Artificial intelligence in sports: PwC

For years, sports teams have been using machine learning (ML) to gain an edge by analyzing player statistics and honing pricing, while leagues have been using ML to improve scheduling and other operations processes. Despite these advances, however, sports organizations are often late adopters of emerging technologies overall. As generative AI (GenAI) starts to fundamentally change how business gets done, stakeholders in the sports industry — including teams, leagues, content owners and advertisers — should seize this moment to evaluate how GenAI can help improve the fan experience significantly and help boost revenue.

GenAI is highly customizable and can be fine-tuned to recognize new patterns. This can unlock opportunities for teams to create personalized offerings based on fans’ individual preferences and deliver them at scale. Your organization could offer more tailored experiences that deepen fan loyalty and a feeling of connection to a franchise.

What could this look like in practice? It’s all about cultivating the ultimate live experience — whether it’s in the stadium or at home watching a game. And it needs to start with getting serious about digitizing fan data.

Modernize your fan data collection

Collecting data about fan preferences isn’t new. Most organizations already obtain key information about their customers to better under their needs and meet them where they are. Using that valuable data stategically can help you understand fans and craft engagement strategies that build loyalty and better experiences.

Game watching experiences are being reinvented through second screens, on-demand statistics, immersive streaming and seamless purchasing options. Now, imagine if you could supercharge an organization’s ability to extract actionable insights from all your unstructured fan data across multiple systems and points of fan engagement. One GenAI model can conduct deep retrieval across your sports business, so you could see incredible ROI — quickly.

Deploying an AI model isn’t as challenging as it may sound, especially when fan data is digitized. Moving customer data to cloud enables GenAI to access that data and makes compliance, privacy and governance easier. Businesses can license a private version of one of the publicly available models cloud services providers offer and then customize it to meet their needs. With data in the cloud and a GenAI model at the ready, sports organizations can be set up to process and deliver insights that can power hyper-personalization for fans — at scale.

Elevating the fan experience with GenAI-driven experiences

In our 2024 AI Business Predictions, we unpack the transformative potential of GenAI to help shape how business gets done. Cloud-based enterprise applications already incorporate GenAI capabilities, but this is just the beginning. Previously complex tasks such as building new processes, creating innovative products and services, and enhancing customer engagement can now be accomplished through “no code” activities facilitated by GenAI. This power extends beyond traditional businesses and has the potential to revolutionize the sports industry, creating exciting new opportunities for revenue generation and immersive fan experiences.

Let’s take a look at the possibilities.

Imagine Stephen, a loyal fan of his hometown MLB team. Each season, he attends games at the ballpark, whether it’s with his family, business associates or close friends. In the past, Stephen faced a frustrating choice: Spend time selecting games and seats with a team ticketing agent or settle for a rigid package with predetermined games and seats. But thanks to GenAI, new experiences and opportunities emerge for Stephen and for his local team.

Past preference data combined with predictive analytics can help generate customized recommendations for Stephen from the point of ticket purchasing to halftime refreshment selections, whether he’s at home or at the stadium.

Here are some GenAI-driven fan experiences we expect to see in the near future.

Personalized commentary generated by analyzing an individual fan’s viewing history and team/player preferences could enable GenAI to deliver customized content like game highlights. And with some inputs from Stephen, perhaps he’ll be able to ask for real-time commentary delivered in the style of your favorite comedian or athlete.
Customized, relevant advertisements and offerings that reach spectators on a personal level — and cut down on game time interruptions. This could be as specific as a notification that pops up while Stephen’s watching the game at home to remind him that this time last week he ordered pizza, and that if he puts in an order now he could have it delivered by halftime. Cross-platform engagement will be more easily enabled. A car ad during a break could be coupled with a personalized reminder that Stephen’s car lease expires soon, and an AI assistant could ask him if he’d like to bookmark his dealership’s page and be reminded to look at his options later.
Customized highlights and game summaries based on favorite teams, watching habits and social media activity.
High-quality translations of player interviews to grow the sport's and organization's fan base worldwide. This could even include their take on game strategy to deepen understanding and help introduce new fans from other cultures to rules and plays.
Engaging branded moments that connect with audiences. For example, replacing the in-stadium ads visible in the background during historic big plays to reflect current sponsors during a high-stakes game moment.

The capabilities to bring these experiences to life are already here, or will be soon, and the possibilities are exciting. This unprecedented level of personalization can provide every fan with their own set of options for having fun, while also providing your organization with revenue boosting opportunities.

Getting started to change the game

The opportunity to build AI-driven sports experiences is very real. And because this is a domain that sees radical improvement and scale with familiarity, teams should start experimenting now to streamline operations and improve the fan experience. To effectively use AI, they should develop a holistic strategy involving the organization’s technology stack, internal skill sets and roadmap. They should utilize the trove of historical data at their disposal to augment their models and expand impact.

Building an internal team is key. It should be composed of both technology specialists and your top leaders who know the business, its differentiators and can pinpoint the kinds of data needed to customize GenAI models. Consider a methodology that analyzes the value of existing customer engagement processes, their scalability, how much time is spent on them and the kinds of fan data that can be leveraged to build new fan experiences.

As more enterprise applications have GenAI capabilities, it will become easier to improve and scale your fan enagagement efforts with an omnichannel approach. Take CRM as one example. AI-powered CRM more easily conducts deep retrieval from past communication histories, purchase data and other intelligence, and delivers insights into how the organization can fine tune existing fan engagement and improve in the future. This may require AI solutions as an overlay in the CRM, depending on the type of data that’s in and out of the CRM instance, and the overall data and AI ecosystem. AI can also help scale competitive landscape analysis. Data-driven insights about competing teams can indicate which sports culture and fan bases have the potential to get the most bang for the buck — and where synergies might indicate joint marketing initiatives that could increase savings and reach.

Playing by the rules with responsible AI

Yes, embrace the use of AI to improve fan experiences, but implement governance around your AI use to help make responsible and ethical practices as well. Keeping responsible AI guidelines in mind can help you establish a solid foundation for using AI. And to capitalize on the opportunities GenAI presents, sports organizations should prioritize a responsible approach to AI and incorporate trust by design.

Be good stewards of your data. Assess its value to your organization while also practing sound data governance.
Leverage your existing governance, cybersecurity, privacy and compliance programs as you establish responsible AI in your organization.
Adopti an enterprise-wide approach, with every senior leader understanding their role and responsibilities when it comes to fostering trust in AI systems.

Implementing these steps is a good start in establishing AI transparency and accountability. Building trust among fans and stakeholders is key as we work to reimagine the future of sporting events.