When algorithms become employees: reframing learning and development in the age of agentic AI
TL;DR
This study examines agentic artificial intelligence as “digital employees” and extends learning & development frameworks to include their training and ethical alignment, and reframes agentic AI as part of the workforce and extends L&D to include artificial agents.
Abstract
This study examines agentic artificial intelligence (AI) as “digital employees” and extends learning & development (L&D) frameworks to include their training and ethical alignment. A conceptual, theory-integrative approach is adopted, drawing on socio-technical systems theory and the resource-based view (RBV), supported by evidence such as bias on Airbnb. Agentic AI systems act as organizational participants whose learning shapes decisions. Reframing their learning as an L&D responsibility requires core L&D activities to be rethought for non-human learners. Without governance, these systems may amplify bias. The study is conceptual and lacks empirical validation. Future research should test AI-focused L&D models and examine human-AI collaboration. The study can be used to implement structured AI training, align systems with ethical norms, and strengthen governance for responsible AI use in organizations. The paper reframes agentic AI as part of the workforce and extends L&D to include artificial agents, clarifying what the L&D function specifically gains from this perspective.