In a fundamental change, one expensive act of content creation could become many inexpensive acts of distribution and monetizationThe Story Object Model (SOM) has emerged from the newsroom world, but it would be a mistake to think of it simply as a new newsroom-technology standard.
The more interesting question is whether the ideas behind SOM can help solve one of the biggest economic problems facing sports media: we are creating more content, for more platforms, in more formats, with more commercial opportunities, but we cannot afford to increase the cost and complexity of producing and managing that content. That makes SOM potentially much more important than just another piece of technical plumbing.
SOM attempts to describe a story as structured, machine-readable objects rather than an item locked inside an application. Video, audio, text, images, metadata, contributors, versions, and their relationships can be represented consistently. Its technical process has operational and economic significance.
Much of the media supply chain works because systems exchange just enough information. Newsrooms, production systems, asset-management platforms, graphics tools, editing applications, publishing systems, and distribution platforms are connected through APIs, integrations, and considerable custom engineering.
It works, but it is expensive. Sports exposes this clearly. Traditionally, sports television meant producing an event, creating a high-quality feed, and delivering it to relatively few broadcasters. That world remains hugely important today but is only part of sports production.
Rights owners increasingly operate direct-to-fan apps. Broadcasters distribute simultaneously across linear television, streaming, and social media. Clubs, leagues, and federations have become publishers. Audiences expect highlights within minutes, vertical video for phones, personalized clips, multiple languages, alternative commentary, and growing ranges of shoulder programming.
According to Caretta Research, the global sports-rights market was valued at just under $60 billion in 2025 and was forecast to exceed $70 billion by 2030. Beneath that growth, the traditional model is becoming harder: growth in established major rights is slowing; newer and smaller sports, digital platforms, and different distribution models account for more expansion.
Rights owners cannot assume that the next rights cycle will solve the economics through another large fee increase. They increasingly need revenue from direct-to-consumer services, sponsorship, advertising, commerce, and fan engagement.
Technology must support dramatically more output while costing less per piece of content. This is where SOM becomes interesting.
Caretta Research's media-technology data indicates that highlight-clips editing and reversioning software - consistently among the market's fastest-growing areas - grew at an average 23% annually between 2018 and 2025.
This growth reflects a fundamental change in sports production beyond editing software. The live match is just the starting point. Value comes from turning it into hundreds or thousands of content pieces, optimized for different audiences, platforms, sponsors, and territories.
Streaming adds another layer. Caretta Research's current data puts the global video-streaming market at roughly $274 billion in 2025. It is not replacing every other television format overnight but steadily increases the destinations, versions, and commercial models that premium content must support.
These connected trends describe an industry trying to industrialize digital video. Traditionally, workflow automation connects system A to system B, adds orchestration, and automates previously manual processes.
SOM potentially goes deeper. Content carrying a standardized description of itself, its contents, and its constituent objects' relationships lets applications operate with less prior knowledge of surrounding systems. That could substantially change automation economics.
Consider a football match. Instead of a finished program and files, imagine a persistent set of interconnected objects: teams, players, goals, interviews, graphics, camera feeds, commentary, statistics, sponsorship elements, rights restrictions, and editorial descriptions. Machine-readable relationships let downstream products become views of the same event.
A broadcaster might construct a traditional highlights program. A club might automatically produce player-specific clips. A social team could create vertical video. A streaming service might offer alternative commentary. A sponsor could receive approved branded highlights. An AI system could assemble personalized packages for individual fans.
These processes would not necessarily require each application to understand every other application's proprietary-data model. Here lies the efficiency opportunity.
For decades, media-technology interoperability has often meant connecting proprietary systems, creating substantial hidden costs: integration projects, middleware, professional services, API maintenance, and workflow testing.
Standardizing content descriptions rather than merely application interfaces could reduce some friction.
It could also improve AI. Generative and agentic AI are rapidly entering media workflows but perform best with context. Access to 200 video files differs from knowing that six clips contain the winning goal, three interviews feature the goal scorer, graphics contain match statistics, and assets are cleared for international social distribution.
Structured content models therefore offer a potentially important foundation for AI-driven media automation. However, caution is warranted.
Standards succeed by solving painful commercial problems, not through technical elegance. Many media standards have been enthusiastically discussed but pa
