?
Artificial intelligence and tacit knowledge in radio content production
Purpose
This study explores how artificial intelligence (AI) is transforming tacit knowledge practices and shared leadership in radio broadcasting – a sector characterized by creativity, spontaneity and strong audience engagement. The aim is to assess whether AI can complement or replicate the human dimensions of knowledge sharing in cultural and media organizations.
Design/methodology/approach
Grounded in Media Richness Theory (MRT), the study adopts a qualitative methodology based on semi-structured interviews with professionals from the Italian radio industry. The empirical material was analysed through thematic analysis to identify key dimensions and tensions in the relationship between AI systems and human expertise.
Findings
The findings highlight a complex interplay: while AI enhances efficiency and content personalization, it falls short in reproducing tacit knowledge dimensions such as empathy, creativity and improvization. Moreover, the integration of AI reshapes professional roles and leadership structures, revealing the value of non-hierarchical, shared leadership in sustaining knowledge flows within high-creativity contexts.
Research limitations/implications
The qualitative design limits generalizability; future studies could integrate quantitative or cross-cultural comparisons to validate and broaden insights.
Practical implications
The study provides actionable recommendations for media organizations undergoing digital transformation, emphasizing the need to preserve human expertise, redesign workflows to balance automation with creativity and foster shared leadership.
Social implications
The findings suggest that maintaining authentic human–audience connections is vital to sustaining cultural diversity and emotional engagement in an era of increasing automation.
Originality/value
This study extends knowledge management frameworks into the creative industries, an underexplored domain where embodied and experiential knowledge is central. By applying MRT to AI-mediated broadcasting, it introduces a novel perspective on how automation interacts with tacit knowledge and performative leadership.