Artificial intelligence (AI) has been used in a variety of sports-specific use cases for several years now. In venues, it’s been used to improve security and provide fans contactless checkout. For athletes, coaches, trainers, and referees, it’s been used to help improve performance, enhance scouting, prevent injury, and make officiating more objective. For fans, AI is personalizing their experience and creating a better product to watch. And for front-office operations, it’s providing deeper insights into fan behaviour, driving tickets sales through better marketing, and simplifying contracting.
Like data analytics and machine learning before it, Generative AI (GenAI) will likely quickly permeate many aspects of sports. Over the course of the next 12 to 18 months, we expect to see a groundswell of innovative applications involving content generation and management, live sports coverage, player evaluation, sports betting, fan engagement, and back-office operations. For fans, GenAI tools and applications can be used to create customized videos and highlights of their favorite teams and players, provide them with promotions based on their behaviours and interests, and power chatbots and digital avatars to help them engage with sports content in new ways.
Although there’s a lot of internal and market pressure to quickly adopt GenAI technologies, rushing the adoption journey may create issues. Teams, leagues, and organizations should look at ways to address both their shorter- and longer-term needs—not only across strategy and technology infrastructure, but also around risk management, governance, and talent.
Strategic questions to consider:
- With the technology and adoption moving so quickly, how can sports organizations stay on top of developments, rapidly experiment, and scale?
- How can teams, leagues, and organizations build upon their existing AI and data capabilities with GenAI? How will they need to improve their computing infrastructure and data platforms to effectively do so?
- How can organizations best leverage their proprietary data in combination with increasingly commoditized large language models to enhance and create new revenue streams?
- What risk mitigation strategies will organizations need to build to handle uncertainties and unanticipated consequences around GenAI—like trusting results, intellectual property issues, and multiple regulations?