Welcome to the last chapter of our Digital Asset Management series. So far, we’ve taken a deeper look into digital, media, brand, and video asset management along with the best practices for metadata tagging, all of which can help you better manage your assets and uncover new revenue streams.
In this chapter, we’ll take a look at AI-powered auto-tagging and discuss these key aspects of AI discovery and indexing of metadata:
- How is AI auto-tagging done?
- Exploring the power of AI cognitive engines in auto-tagging
- What are the benefits of AI auto-tagging?
- How to leverage AI auto-tagging
What is AI auto-tagging?
AI auto-tagging is the process by which artificial intelligence automatically applies descriptive metadata to media files. Instead of requiring a person to manually review each file and add keywords, AI models analyze the content itself, such as images, videos, or audio, and generate tags that identify objects, people, text, scenes, or even emotions present within the asset. These tags function as structured metadata, enabling more precise organization and retrieval.
This represents a modern evolution of metadata management. Traditional tagging often relied on human input, which could be slow, inconsistent, and incomplete, especially when archives contain thousands or millions of assets. AI auto-tagging addresses these challenges by offering speed, scale, and consistency. It creates meaningful terms, keywords or phrases, that describe the essence of a media file and then assigns them as metadata tags directly to the asset.
In the context of digital asset management (DAM), metadata tagging is essential for discoverability. As we’ve described in-depth in a previous blog, tags make content much easier to find using search queries. For example, a marketing team member searching for “summer campaign” or “red sneakers” can instantly retrieve relevant images or videos, rather than browsing through folders manually. The same applies to external partners or customers. AI-generated metadata ensures that the right files appear at the right time, improving efficiency and user experience.
Auto-tagging lays the groundwork for more advanced capabilities. By enriching assets with detailed metadata, businesses can unlock AI-driven personalization, recommendation systems, and analytics on how content is being used. In this way, what begins as a productivity tool evolves into a foundation for strategic insights and smarter decision-making.
How is AI auto-tagging done?
Companies with large media archives need AI auto-tagging to ease the lift in accurately and quickly tagging all their digitized content. Without it, they would have to hire a team of interns and use employees to tag this content manually, which would take:
- Months if not more than a year to complete
- A lot of overhead to maintain the team for the duration of the project
- Potential inaccuracies with so many people touching the assets
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. 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.
This is easily solvable with metadata, but historically, creating metadata tags has come with challenges. Manual tagging is a tedious and time-consuming process that isn’t always accurate—a lack of standards and metadata strategy for tagging content can lead to inconsistent tagging and missed content, and a team without the necessary expertise to implement tagging could lead to even more issues. If the content is not tagged accurately, that media asset can become buried in the archive.
It’s no surprise then that nearly 80% of DAM offerings with AI have auto-tagging. Its high adoption rate is directly correlated to the value it offers in reducing manual work and searchability. Yet auto-tagging alone only provides a slight edge. Organizations really need other, robust AI capabilities in addition to auto-tagging to maximize what they can do with their media.
Exploring the power of AI cognitive engines in auto-tagging
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.
Here is a rundown of the top AI engines:
Sometimes called acoustic fingerprinting, audio fingerprinting engines use a specific signature or fingerprint to identify pre-recorded audio snippets contained within audio and video files.
Face recognition, which is also interchanged with face identification or face ID, analyzes human faces in images and video, scoring them based on how similar they are to known faces.
Speaker recognition, also called speaker identification, can scour through a piece of audio and determine when speakers change and who those speakers are.
Used interchangeably with logo recognition, this engine is used to detect and identify images that represent entities such as retailers, sports teams and groups, media networks, products, companies, and other brands within images and videos.
Also referred to as object recognition, object detection engines help detect specified objects within videos or still images.
What are the benefits of AI auto-tagging?
As you can imagine, these engines offer immense benefits when it comes to tagging media. AI auto-tagging can work with any type of content that needs to be tagged, including images, videos, audio files, documents, and more, ultimately leading to:
- Faster and more cost-effective metadata tagging that can reduce time by up to 50%.
- AI-enabled discovery of media assets has the ability to help surface properly tagged content in seconds to minutes versus hours to days manually.
- A higher volume of tagged data unlocks assets that organizations might not have known they had, helping them unlock the full potential of their content.
- Assurance that future content will be tagged appropriately so past efforts are not frozen to the time they were created but used for future campaigns.
- Real-time tagging capabilities makes media for live sporting events that much more accessible to all stakeholders involved.
For the latter, this is especially beneficial for live events (particularly live sporting events) making content readily available and searchable in a matter of minutes to advertisers and partners. This allows companies to move faster when producing and sharing content, enabling timely customer engagement.
How to leverage AI in auto-tagging
Using AI in auto-tagging media allows companies to free up their teams who would have to manually tag content. This makes managing a massive archive not only more cost-effective but also helps companies understand more clearly what they have from a content standpoint.
In doing so, it opens the door to them reusing or repurposing content that may still resonate with their audience, saving on production costs and helping extract even greater ROI from their content.
One of the key capabilities of Veritone Digital Media Hub, a media asset management platform powered by Veritone aiWARE is its ability for accurate AI auto metadata tagging.
Using proprietary and third-party cognitive engines of aiWARE, companies can more accurately tag content and understand what they have in their inventory. Only then can companies truly automate, curate, and activate their content so they can easily manage, distribute, and monetize their media.
