From Fragmentation to Co-Optimization: A Socio-Technical Thematic Analysis of AI Integration in Media Organizations

As technological entanglements deepen, media organizations are undergoing a fundamental transformation marked by increasingly complex intersections between human and algorithmic agency. Drawing from 33 peer-reviewed studies, this paper develops a socio-technical synthesis of AI integration, identifying four interrelated subsystems—social, technical, contextual, and interactional—that collectively reshape roles, hierarchies, workflows, and institutional values in media organizations. Grounded in socio-technical systems theory, the study frames ‘co-optimization’ as a conceptual anchor to capture the recursive interplay between human judgment and algorithmic logic in contemporary media environments. Theoretically, the paper reframes AI integration as a dynamic process of mutual adaptation; practically, it offers actionable frameworks for responsible AI governance in journalism and media practice. The findings indicate that AI integration in media organizations is a dynamic, iterative process that involves continuous negotiation across social, technical, and contextual dimensions. The analysis underscores the importance of considering these dimensions to effectively balance the benefits of AI with journalistic integrity and public trust.