Algorithmic Editorial Power: AI, Capital, and Journalistic Autonomy in Smart Newsrooms

This article examines how artificial intelligence reshapes editorial power and journalistic autonomy in contemporary newsrooms through the concept of Algorithmic Editorial Power. Drawing on a qualitative, multi-method case study combining analysis of The Queen of News 2, semi-structured interviews with journalism practitioners, and supplementary newsroom materials, the study situates AI within longer-standing regimes of audience analytics, performance measurement, and algorithmic governance. The findings suggest that AI may intensify metric-driven governance when embedded in managerial and commercial arrangements, while remaining limited in situations requiring contextual interpretation, ethical judgment, and responsiveness to uncertainty. Journalistic autonomy is therefore neither simply eliminated nor preserved, but negotiated through practices of compliance, adaptation, resistance, and moral boundary-setting. By conceptualizing Algorithmic Editorial Power as the capacity of AI-enabled and algorithmically mediated systems to shape editorial decisions and redistribute professional authority, the study offers a framework for understanding how technological capabilities interact with organizational priorities and professional judgment in datafied, platform-dependent newsrooms.