Structural Reconfiguration of Streaming and OTT Business Models: Discovery, Personalization, and Traffic Engineering in the AI Era

This chapter theorizes the AI-drivenAI-driven OTT structural reconfiguration of Over the Top (OTT) streaming from a supplementary extension of linear television into a digitally native, algorithmically governed media economy. It defines AI in the OTT context as an integrated stack of machine learning, natural language processing, deep neural networks, recommendation engines, and predictive analytics operating across three strata: intelligent (sensemaking), predictive (forecasting), and prescriptive (optimization). Building on this framework, the chapter analyzes how AI reshapes discovery and hyper-personalization, programmatic advertising and precision monetization, semantic and voice search, predictive churn mitigation, editorial content intelligence, and fraud and integrity assurance. Comparative evidence from Netflix, Amazon Prime Video, Disney+, Hulu, and Roku demonstrates that competitive advantage increasingly derives from proprietary algorithmic identities coupling audience micro-profiling with adaptive interfaces, creative testing, and real-time traffic engineering across devices and networks. The discussion extends to an AI-augmented value chain spanning pre-production ideation, production automation, localization, and rights governance, highlighting an algorithmic aesthetic that modulates affect as well as attention. Finally, it interrogates governance implications of edge AI and federated learning, which decentralize computation while recentralizing control, and maps future horizons in quantum optimization, multi-agent systems, and conversational, potentially sentient interfaces within the emerging Post-Linear Media Intelligence Paradigm.