Article overview:

• Video indexing transforms media archives from unsearchable storage into discoverable, monetizable assets by attaching rich metadata to every moment of content
• Controlled vocabularies and standards form the foundation of effective indexing, helping to ensure consistency and interoperability across systems and teams
• AI-powered indexing delivers speed and consistency, but human review remains essential for quality assurance and handling edge cases.
• Index at ingest to ensure content is searchable immediately when needed, not cataloged retroactively when demand arises

A sports broadcaster needs footage of a game-winning goal from 2019. A news producer searches for archival interview clips featuring a newly elected official. A documentary team hunts through decades of historical footage for a specific moment that will anchor their narrative. In each scenario, the difference between finding that content in seconds versus days—or never finding it at all—comes down to one critical capability: video indexing, or how the media asset has been tagged and filed in the archive.

The broadcast and media production industry is facing an unprecedented archiving challenge. Video content creation has exploded exponentially, with online videos now comprising more than 82 percent of all consumer internet traffic. Meanwhile, 93 percent of adults in the United States get at least some of their news online, with video content driving much of that consumption. Broadcasters, production houses and media organizations find themselves managing libraries containing millions of hours of footage—but much of it remains underutilized simply because users can’t find it, or don’t know it exists.

But by learning from the best practices for video indexing, broadcasters and producers can help users to harness that untapped power. In this article we’ll explore the world of video indexing: first we’ll look in detail at what it is and why it matters, then go into some proven best practices and the trends shaping its future so you can be ready to make the most of AI opportunities.

What is video indexing?

Video indexing is the systematic process of analyzing video content and assigning descriptive metadata to help make that content searchable, discoverable, and actionable. At its core, indexing transforms raw footage from an opaque, unknowable file into a rich, queryable asset where every moment, face, word, and object is optimized for location and retrieval.

As artificial intelligence and machine learning technologies have matured, the capabilities available to media organizations have likewise advanced dramatically. What once required armies of human catalogers working for months can often now be accomplished in just hours, with greater accuracy and consistency. Video indexing has emerged as an essential discipline that helps sprawling, chaotic media archives become monetizable assets. 

The components of video indexing

Metadata tagging forms the foundation of video indexing. Metadata are descriptive labels attached to media files; it’s information that does not appear to viewers but exists in the source code and database records. This data enables both internal teams and external partners to search through archives using key terms attached to these files.

Modern video indexing encompasses multiple layers of analysis, including:

• Speech-to-text transcription: Converting spoken dialogue into searchable text, synchronized to precise timecodes
• Visual recognition: Identifying objects, scenes, locations, logos, and on-screen text within video frames
• Facial recognition: Detecting and identifying individuals appearing in footage
• Audio analysis: Cataloging music, sound effects, ambient audio, and speaker identification
• Sentiment and tone analysis: Categorizing emotional content and thematic elements
• Technical metadata: Recording format specifications, resolution, frame rates, and quality indicators

Each piece of AI-generated metadata connects to specific timecodes, allowing users to navigate directly to relevant moments rather than scrubbing through entire files.

How video indexing works

Traditional video indexing relied entirely on human catalogers who would watch content and manually enter descriptions. This approach, while accurate when done well, proved impossibly slow for modern content volumes.

Contemporary indexing leverages artificial intelligence to automate the process. AI metadata tagging uses natural language processing, computer vision, and machine learning to generate consistent descriptors faster than any manual team. These systems analyze video content frame-by-frame, processing audio, video, and graphics in parallel to produce comprehensive metadata automatically.

The output then feeds into media asset management (MAM) systems where the indexed content becomes searchable. Users can query these systems using keywords, phrases, visual characteristics, or increasingly, natural language questions to surface precisely the footage they need.

Why is video indexing important to media and production?

For broadcast media and production organizations, effective video indexing helps to address fundamental operational challenges while working to unlock significant business value. Companies looking to reap the benefits of AI-driven video indexing could find themselves with more efficient operations, more streamlined rights management, and better distribution and monetization avenues. 

Operational efficiency

Production teams generally spend large amounts of time searching for content. And without proper indexing, your editors, researchers, and producers must manually review footage—a process that scales poorly as libraries grow. AI-powered indexing systems have demonstrated the ability to deliver an 80 percent productivity gain for video ingest teams, with a 40 percent time saving on content search and production.  

