Integrating human-centric AI into corporate learning: balancing automation with empathy

Purpose The aim of this study is to examine how organizations can integrate human-centric Artificial Intelligence (AI) into corporate learning programs to balance automation with empathy for better learning experiences. It explores strategies to successfully implement AI into corporate learning without compromising the human aspects. Design/methodology/approach The study employs a qualitative research method using semistructured interviews with 20 corporate learning practitioners. These interviews reveal insights into strategies, challenges, and opportunities for achieving this balance. Findings Key findings include strategies such as employing hybrid models that combine AI-driven processes with human interaction, using sentiment analysis to enhance AI’s empathetic integration, implementing role-based access to trainers, fostering feedback loops, and encouraging employee involvement in program design. Research limitations/implications A key limitation of the study is its reliance on qualitative data drawn from a small sample size, which may not fully capture broader industrial trends. Future research could incorporate larger, more diverse samples and quantitative analysis to validate and expand upon these findings. Practical implications This research offers practical implications for organizations seeking to integrate AI into corporate learning. It guides the design of human-centered AI systems that prioritize learner needs and engagement while balancing automation with human interaction. Originality/value This study contributes to the under-researched intersection of AI and human-centered learning in corporate systems. By offering a framework and strategies for balancing automation and empathy, it advances the development of responsible, inclusive, and effective AI-enhanced learning environments.