AI-enabled media asset management offers new revenue opportunities

AI-enabled media asset management offers new revenue opportunities

August 6, 2018

It is well known that manual tagging and cataloging of media assets is a time consuming and an uneconomical activity, which affects the usefulness and profitability of media assets. However, with the emergence of Artificial Intelligence (AI), the perception is beginning to change. Audiovisual content is being transformed into dynamic assets using metadata enriched by a combination of AI techniques and manual processes. According to T Shobhana, Vice President and Head of Global Marketing and Communications for Prime Focus Technologies, automated AI technology can aid faster content discovery and improve productivity.

AI technology brings powerful and faster capabilities that help in content discovery for reuse by enriching metadata. For example, content can be auto-tagged using object recognition as well as face/location recognition technology. As a result, the enriched metadata can be used to spot specific areas where compliance issues have occurred. Similarly, improvements in speech-to-text and image recognition algorithms are automating parts of the metadata process, making the metadata useful for both new and legacy content use cases.

Furthermore, the integration of mobile application management solutions with metadata management tools, extensive data models and AI-based functionalities, hand-in-hand with human quality control and judgment, will help organizations achieve effective labeling of vast quantities of existing and new content.

In fact, the improved accuracy and efficiency will deliver, enhanced business efficiencies and results across cataloging, editing and mastering operations. By making it faster and easier to find content, AI-enhanced systems will create new revenue opportunities.

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Brought to you by Scope e-Knowledge Center, an SPi Global Company, a trusted global partner for Digital Content Transformation Solutions, Knowledge Modeling (Taxonomies, Thesauri and Ontologies), Abstracting & Indexing (A&I), Metadata Enrichment and Entity Extraction.

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