, which extends the SECI spiral into new “GRAI logics” of knowledge creation (Böhm & Durst, 2025). AI now operates across all KM processes—creation, storage, sharing, and application—enabling predictive insights, automated retrieval, and conversational access to knowledge, while requiring employees to develop new reflective and interpretive competences (Jarrahi et al., 2023). Contemporary KM research highlights the fusion of human and machine intelligence (Tsoukas, 2009; Alavi et al., 2024), exploring how AI can augment yet also challenge organisational epistemologies and practices of knowledge sharing (Von Krogh, 2018).

However, challenges persist. Ethical issues such as fairness, transparency, and data privacy remain critical (Chen, 2024). Algorithmic bias and employee resistance may hinder adoption, as workers fear job displacement or reduced human interaction (Dixit & Jatav, 2024). Achieving balance is essential to preserve mentorship, emotional intelligence, and contextual adaptability as core elements of learning (Nimmi et al., 2022).

This track aims to deepen understanding of AI’s transformative impact on organisational learning, knowledge management, and HR development. It invites empirical and theoretical contributions addressing the opportunities, challenges, and implications of AI-driven learning and knowledge systems across diverse organisational and industrial contexts.