This efficiency gain can translate directly to faster production cycles. Sports teams can mark goals, fouls, and player statistics as events happen, then push highlight packages to digital channels by half-time. News operations can surface relevant archival footage within minutes of a breaking story.

Content monetization

Every media library contains potential hidden value—footage that could be licensed, repurposed, or syndicated if only it could be found. Video indexing can help turn archives from cost centers into revenue-generating assets. When content is properly tagged and searchable, licensing teams can quickly identify and package footage for external buyers. Marketing departments can repurpose evergreen content without commissioning expensive reshoots.

Compliance and rights management

Broadcast operations must track complex rights information: who owns what footage, where it can be shown, and for how long. Video indexing platforms can integrate with rights management systems to help ensure compliance, flagging content that approaches license expiration or cannot be used in certain territories. This capability has become increasingly critical as distribution channels multiply and regulatory requirements intensify.

Preserving institutional knowledge

Media organizations possess irreplaceable historical archives. Proper indexing helps to ensure this content remains accessible as staff move on and institutional memory fades. A well-indexed archive can preserve not just the content itself, but also the context and relationships that give it meaning.

Multi-platform distribution

Modern audiences consume content across linear broadcast, streaming platforms, social media, and mobile applications. Each channel may require different edits, formats, or compilations. Indexed content can be rapidly assembled into platform-specific packages, driving the multi-platform strategies that contemporary distribution demands.

What is the current state of the video indexing market?

The video indexing market, typically measured as part of the broader media asset management sector, is entering 2026 during a period of substantial growth driven by content proliferation and advancing AI capabilities.

Video indexing market size and growth

The global media asset management market is projected to grow from $7.53 billion in 2025 to $16.18 billion by 2034, exhibiting a compound annual growth rate (CAGR) of 8.87 percent during the forecast period according to Market Research Future. Other analysts project even more aggressive growth, with Technavio forecasting the MAM solutions market to grow by $2.16 billion at a CAGR of 16.2 percent between 2024 and 2029.

Within this market, archive and storage management functions—which include indexing capabilities—represent the largest segment, holding an estimated 24.8% market share in 2025, says Coherent Market Insights.

Key market drivers in video indexing

Several factors are propelling this market expansion, including:

• Content volume growth: Every media library grows by the hour. Broadcasters produce more content than ever while simultaneously digitizing historical analog archives. This dual pressure creates urgent demand for automated indexing solutions.
• Cloud migration: The industry continues its transition to cloud-based workflows and hybrid models that balance cloud flexibility with on-premises security considerations. Cloud infrastructure enables scalable indexing that can ramp up during peak events and scale down during quieter periods.
• AI maturation: Artificial intelligence has moved from experimental technology to production-ready capability. AI engines now process audio, video, and graphics in parallel, simultaneously, with greater accuracy, helping to speed up archiving processes.
• Remote production demands: Distributed production teams require centralized, searchable asset repositories. The shift toward remote and hybrid work models has accelerated investment in systems that empower seamless collaboration across locations.

How are broadcasters adopting AI-driven video indexing systems?

Large broadcasters have largely adopted sophisticated MAM systems with integrated indexing capabilities. The challenge now shifts toward optimization—improving accuracy, expanding the types of metadata captured, and better integrating indexing outputs into production workflows.

Small and medium-sized content producers continue to seek solutions that balance capability with cost. 

Best practices in video indexing for broadcast media

Implementing effective video indexing requires attention to strategy, standards, and workflow integration. Learn from these best practices when implementing your own AI-driven video indexing program.

Establish controlled vocabularies and taxonomies

Consistent terminology forms the foundation of effective indexing. Controlled vocabularies limit the terms that can be applied to content, reducing spelling errors, synonym drift, and inconsistent categorization. A search for "soccer" should return the same footage that a producer labeled "football" in another region.

Example: A news organization might establish controlled vocabularies for politicians (linking variants of names to canonical identifiers), locations (connecting colloquial names to official designations), and topics (ensuring "healthcare" and "health care" resolve to the same category).

Implementation approach: 

• Develop hierarchical taxonomies that reflect your organization's content domains 
• Map synonyms to preferred terms
• Ensure the vocabulary accommodates regional variations while maintaining searchability

Implement standardized metadata schemas

Adopt industry-standard metadata frameworks rather than creating proprietary schemas. The IPTC Video Metadata Hub provides a comprehensive recommended standard, including terms for rights usage, language, and content created by generative AI models.

