Solving Localization & Compliance Pain Points with AI for the Video Streaming Industry
Originally Aired - Sunday, April 14 | 10:40 AM - 11:00 AM PT
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It is a business-critical requirement for broadcasters to distribute content to multiple platforms and countries, requiring different languages, licensing, and rules (like what can or can not appear in the content). More broadly, businesses are looking to move from an in-house platform strategy to selling IP globally again via FAST (free ad-supported streaming television) and other areas as a method of realigning revenue strategies. The studios need to sell content and they need to do it quickly. This means manually creating multiple (dozens or even hundreds) of versions for a single title to meet the numerous compliance requirements, technical specs, language and localization needs. These scenarios often stress the storage environment and make moving into the cloud an expensive proposition. It also delays distribution when unclear asset versions require manual attention to confirm, thus delaying monetization opportunities. The impact of these challenges are far-reaching and multi-faceted, warranting a closer examination. These complexities manifest in a variety of ways, such as: For those operations that are already in the cloud, or working with external vendors, they pay significantly more than they need to. For those operations still on legacy, on-premises solutions, packaging and delivery workflows are complex, slow, and unable to expand and easily grow their business. Disparate, siloed content libraries that make it difficult or impossible for sales to sell their content catalog quickly and efficiently, which is key to competitive success. The added compute power and storage to maintain a “larger than necessary” content library and archive is also a step in the wrong direction for sustainability. This paper and presentation will discuss how broadcasters can leverage AI and media processing technology to effectively organize and de-duplicate their content libraries. This involves using machine learning algorithms to “fingerprint” the content within media assets as well as automatically detecting and extracting detailed metadata from the content, allowing for faster and more accurate identification of media assets. By implementing AI-driven technology, broadcasters can reduce the need for manual versioning and compliance checks, as well as streamline their packaging and delivery workflows via automation. This not only saves time and resources but also allows for quicker distribution to multiple platforms and languages, increasing potential monetization opportunities.
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