Generative AI has the potential to be a revolutionary technology in many sectors. For instance, the pharmaceutical industry can use generative AI to reduce the time and cost it takes to get a drug to market, especially in areas of unmet need like rare diseases. However, similarly disruptive use cases do not exist in the sports industry. While there have been attempts at using generative AI to innovate in the sports sector, none have yielded groundbreaking results.
The sports industry’s impact on the global AI market is minimal
The sports industry will not play an important role in the global AI market, which GlobalData forecasts will be worth $909bn by 2030. Between June 2020 and June 2023, just 363 patents related to AI were granted in the sports industry globally. This can be compared to the automotive sector, which saw 4,155 patents granted in the same period.
The sports industry has traditionally been slow to innovate compared to other sectors. Technological innovation in sports has frequently drawn indignation from fans, players, and clubs alike. For example, the Premier League’s use of the video assistant referee (VAR) has been controversial, even though this equipment does not contain particularly exceptional or innovative technology and could have been adopted years earlier.
Ultimately, the global AI market will be propelled by industries where there is the potential for significant impact such as the technology and pharmaceutical sectors.
There are still uses for generative AI in the sports industry
Generative AI refers to a category of artificial intelligence techniques and algorithms that are designed to generate new data or content that is like what it has been trained on. Of the five advanced AI technologies receiving the most attention today, generative AI—or “creation”—is the fastest-growing. Despite the minimal adoption of generative AI, there are potential use cases in sports for the technology, which would help simplify business processes and improve the fan experience. For example, this could be in sports betting, where generative AI could be used to create personalised models that generate odds for specific scenarios. Or the technology could be used to create personalised video content based on viewing and social media engagement habits, meaning that sponsors can benefit from a customised activation strategy.
The primary advantage that generative AI offers the sports sector is personalisation. Training plans, nutrition, media content, and betting odds could all be personalised and automated using generative AI, reducing the need for human staff, and helping those in the industry to cut costs. The sports industry will need to see a tried and tested use case in a different industry before there is widescale adoption. Sports companies have attempted to use generative AI with limited success. However, there is still great potential for the technology in the industry, especially around personalisation and the generation of media content. However, this potential has not yet been fulfilled.
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