By Scott Alexander, General Manager, ViewNexa by Bitcentral
The rapid growth of streaming platforms has transformed the media landscape, creating unprecedented opportunities for broadcasters, content owners, and digital-first providers to directly reach audiences at home, on the go and anywhere in between. But this expansion of touch points has also introduced a persistent challenge of connecting viewers to the content they want quickly, seamlessly, and at scale.
According to  Talker Research, the average U.S. viewer now spends about 110 hours per year scrolling through streaming platforms. In businesses driven by subscriptions or ad revenue, every minute spent searching rather than watching represents lost engagement, weakened retention, and reduced monetization potential.
At the heart of the issue is discoverability. With the media landscape increasingly fragmented by platform silos, legacy content libraries, and outdated search tools, discovery has become a business-critical function. Artificial intelligence (AI) is now bridging this gap by transforming the discovery bottleneck into a strategic advantage.
The discovery dilemma
Traditional discovery systems, built on rule-based algorithms and basic content metadata, were designed for a different era. They emerged at a time when streaming platforms offered curated catalogs and relatively limited inventory, often supported by manual tagging and broad categorization.
But today’s streaming environment is more complex. Libraries now span decades of content across genres, languages, formats, and regions. Many platforms ingest live content daily, adding new material that must be indexed, categorized, and made discoverable in real time. The volume and velocity of content growth have exposed the limits of legacy discovery tools.
Many of these systems also operate without sufficient metadata, the critical framework that makes content searchable. Without rich, consistent tagging, valuable content remains buried, limiting its commercial potential. This is especially problematic for long-tail assets, which may otherwise drive niche engagement or syndication value when surfaced effectively.
AI and the rise of intelligent metadata
AI transforms content discovery by generating deep, dynamic metadata at scale. Through technologies like machine learning, computer vision, and natural language processing, AI analyzes video content frame by frame, detecting elements such as faces, logos, settings, emotional tone, and spoken keywords.
This automation dramatically reduces the need for manual tagging, often cutting labor hours by more than two-thirds. In fast-paced environments such as live news or sports, the ability to generate structured metadata in real time enables faster turnaround for publishing clips, populating OTT feeds, and powering advanced search capabilities.
Beyond speed, AI-generated metadata also introduces granularity. Rather than relying on generalized descriptors, platforms can build highly searchable archives that respond to specific editorial, commercial, or audience signals. This opens up new discovery pathways for content that might otherwise be missed, including archived interviews repurposed for current events, scene-level segments, or topical highlights.
For content owners, AI-powered metadata becomes a commercial asset. It improves searchability, supports platform-wide navigation, and unlocks revenue opportunities through syndication, FAST programming, or catalog licensing.
Scaling editorial strategy
While AI excels at volume and precision, it can’t replace human judgment. Editorial teams remain essential in crafting discovery strategies that align with brand identity, content priorities, and business goals. In other words, AI supports but does not replace these efforts.
By handling repetitive or technical tasks such as metadata tagging, asset segmentation, or keyword extraction, AI empowers editorial teams to focus on creative and strategic work. These include curating playlists, programming digital channels, or shaping cross-platform content journeys.
The most effective discovery strategies blend AI efficiency with human insight. Machine outputs provide a foundation, but editorial oversight ensures that surfaced content matches the tone, intent, and expectations of the target audience.
Optimizing discovery for live content
Discovery goes beyond libraries and archives, reaching into live content, where speed and timing are critical. In sports, breaking news, or live entertainment, the ability to extract and surface key moments as they happen has become an essential part of any discovery strategy.
AI enables real-time clip creation by identifying highlights based on visual, audio, and contextual cues. These can include goals, sound bites, reactions, or notable events that are relevant to viewers. Once detected, these moments can be automatically segmented and prepared for discovery across digital channels.
This rapid workflow ensures that live content is not trapped in long-form streams, but made immediately available for discovery through search, recommendation tiles, social sharing, or curated playlists.
By enabling real-time discoverability, AI improves viewer engagement and extends the monetization window for live programming. It turns fleeting moments into searchable assets.
A connected ecosystem
A critical frontier in AI-driven discovery is interoperability. Discovery systems today are often fragmented across platforms, apps, and devices. Metadata, tagging protocols, and behavioral signals are frequently trapped in silos, unable to inform discovery outside their immediate environment.
This lack of connectivity creates inefficiencies. Content tagged in one CMS may need to be reprocessed for another platform. Usage signals on mobile apps may not enhance discovery on smart TVs. As a result, viewers encounter inconsistent discovery experiences across platforms, and content owners miss opportunities for deeper engagement.
Solving this problem will require greater industry collaboration. Broadcasters, streaming platforms, technology vendors, and device manufacturers need to align on open metadata standards, tagging schemas, and interoperable APIs. A more connected ecosystem will allow AI systems to deliver smarter, more consistent discovery regardless of where or how viewers access content.
When metadata and insights flow freely across systems, discovery becomes more accurate, less repetitive, and more aligned with both viewer preferences and content strategy.
The road ahead
As streaming audiences grow more sophisticated and content libraries more complex, discovery will remain one of the defining challenges in the media landscape.
AI is no longer a theoretical experiment in this space. It is the operational foundation for modern discovery. From metadata automation and live content indexing to intelligent archiving and cross-platform discoverability, AI helps media companies bridge the growing gap between content abundance and content accessibility.
The winning platforms will be those that use AI as a fully integrated part of their editorial and distribution workflows. AI enables them to surface the right content, reduce friction in navigation, and maximize the value of every asset in their library.
Ultimately, content discovery is not just about helping audiences find something to watch. It is about enabling the business models that depend on viewer engagement, retention, and satisfaction.
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