Example: The IPTC standard's inclusion of generative AI tracking reflects how schemas must evolve with technology. Organizations adopting this standard can help to future-proof metadata structures for any new emerging content types.

Implementation approach: 

• Map your existing metadata fields to established standards
• Where proprietary fields are necessary, document them clearly and consider how they might eventually align with evolving industry schemas

Integrate AI with human oversight

AI-powered indexing delivers speed and consistency, but human review remains essential for quality assurance and handling edge cases. The most effective implementations combine automated processing with targeted human verification in a process known as Human in the Loop, or HITL. 

Example: Sports broadcasters use AI to automatically tag game action, player appearances, and statistics. Human reviewers verify critical moments—ensuring the game-winning play is accurately tagged—while accepting AI determinations for routine footage without individual review.

Implementation approach: 

• Deploy AI for initial metadata generation
• Establish workflows where humans review AI-generated tags, correct errors, and train custom models for domain-specific recognition
• Focus human effort on high-value content and categories where AI accuracy remains developing

Index at ingest

The most valuable metadata is captured when content enters the system. Waiting until content is needed creates backlogs, and can risk leaving valuable footage undiscovered.

Example: A broadcast facility processes all incoming feeds through automated indexing before files reach storage. By the time an editor needs content from a morning shoot, it has already been transcribed, face-tagged, and categorized—ready for immediate search.

Implementation approach: 

• Integrate indexing into ingest workflows so content is processed automatically upon arrival 
• For live content, consider edge tagging at cameras and encoders to reduce latency and enable near-real-time searchability

Capture multiple metadata layers

Different users search for content in different ways. Comprehensive indexing captures multiple layers of metadata to support varied discovery patterns.

Example: A researcher looking for footage of a specific executive discussing a product launch might search by face, by spoken words, by topic, or by the product shown on screen. Multi-layer indexing ensures discovery regardless of approach.

Implementation approach: 

• Index content across speech (transcription), visual (objects, scenes, faces), audio (music, effects), and contextual (topics, sentiment, rights) dimensions 
• Enable search across all layers simultaneously

Maintain rights and compliance metadata

Technical content description means little if usage rights are unclear. Integrate rights information into the indexing schema so compliance data travels with content.

Example: When a news operation queries for footage of a musical performance, the system returns only clips where music rights have been cleared for news use—preventing inadvertent rights violations that could result in legal liability.

Implementation approach: 

• Capture license terms, usage restrictions, territorial limitations, and expiration dates as searchable metadata
• Configure systems to flag or filter content based on intended use

Enable continuous learning and refinement

Indexing accuracy improves when systems learn from corrections and new examples. Establish feedback mechanisms that capture human improvements and use them to enhance automated processing.

Example: A regional broadcaster trains its indexing system to recognize local landmarks, politicians, and regular program contributors. Over time, the system achieves accuracy on regional content that generic models cannot match.

Implementation approach: 

• Track metadata corrections and additions
• Feed approved changes back into AI training pipelines
• Develop custom models for organization-specific recognition needs—proprietary logos, on-air talent, recurring locations

Design for interoperability

Media workflows involve multiple systems—MAM, editing platforms, automation systems, distribution networks. Indexed metadata must flow across these systems without loss or corruption.

Example: When an editor pulls a clip from MAM into their editing software, key metadata travels with it. When the finished piece returns to MAM, any new metadata added during editing integrates with the original index record.

Implementation approach: 

• Use standard interchange formats
• Verify metadata preservation through workflow testing
• Establish clear ownership of metadata fields across systems

Future trends in video indexing

Video indexing continues to evolve rapidly, shaped by advances in artificial intelligence and changing industry requirements. What’s next for AI-driven media asset management? Keep an eye out for developments like these. 

Multimodal AI integration

The next generation of indexing leverages multimodal AI models that process text, images, audio, and video within unified architectures. The multimodal AI market reached $2.51 billion in 2025 and is predicted to grow to more than $42 billion by 2034, reflecting increasing substantial investment in these technologies.

Rather than running separate engines for transcription, visual recognition, and audio analysis, multimodal systems understand content holistically. This enables recognition of relationships that single-mode analysis misses—models that can understand not just what is shown and said, but how elements interact to create meaning.

