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 the fan experience and creating a better product to watch. And for front-office operations, artificial intelligence is providing deeper insights into fan behavior, driving tickets sales through better marketing, and simplifying contracting.
Like data analytics and machine learning before it, generative AI will likely quickly permeate many aspects of sports. Over the course of the next 12–18 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, generative AI 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 behaviors and interests, and power chatbots and digital avatars to help them engage with game content in new ways.
Although there’s a lot of internal and market pressure to quickly adopt generative AI tools and applications, 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:
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