The Super Bowl is one of the largest sports entertainment events on the planet, bringing in more than a hundred million viewers and a billion in revenue.

But for NFL teams and sports entertainment in general, there is a long road to championship as franchises aim to build brand, grow fandom and maximize revenues.

One of the ways to make that happen is AI.

The technology is no stranger to the world of sports entertainment. Predating the modern era of generative AI — as far back as 2017 — big vendors like IBM were already discussing how AI would disrupt sport entertainment networks. The NFL itself is using AI to help improve player safety with a Digital Athlete system developed in partnership with AWS. The NFL is also using AWS to build gen AI-powered apps using the Amazon MemoryDB database.

For individual teams, both in the NFL and across the sports entertainment landscape, there are other options for implementing gen AI. One such option, launching today, comes from Elevate, a technology vendor led by Al Guido, who is also the president of the San Francisco 49ers NFL football team.

The company’s new Elevate performance and insights cloud (EPIC) data and AI platform combines consumer insights, ticketing management and property analytics to help sports and entertainment organizations engage better with fans. The platform helps organizations with targeted engagement efforts to better understand potential customer personas. That information helps determine stadium seating options, ticket pricing and fan retention. The platform has already been used by more than 25 organizations, including the Tennessee Titans.

Elevate has been in operation since 2018, but now with the advent of gen AI, the company is able to do much more with data.

“Building EPIC has reinforced a fundamental truth that we’ve seen and validated with our clients since we’ve been in operation — data is only as powerful as the decisions it enables,” Guido, Elevat’s chairman and CEO, told VentureBeat. “In sports, the challenge isn’t just capturing that data but harnessing it to drive real, actionable intelligence that improves fan engagement, revenue strategies and operational efficiency.”

The data challenges of building an AI-first engagement system

Elevate already has data for approximately 220 million people in its system. The company collects first-party data through its client work and relationships. This includes data on fan behavior, ticket sales, sponsorships and other property-related information. Elevate also licenses and purchases third-party data sets to further enrich user profiles.

Guido noted that many organizations collect what seems like infinite amounts of data, but they struggle to unify and leverage it. EPIC was designed to bridge that gap. 

To fully benefit from modern gen AI, data should be in a vector database format, Elevate contends. CIO Jim Caruso explained to VentureBeat that his company has undergone an intensive process to not only vectorize data, but to make sure it’s the right data to help inform business decisions.

There is no shortage of database vendors and technologies that claim to make vectorizing data simple. In reality, Caruso stressed that the vectorization process isn’t as simple as turning on a switch. As part of building EPIC, they reevaluated all data and how it could work together to provide the best insights. The actual vectorization process involved testing different approaches and processing pipelines to find the right balance of accuracy and performance.

Currently, Elevate uses Amazon Sagemaker to make its vectorization work.

How Anthopic Claude, XGBoost and Amazon Bedrock help to power AI insights for EPIC

Caruso explained that the EPIC system provides a wide range of AI-powered applications, from pricing tickets to developing consumer insights personas. Elevate is using a combination of different technologies to build those tools.

At the core is the Anthropic Claude Haiku 3.5 large language model (LLM), which has been fine-tuned on Elevate’s data. Claude provides the interface to ask questions and get insights based on different personas. 

For example, one persona could be a venue operator that wants to determine the best way to configure premium seating in a venue. That operator will need to understand who would be interested in those seats and how they should be marketed to different groups.

Elevate went beyond just identifying broad demographic segments, like suburban millennials. Instead, they created a series of distinct personas with a range of attributes including finances, buying preferences, entertainment choices and social networking engagement. The key goal is to provide very concrete, detailed personas that enable organizations to make specific business decisions.

The system also uses the XGBoost (eXtreme Gradient Boosting) open-source machine learning (ML) library via Amazon Sagemaker to specifically help with numerical data for ticket pricing.  XGBoost is a supervised ML algorithm that uses decision trees to make predictions. Caruso explained that his team converted historical data, as well as real-time data, into 55 different features. These include event details, inventory details and recent sales information. All were then then fed into the XGBoost algorithm. 

The competitive landscape for AI across sports entertainment

Guido said that across the NFL and beyond, the initial response to EPIC has been positive.

Many properties face similar challenges: fragmented data sources, evolving fan expectations and the need for smarter, more efficient revenue generation. Guido also clearly recognizes that the competitive landscape for this kind of technology is expanding. There are traditional customer relationship management (CRM) and analytics providers, like Salesforce, but in his view, they often lack the industry-specific intelligence that EPIC brings to sports and live entertainment.

“What sets EPIC apart is its deep integration with the realities of sports,” said Guido. 

How AI-powered insights are driving real-world impact for the Tennessee Titans

Among the early users of EPIC is the NFL’s Tennessee Titans. The team is working with Elevate as it develops a new $2.1 billion stadium set to open in 2027.

As part of the engagement, Elevate has helped lead sponsorship sales for the new stadium. The company developed a strategic partnership revenue roadmap, a category-specific go-to-market strategy and set annual sales goals through the stadium’s launch. 

With EPIC, the Titans have been able to build out detailed personas for fans to inform targeted marketing strategies, from messaging to premium seating and hospitality offerings. Although the new stadium is still several years away from opening, the Titans have been able to exceed sales targets for premium seating already, with data and AI-powered insights as the foundation.

It’s not just for the NFL; college athletics are also benefiting from AI-powered insights

While there is big money in the NFL, there is also a lot of opportunity (as well as many challenges) at other levels of sports entertainment, including colleges.

“University athletic departments are undergoing a profound digital transformation, and data is at the center of it,” Tom Moreland, chief commercial officer at the University of Illinois Athletics, told VentureBeat. “One of the biggest lessons we’ve learned is that technology alone isn’t the solution — strategy comes first.”

Moreland explained that his school has been prioritizing how it collects, interprets and applies data to enhance the experiences of its coaches, student-athletes, and fans. 

So far, the EPIC platform has provided University of Illinois Athletics with the crucial data-driven insights required to improve football and men’s basketball ticketing, as well as an annual giving model. Moreland said that the EPIC analysis provided intelligence that enabled the school to move beyond assumptions and make strategic, informed decisions. Ultimately, he noted, EPIC empowered his department to create a more engaging and sustainable model for loyal fans and donors.

“Athletic departments that take the time to invest in data quality, structure and application will be the ones that truly benefit from any new technology,”  said Moreland.