Natural language discovery

Traditional search requires users to construct queries using the system's vocabulary and logic. Emerging interfaces allow natural language interaction—asking questions in the same way you would ask a colleague.

Users might query: "Find clips where the CEO discusses the merger while standing in the factory" rather than constructing complex Boolean searches across multiple metadata fields. AI-powered discovery agents can interpret intent and return relevant results without requiring precise technical query construction.

Agentic AI assistants

Beyond search, we’re seeing AI agents emerge that can conduct research across video archives, synthesizing findings and presenting conclusions. These systems function as research assistants, capable of remembering every video in an archive and proactively surfacing relevant content based on project context.

This shift moves video indexing from passive infrastructure to active collaboration—systems that participate in creative processes rather than merely responding to queries.

Real-time indexing at scale

Edge computing and optimized AI models enable indexing of live content with minimal latency. Sports broadcasters can already push highlights to digital platforms during events. This capability looks set to expand to news operations seeking to surface relevant archival content while stories unfold.

Generative AI integration

As generative AI creates more media content, indexing systems must track synthetic content appropriately. The IPTC Video Metadata Hub's inclusion of generative AI tracking reflects this emerging requirement. Organizations will need to identify AI-generated content for compliance, creative, and editorial purposes, so it’s important to get your tagging processes clear and embedded ASAP.

Semantic understanding

Future indexing will move beyond literal content description toward semantic understanding as we look to grasp those themes, narratives, and emotional arcs that give content meaning. This capability can support more sophisticated content recommendations and enable creative discovery that current keyword-based systems cannot achieve.

Key takeaways: Video indexing is transforming media archives for the better

• Video indexing transforms media archives from unsearchable storage into discoverable, monetizable assets by attaching rich metadata to every moment of content.
• AI has revolutionized indexing efficiency, delivering up to 50% improved accuracy, 10x faster logging, and 5x quicker discovery compared to manual methods.
• The market is growing substantially, with the media asset management sector projected to exceed $16 billion by 2034 at a CAGR of nearly 9%.
• Controlled vocabularies and standards form the foundation of effective indexing, helping to ensure consistency and interoperability across systems and teams.
• Index at ingest to ensure content is searchable immediately when needed, not cataloged retroactively when demand arises.
• Multi-layer metadata capture enables diverse discovery patterns, supporting users who search by speech, visual elements, audio characteristics, or contextual attributes.
• Human oversight remains essential even as AI handles primary processing—combine automation speed with human judgment for quality assurance.

Ready to see if Moments Lab’s AI-powered video discovery platform is the right fit for you? Contact us for a demo.

Frequently asked questions about video indexing

What is the difference between video indexing and video tagging?

Video tagging typically refers to applying quick labels to assets, often in an ad-hoc or inconsistent manner. Video indexing is a more comprehensive, structured approach that follows formal schemas with defined fields, controlled vocabularies, and precise timecodes. Indexing delivers precision at scale that informal tagging cannot match, empowering more reliable search across large archives and integration with production workflows.

How long does it take to index video content?

With modern AI-powered systems, video content can be processed at many times real-time speed—a one-hour video might be fully indexed in minutes. The exact speed depends on the depth of analysis required, the processing infrastructure available, and whether content is processed in batches or streamed. Real-time indexing of live content is increasingly common for time-sensitive applications like news and sports.

What metadata should be captured during video indexing?

Comprehensive indexing captures multiple metadata layers: speech transcription with timecodes, visual elements (faces, objects, scenes, on-screen text), audio characteristics (music, effects, speakers), technical specifications (format, resolution, quality), and contextual information (topics, rights, sentiment). The specific metadata priorities depend on how content will be discovered and used.

How does video indexing integrate with existing media asset management systems?

Video indexing typically functions as either an integrated capability within MAM platforms or as a connected service that enriches MAM systems with metadata. Modern implementations use standard APIs and metadata formats to ensure indexed data flows seamlessly into storage, search, and distribution systems. The key is ensuring metadata generated during indexing remains accessible throughout the content lifecycle.

What is the return on investment for video indexing implementation?

ROI varies by organization but typically manifests in reduced search time for production staff, increased content licensing revenue from newly discoverable archive material, lower compliance costs through automated rights tracking, and faster time-to-air for time-sensitive content. Organizations report up to a 50 percent increase int he marketability of media assets. This can equate to a potential revenue of $1 million per 10,000 hours of archive with the Moments Lab Media Marketplace.