Innovation in artificial intelligence (AI) technology, especially generative AI, is moving fast. The sports media industry is feeling the pressure to adapt and is increasingly adopting AI to remain competitive. Indeed, 90% of sports executives believe that AI will have a medium or high impact on the sports media industry by 2030. AI is expected to strongly impact sports by driving operational efficiencies and unlocking new revenue opportunities including data licensing, IP licensing and management, new sponsor categories, and new AI-based products.
Taking advantage of AI and other tech innovations requires a holistic view and an agile approach to identifying and prioritizing new impactful use cases.
While major organizations like the International Olympic Committee (IOC) have established a public AI governance and oversight agenda, other sports properties have experimented with AI with some success.
For example, the English Premier League partnered with sports AI company Stats Perform as part of a long-term data licensing agreement. This licensing deal covers rights to player data for betting, and AI-powered insights. By licensing data, the Premier League has effectively created new revenue streams and set the stage for future analytics partnerships.
Likewise, the National Basketball Association (NBA) leaned on AI technology from WSC sports to automatically generate personalized highlight packages. Using AI for personalized automated content generation has boosted fan engagement and was crucial in generating over 1 billion views for 2023 season on the recently relaunched NBA App. To win in this highly competitive environment, executives need to identify and address revenue and optimization opportunities created by AI by cultivating the right environment (including data infrastructure, technology architecture, and more) and continuously developing new capabilities. It will also require new partnerships with new types of partners to stay on top of the latest trends and remain innovative.
Unlocking meaningful opportunities with AI starts with a structured methodology. This should include the following steps:
- Evaluate use cases: Long-list all potential use cases, including impacts on revenue and costs as well as risks and any mitigators
- Prioritize use cases: Consider cost and revenue opportunities against the time and resources needed for implementation
- Design an AI roadmap: To make implementation easier, establish ground rules about technology (e.g., closed versus open-source model, vendor selection), people (e.g., nominating a cross-functional team of AI champions, investing in upskilling in tech and non-tech functions), and processes
