Artificial intelligence in organizational learning: a systematic review of applications, opportunities, and challenges

Abstract

Purpose This study investigates how artificial intelligence (AI) transforms organizational learning (OL), examining applications, opportunities, and challenges in fostering adaptive, innovative workplaces. Design/methodology/approach A systematic review of the Web of Science database analyzed 23 peer-reviewed articles (2000–2025) using the TCCM (Theory–Context–Characteristics–Methodology) framework to synthesize trends and gaps. Findings AI enhances OL through socio-technical integration, sensemaking, and human-machine coordination, with tools like generative AI and machine learning boosting informal learning and innovation. However, risks include overreliance, ethical concerns, and limited focus on diverse contexts. Qualitative and conceptual methods dominate, with few longitudinal studies. Research limitations/implications The broad temporal scope may dilute context-specific findings. Future research should prioritize empirical studies in underrepresented settings, focusing on emotional and ethical dimensions. Practical implications Organizations can leverage AI for real-time feedback and informal learning but must ensure ethical alignment, human judgment, and cultural support to enhance agility and inclusivity. Originality/value This pioneering review applies the TCCM framework to a comprehensive AI-OL literature set, offering novel insights into human-AI collaboration and ethical integration.

Ähnliche Arbeiten