Key Takeaways:
–AI is not theoretical: rights holders are already using it for various content, digital, production, and other tasks that enable scale, personalization, and new revenue streams
–Start small, scale smart: The most immediate ROI comes from automating repetitive tasks like team waste time on (or usually elect to intentionally neglect)
–Governance matters: long-term success comes from solutions that deliver measurable impact and team-wide (sometimes cross-departmental) adoption
Many sports fans already know that AI empowers the world’s top teams, leagues, and broadcasters in many ways – chief among them is the ability to deliver content tailored to their interests. VP of Digital and Social Content for the NBA, Bob Carney, recently said in an interview that only 10 years ago they had a one-size-fits-all approach to content. But, over time, he and his team realized that with AI, they could transform their whole digital strategy.
The NBA has already deployed AI in multiple impactful ways. For example, they leverage it to turn basic highlight packages and game recaps into content personalized by fan preferences, then to localize it in multiple languages. And these are just two examples of how the new technology is influencing the way rights holders produce content and how fans consume it.
The (Non-Linear) Reality of AI Adoption
While the NBA was an early adopter of AI, it’s understandable that not all rights holders have the resources enjoyed by one of the world’s most prolific sports leagues. Despite widespread interest, many organizations struggle with effectively implementing AI. According to Deloitte, 75% of surveyed organizations have increased their investments in data lifecycle management due to generative AI, yet many are still navigating the complexities of integration.
Gartner's 2025 Hype Cycle for Artificial Intelligence indicates that generative AI is entering an age of “disillusionment," suggesting that initial excitement is giving way to a more measured understanding of its capabilities and limitations.
Is AI Overhyped and Underused?
It’s no wonder, then, that a recurring theme we come across when meeting with industry professionals revolves around the different levels of skepticism people feel towards AI as a buzzword. After all, AI is often discussed in abstract terms.
At a recent industry event for sports content professionals, one common theme shared among many organizations was that many feel people talk about AI without truly understanding how to apply it.
People see it as an easy way to create content, but they don’t fully comprehend its full potential.”
This misunderstanding often stems from limited exposure to real-world workflows rather than a lack of technical knowledge. While fans may be familiar with AI’s role in athlete diagnostics, refereeing, or even coaching in sports from basketball to gymnastics, they’re less aware of how it powers the content they watch every day, especially compared to how prevalent it is nowadays.
Four Workflows AI Already Solves
-Auto-Clipping Live Games: WSC Sports is a leading sports content AI solution that identifies, tags, and clips key moments and highlights during live games in more than 40 different sports, eliminating the need for manual editing and significantly reducing turnaround time
-Multi-Language Voiceovers: Also offered by WSC Sports, AI-generated voiceovers enable the creation of localized content without replicating the production process. The NBA has used this technology to better-serve their global audience; 75% of which is located outside the U.S – seeing fantastic spikes in engagement trends across the board
-Player-Specific Reels on Demand: Once content is created, AI-driven platforms can analyze user behavior, including watching preferences to deliver content that fans are most likely to enjoy and continue watching
-Internal Content Management: Beyond serving their fans, saving, organizing, and managing tens to hundreds of thousands of videos (sometimes more) has been a historically challenging task. (Think warehouses filled with reels of raw tapes.) AI is now able to help organizations retroactively “file” their digital content efficiently while also making it more accessible to business stakeholders like third-party partners, teams in a league, or the players themselves.
These are just a few real-world, everyday examples of how AI is already changing the game for many sports organizations on a daily basis. And we almost didn’t touch on gen-AI and other more advanced solutions, that are already depending on relationships between rights-holders and fans across the globe.
Quick-Start Checklist: Your Next 90 Days
To begin using AI to create and distribute sports content, teams can focus on the following steps:
-Identifying 1–2 repetitive, time-consuming tasks
-Define success metrics (i.e., reduction in turnaround time, cut data time-to-value)
-Research and demo several solutions
-Run a pilot that includes editorial and product teams
-Document outcomes, including areas where human input remains necessary
-Present results internally to support future planning and resource allocation
