To understand just how powerful and valuable AI auto-tagging is for understanding and tagging media archives, we must explore some of the cognitive engines that are available with the technology. Exploring the power of AI cognitive engines in auto-tagging If content is not tagged accurately, that media asset can become buried in the archiveīut this all changes with AI, which can use a variety of cognitive engines to tag media.It’s extremely laborious to tag content manually.Missing the necessary expertise to implement tagging.Some content ends up tagged while others don’t for various reasons, including a lack of a metadata plan.Lack of standard and metadata strategy for tagging content as multiple people touch these assets, creating inconsistencies.This is easily solvable with metadata, but historically, creating metadata tags has not come without challenges. Most don’t have a complete inventory of their content and struggle to take advantage of their content because it’s hard to find assets and takes a long time to resurface them. Media companies across industries, from sports teams and federations to film studios and news organizations, have years of content that they have accumulated over the years. Potentially lead to inaccuracies with so many people touching the assets.A lot of overhead to maintain the team for the duration of the project.Months of not more than a year to complete.Without it, they would have to hire a team of interns and use employees to tag this content manually, which would take: How to leverage AI auto-tagging How is AI auto-tagging done?Ĭompanies with large media archives need AI auto-tagging to ease the lift in accurately and quickly tagging all their digitized content. What are the benefits of AI auto-tagging? In this blog, we’ll discuss these key aspects of AI discovery and indexing of metadata:Įxploring the power of AI cognitive engines in auto-tagging It also helps companies control who can access their content and ultimately distribute it. Both internal teams, or external users in the form of partners or customers, can more easily and quickly find the media that’s most relevant to their search. In digital asset management, which we described in-depth in a previous blog, metadata tags are used to make content easier to find with search queries. This is a modern approach to metadata tagging, which creates a term that describes a keyword or phrase and assigns these metadata tags to a media asset. AI auto-tagging is the process in which artificial intelligence is used to tag media files with metadata.
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