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Optimizing Global Expansion in Media and Entertainment Through AI-Driven Engagement and Cultural Relevance

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Abstract

This paper explores how media and entertainment (M&E) companies can achieve scalable global expansion while preserving cultural authenticity and user trust. As emerging markets become key drivers of digital growth, companies face the dual challenge of localizing experiences without diluting brand identity. The paper examines how artificial intelligence (AI) is being leveraged to personalize content, curate user journeys, and adapt digital interfaces across geographies. Through case studies of platforms like Netflix, Spotify, and Disney+ Hotstar, it analyzes the strategic role of hybrid AI-human workflows, regional content hubs, and modular branding systems. Special attention is given to the ethical implications of algorithmic decision-making and the importance of transparency in AI-driven engagement. Ultimately, the paper provides a strategic framework for global M&E leaders to balance innovation, localization, and trust while navigating the complexities of digital transformation in emerging economies.

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This white paper was developed as part of an independent study at Cornell Tech. It examines the intersection of artificial intelligence, global expansion, and cultural localization in the media and entertainment industry. Focusing on emerging markets, the paper offers a strategic lens on how companies can use AI-driven personalization and regionally tailored engagement strategies to scale operations without compromising cultural relevance or user trust. It includes case studies, industry frameworks, and forward-looking insights to support leaders navigating the challenges of digital transformation across borders.

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Date Issued

2025-06-01

Publisher

Cornell University

Keywords

Artificial Intelligence, Cultural Localization, Customer Engagement, Digital Media Strategy, Emerging Markets, Global Expansion, Media and Entertainment, Personalization Algorithms, Platform Strategy, User Trust and Ethics

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Government Document

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Attribution-ShareAlike 4.0 International

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case study

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