The Machine/Human Agentic Impact on Practices in Learning & Development: A Study across MSME, NGO, and MNC Organization

Journal: Personnel Review

Artificial Intelligence (AI) is emerging as a disruptive technology, with increasing adoption by various HRM functions, facilitating problem-solving, efficiency enhancement, and organizational improvements. The adoption of AI in HRM, specifically in learning and development (L&D), is limited but can represent a significant shift in how human intellect and effort is replicated by technology. However, it can potentially transform L&D-related practices as it allows technology agents to replace human cognitive function while improving operational effectiveness and helping to create a sustainable and effective learning organization. AI adoption can lead to the emergence of new forms of conjoined agencies, where human and technological agents work together for L&D tasks.

 The study explores the impact of human and machine agencies on L&D practices in different types of firms, including MSMEs (Micro, Small, and Medium Enterprises), NGOs (Non-Governmental Organizations), and MNCs (Multinational Corporations), to understand the reasons for the limited AI adoption in L&D. The researchers conducted in-depth interviews with L&D professionals and experts to gather qualitative insights and gain a deeper understanding of the challenges and considerations related to HRM digitization and AI adoption for L&D, allowing flexibility for probing and exploring emergent themes. By adopting a practice lens, the study attempts to understand how human and technological agents participate in routine, protocol, and action selection in L&D within various types of firms. This study fills that knowledge gap and provides insights into protocols and frameworks for using AI in L&D.

The study emphasizes the interdependence between human activity, technical capabilities, and the structured context of technology adoption in practice. The authors propose a conceptual framework for L&D structures and practices through the study, theorizing on the human-technology agentic effect and AI adoption in L&D across various types of organizations based on the analysis. Considering internal and external contexts, this insight is valuable for understanding how technology is adopted and how it can play a transformative role in businesses. As such, the emerging findings are highly relevant to practitioners who can assess the adoption of AI in their specific contexts.