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Generative AI: What Sports Teams and Leagues Should Know

matt perl
·4 min read

Today’s guest columnist is Matt Perl, senior director of marketing at GameOn Technology.

With 100 million users and climbing, ChatGPT’s hyperbolic growth has already cemented OpenAI (the company that created ChatGPT) as one of the most disruptive technological forces in recent memory. ChatGPT’s ascent has also driven a massive increase in conversations around the power, scope, use and impact of artificial intelligence.

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At its core, ChatGPT provides highly nuanced answers to complex human questions in chat form as part of a generated conversation. DALL-E (another OpenAI project in the headlines) similarly generates content—but its application comes in generated images based on simple text descriptions.

These two applications of “generative AI” have quickly become part of a larger conversation about the limits of this technology, but the algorithms that drive the experiences have been powering millions of consumer interactions for years. Within the massive sports industry, fan engagement, analytics and even betting have seen the power (and the challenges) of generative AI in driving powerful results.

Teams, leagues and venues across professional sports already rely on intelligent chat to drive fan experiences. Across the sports landscape, organizations are driving “sticky” and powerful conversations with their fanbases that unlock new business opportunities. With all of those interactions comes the opportunity to harness generative AI as an additive tool in producing even more authentic intelligence for fans.

While the excitement, attention and even investment capital inquiries around generative AI are spiking, it’s imperative to use this time to also consider some of the challenges and perils of the space.

Every transaction, interaction and click from a fan helps build a profile of that buyer for an individual sports franchise. In aggregate, those data points contribute to understanding the fanbase and the team’s various buyer personas. This data, when applied to digital interactions, helps drive a highly personalized journey and arguably a stronger experience for consumers by using algorithms, machine learning and human intelligence. When combined, these factors power a customized experience for the fan that can do everything from recognizing purchase intent to understanding which player’s jersey is most likely to drive a sale. Teams with these solutions can identify patterns in fan behavior and address those opportunities without the need for human intervention.

Beyond individual fan engagement, new experiences can be driven by generative AI to support fan interest. One area where we see some of the highest engagement from fans is in video highlight packages from our NBA partners. In many cases, these highlight packages are generated by humans who meticulously comb through videos to find the specific clips they want. It’s hours of work for what could ultimately be a 30-second snippet. With generative AI, this could be as simple as typing “show me every 3-point shot Steph Curry made last season” into a tool that immediately generates a video compilation of those 285 jumpers.

It’s more than conceivable we will see generative AI play a role in automating key functions in all areas of sports, especially fan-focused experiences. Additionally, the excitement around this space is causing brands to come together with chat providers to rapidly accelerate partnership conversations.

While sports teams have seen the power of authentic intelligence in driving meaningful fan engagement and revenue opportunities, the ability for generative AI to drive sustainable, safe conversations is still evolving. In just the last few weeks, high-profile headlines have highlighted generative AI tools responding to user questions with hostility, misinformation and other concerning behavior. The risks and concerns around the technology led Microsoft president Brad Smith to suggest the potential for AI technology to be regulated by the federal government.

The concept of sports franchises (and other brands) harnessing the power of generative AI in so-called “walled gardens” is something that should be considered. Without guardrails or protections, the potential damage to these carefully managed franchise brands could be severe. This could include financially impactful inaccuracies, such as identifying “the best place to watch a Cubs game” as outside Wrigley Field from a nearby rooftop bar.

To maximize the generative AI opportunity right now, teams and organizations should rapidly define the objectives of using these tools in fan experiences. In doing so, the potential risks will help the team identify the use cases and outcomes they want to achieve, and those they want to avoid. Additionally, franchises should stress test their data sources to identify some of the outcomes generative AI could provide. This should include past interactions with fans, social media activity and customer feedback. Additionally, this plan should include the technology infrastructure required, the resources needed, and the timeline for implementation—all of which is critical to ensuring that the data is accurate, reliable and relevant.

If used effectively, generative AI can fundamentally change how sports teams engage with fans. By harnessing the power of the tool, teams can continue to find innovative ways to bring fans closer to the franchises they love. Wise use of that power requires a plan, patience and an understanding of the technology.

Perl is a senior director of marketing at GameOn Technology. He previously spent four years as director of customer acquisition & broadcasting at the Oakland A’s, four years at ESPN, one year as the SVP of marketing at Orange County Soccer Club (USL), four years as a producer for the Washington Nationals and one year as manager of demand generation at NFL On Location.

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