Generative AI in Media Production: From Content Creation to Metadata Intelligence

Authors: Nitin Addla

DOI: https://doi.org/10.37082/IJIRMPS.v14.i3.233148

Short DOI: https://doi.org/

Country: United States

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Abstract: Generative artificial intelligence (GenAI) is fundamentally transforming the media production industry, redefining workflows across content creation, post-production, distribution, and metadata management. This paper presents a comprehensive analysis of GenAI adoption, architectures, and real-world deployments across broadcast, streaming, and digital media organisations. Drawing on 2026 industry data encompassing a 76% adoption rate among media enterprises, $5.38 billion in creative AI market capitalisation, 60–70% production cost reductions, and a 300% increase in content output, we examine the technical architectures enabling these outcomes. We introduce a layered GenAI production architecture spanning multimodal ingestion, AI processing cores (large language models, diffusion models, video generation, speech synthesis), content distribution, and metadata intelligence pipelines. Case studies from CNN, BBC, Netflix, and Spotify illustrate production-scale deployments achieving measurable ROI. We analyse implementation challenges including intellectual property rights, regulatory compliance, workforce transition, and quality assurance. A structured cost–benefit analysis across three deployment tiers demonstrates net positive returns within 8–12 months. Future directions encompass real-time AI agents, agentic workflows, and next-generation multimodal architectures. This research provides a practitioner-oriented framework bridging academic innovation and enterprise deployment.

Keywords: Generative artificial intelligence, media production, large language models, diffusion models, metadata extraction, content creation, streaming media, broadcast technology, AI workflows, digital media

Paper Id: 233148

Published On: 2026-05-08

Published In: Volume 14, Issue 3, May-June 2026

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