Lomonosov Moscow State University (professor)
Russian Federation
UDC 654.197
Introduction. The relevance of this study is driven by the need for a theoretical understanding of generative AI's impact on the television industry. The article argues that a systematic methodological framework is required to analyze these processes, which may not just enhance but fundamentally disrupt the industry. Aim. To substantiate the applicability of Clayton M. Christensen's theory of disruptive innovation for the systematic analysis and forecasting of the transformation of television broadcasting models in the era of generative AI. Methodology and research methods. The research methodology is based on the theory of disruptive innovation. Methods of theoretical analysis and conceptual modeling are used to interpret the characteristics of AI and develop market transformation scenarios. Results. It is proven that generative AI has the key characteristics of a disruptive innovation (cost reduction, democratization of access, new value propositions). The mechanisms of 'low-end' and 'new-market' disruption are analyzed. The study reveals that traditional broadcasters face the classic 'innovator's dilemma,' which hinders their ability to respond adequately to the threat. Scientific Novelty. For the first time, the theory of disruptive innovation is systematically applied to analyze the impact of generative AI on television, enabling a shift from describing technologies to explaining and forecasting media market dynamics. Practical Significance. The research findings provide a strategic tool for media managers to assess the risks and opportunities of AI and to make informed business decisions under conditions of high uncertainty.
generative AI, disruptive innovations, television broadcasting, media transformation, business models, media economics
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