AI Tagging Explained: How It Works and Why It Matters

Organizations are producing more unstructured data than ever, from videos and images to transcripts, emails, and documents. The challenge is that most of this data sits untouched, hard to search, and even harder to use. Valuable insights get buried, compliance risks grow, and teams waste time digging instead of doing.

AI tagging changes that. By automatically recognizing and labeling the contents of files, tagging adds structure to unstructured data, making information searchable, organized, and usable. Whether it’s surfacing a key moment in a video, flagging sensitive information for compliance, or preparing datasets for AI training, tagging transforms raw content into actionable information.

What is AI tagging?

AI tagging is a machine learning process where algorithms scan media such as images, videos, and documents, then assign metadata tags or labels that describe the content. These tags act like digital markers, making information easier to find, organize, and retrieve later. For example, a tagging system might identify a specific speaker in a video, highlight personal data in a document, or recognize a shouted phrase in an audio file.

Also known as AI auto-tagging, the practice relies on algorithms trained on massive datasets that teach the system to recognize patterns across objects, sounds, and text. Over time, these models improve, enabling tagging solutions to not only locate and identify items but also predict where specific data is likely to appear. Unlike manual tagging, which is slow, inconsistent, and costly at scale, AI tagging makes content retrieval faster, more accurate, and far more efficient.

Some of the common types of AI tags that might be applied to data include:

●      File Metadata: Automatically generated information like creation and modification dates, version history, and ownership.

●      Enhanced Metadata: Context added by analysis tools, such as identifying events of interest within surveillance footage.

●      Classification Markings: Labels that indicate sensitive or regulated information, such as personally identifiable information (PII) under GDPR, PCI DSS, and HIPAA.

●      AI Training Markings: Tags that label data for machine learning, like distinguishing benign software from malware to train cybersecurity models.

Tagging can happen at different levels as well. File metadata is often applied one file at a time, while cloud object storage allows tagging at both the object and bucket levels. These tags are embedded in the object’s header and can be customized by the user.

How does AI tagging work?

AI tagging takes advantage of the power of large language models (LLMs) like ChatGPT or Claude to ingest and interpret unstructured data. The same capabilities that allow an AI assistant to summarize a document or extract action items from a call transcript can also be applied to generate metadata for unstructured files.

Vector databases and object search

AI tagging tools can automatically identify keywords or main themes from a document and store them in a vector database. In this format, information is encoded as high-dimensional numerical vectors, or “embeddings,” that represent their core concepts.

Those embeddings make it possible to search by concept, not just exact wording. For example, a home improvement show may archive past episodes, and a viewer searching for “woodworking” might also want to see episodes about carpentry. Because the two ideas are related in meaning, and therefore close in embedding space, the AI system connects them.

This ability to search for similar concepts dramatically increases the utility of object tags and metadata. Without it, users would need to search with a precise word or phrase to find the right resource.

Multimodal object tagging

The introduction of multimodal LLMs like ChatGPT-4 has expanded the capabilities of AI tagging tools dramatically. Earlier tagging tools were limited to text only, forcing transcription of audio and video files to extract metadata.

With multimodal tagging, LLMs can analyze the full content of media files. This allows them to extract additional tags and metadata that would otherwise be lost. For example, video tagging can use facial recognition to identify individuals in video, while a tool for tagging audio might be able to recognize and tag specific songs.

Benefits of AI tagging

AI tagging transforms unstructured data into a usable asset with benefits that span efficiency, accuracy, and cost savings:

●      Faster Data Access: Teams can locate files without downloading and scanning them in full, reducing both time and processing resources.

●      Accelerated AI Training: Improved searchability makes it easier to identify the right AI training data, cutting time, cost, and effort in preparing models.

●      No More Manual Tagging: Automated tagging replaces repetitive human work, freeing staff from large-scale labeling projects to focus on higher-value tasks.

●      Improved Accuracy: Human efforts can be inconsistent and prone to errors. AI systems learn and improve over time, shifting human involvement to spot-checking and making results more reliable.

●      Greater Scalability: AI tagging scales effortlessly to large volumes of data, cutting the costs that make manual tagging prohibitive.

●      Lower Development Costs: Combined, these benefits reduce the cost of product development and accelerate time to market, boosting overall ROI.

AI tagging use cases

The benefits of AI tagging aren’t just theoretical: they’re already reshaping how organizations manage and use unstructured data. From publishing to e-commerce, AI-generated tags make it possible to realize new insights, streamline workflows, and scale content management in ways that manual methods never could. Some common applications include:

●      Content Publishing: Publishers and marketers use AI-enabled content management systems to tag documents, images, and videos. In media and entertainment (M&E), this accelerates content organization and post-production tasks such as highlight packages, showreels, transcriptions, content moderation, and news curation.

●      Digital Evidence Management: Law enforcement and legal teams can quickly search and categorize massive media libraries, such as identifying the sound of glass breaking across hundreds of hours of security footage.

●      E-Commerce: Online shopping platforms can create product classification and user behavior recommendation systems, allowing customers to have a more intuitive, Google-like search experience.

●      Archiving and Historical Preservation: Historical archives can digitize and categorize documents, photographs, and artworks, preserving culture for future generations and creating new opportunities for access and discovery.

●      Content Moderation and Localization: Platforms can automatically identify and adapt content for different markets (for example, detecting and removing alcohol logos for countries where they’re restricted) improving compliance and user experience.

Implementing AI tagging with Wasabi

AI tagging has the power to turn unstructured data into a resource that’s accurate, secure, and easy to use, cutting out the manual effort that slows teams down. But putting these capabilities into practice at scale can be complex, costly, and out of reach for many organizations. That’s where Wasabi AiR comes in.

Wasabi AiR takes those benefits further, delivering intelligent media storage that automatically tags and organizes content upon discovery, ensuring it’s always secure and easy to retrieve.

Built for media, Wasabi AiR combines advanced AI tagging with Wasabi’s proven hot cloud storage and flat-rate pricing, making it possible to scale your content workflows with confidence and control. The result: faster access, stronger protection, and smarter use of data so teams spend less time searching and more time discovering.