Today, it seems everyone wants to use AI for everything. But we believe in using automation and algorithms to take care of the boring, technical stuff so that humans can do what they’re meant to: Use their imaginations and strategic thinking to make amazing content. 

Below, we outline the advantages of leveraging AI for metadata tagging and how it can clear the way for more creative endeavors.

Challenges of manual metadata tagging

As you’re likely aware, humans have performed metadata tagging manually for years. But, like with countless other administrative tasks, the adoption of AI has enabled users to streamline this previously tedious process with automated tagging. 

What makes the traditional approach to metadata tagging inefficient? 

• It’s time-consuming. Manually entering metadata is just another task to complete before finalizing assets. 
• It lacks standardization. Establishing an automated system for metadata tagging ensures every box is checked every time a file is updated.
• It’s prone to errors. Humans make mistakes, and that’s to be expected. Algorithms don’t forget or have a bad day.

How AI helps with metadata tagging

For those who don’t know, AI is incredibly helpful for managing and analyzing large amounts of data, and metadata is no exception. In metadata tagging, advanced algorithms are employed to scan a selected asset for key characteristics and identifiable information. The appropriate tags (e.g., location, usage rights, visual contents, and so on) are then applied to the file automatically.

So, what are the advantages of leveraging AI to perform metadata tagging? Apart from simplifying media management for nontechnical users, opting for automated tagging over manual tagging means your teams can:

Eliminate manual work, reserving efforts for creative priorities

When the boring stuff is taken care of reliably (and automatically), the fun stuff gets priority. In other words, you’ve sidestepped the previously time-consuming task of manual tagging, which frees up time and energy for creative efforts. 

AI metadata tagging automatically identifies faces, objects, keywords, and locations and tags footage at scale. That means no more endless searching through files and long-winded scene-by-scene logging. This consequently shifts hours of grunt work off of editors’ plates and keeps projects moving faster from concept to delivery. As a result, creators have the capacity to ideate, experiment, and deliver. AI metadata tagging essentially transforms your media library from a roadblock into a springboard.

Improve searchability and access, streamlining collaboration between teams

AI metadata tagging adds structured metadata to every file for instant retrieval, making your entire library browsable instead of submerged in a sea of disorganization. When assets are easy to find, teams can quickly identify what they need and access it seamlessly, speeding up production time and cutting down on repetitive requests and rework.

Reduce human error and inconsistency to fuel faster repurposing and versioning

Not only are metadata tags applied swiftly and automatically, but tagging is also executed uniformly, allowing for much-needed consistency across assets and platforms. This minimizes (or even virtually eliminates) mislabeling or forgotten keywords — nothing gets left out.

Additionally, teams can more easily locate reusable content for new formats, channels, and campaigns. In this way, proper tagging supports quick turnarounds on cutdowns, trailers, social edits, and more. 

Activating creative momentum through automation

AI metadata tagging is just one of the numerous ways that automation can enrich your media library and boost productivity. Leveraging AI to simplify and streamline such tasks frees up your team to focus on more strategic and creative endeavors. Take advantage of AI-driven tools and features, such as auto-tagging, instant transcription, and more, with iconik.