The AIGC revolution: Enhancing content generation efficiency in Chinese broadcast media

Abstract

Despite rapid advancements in Artificial Intelligence-Generated Content (AIGC) across various sectors, research on its specific implementation within the Chinese broadcast media industry remains limited, particularly regarding its impact on content generation efficiency, quality, and associated challenges. This exploratory study investigates AIGC technology applications in content creation workflows within the Chinese broadcast media, contributing unique insights from China’s distinctive digital ecosystem shaped by the “Great Firewall” and specific regulatory environment. Guided by the Media Ecology Theory, this research employed a qualitative approach through semi-structured interviews with key executives from two leading Chinese film and television companies. The study reveals that AIGC technology brings significant operational efficiency improvements, including approximately 50% increased 3D rendering efficiency and substantial reductions in time and costs for scriptwriting, graphic design, and video editing. These findings indicate a transformative shift in traditional content creation practices, highlighting AIGC’s potential to enhance productivity while reducing operational costs. However, the research identifies critical implementation challenges, including workflow modelling complexity and content duplication issues that create “efficiency paradoxes” where apparent gains mask underlying complexity increases. The study contributes novel theoretical insights by extending the Media Ecology Theory to understand AIGC as an environmental force that fundamentally transforms production ecosystems through environmental restructuring, cognitive reframing, and cultural redefinition. Methodological limitations include a small sample size of two companies and executives, limiting statistical generalisability while providing analytically rich insights into early-stage AIGC deployment within China’s unique media landscape. Despite implementation challenges, high user satisfaction with AIGC-generated content suggests broad application potential. This research offers valuable insights into balancing AI-driven efficiency with human creativity and provides a foundation for future studies on integrating AIGC technology into media production within distinctive cultural and regulatory contexts.