Key Takeaways:
Effective use of AI cultivates better fan experiences and revenue outperformance or “alpha” for sports franchise business operations teams.
Organizational alignment is required to structurally unlock the alpha potential of AI.
A strong data foundation is a prerequisite to using AI effectively.
Seek business value with AI beyond productivity and efficiency measures.
The Sports Alpha Framework:
This operator note covers the impact of AI and the areas of derivable “alpha” in the context of operating a sports franchise, which is made up of two separate business units with different objectives and decision makers: (1) the Team Operations (TOps) and (2) the Business Operations (BOps). The TOps of a franchise is tasked with maximizing the on field/court product quality, which is primarily measured through team performance (i.e., wins and playoff success) and expectations – how excited is a fanbase about a team’s performance outlook. BOps performance is measured by fan hope monetization efficiency and the quality of the created fan experience – whether in-venue, in the community, or digitally.
Within this framework, alpha is the portion of performance that can’t be explained by market forces or external factors (i.e., beta). The concept of the “Wins Above Replacement” (WAR) statistic is a good analogy. For TOps, the extension of WAR is simple: how well did the team perform in contrast to a league average team; a 0.500 club is our “beta” baseline. On the BOps side, the extension is more nuanced: how well would a “replacement-level” management team monetize fan hope given the team’s on-field performance. For BOps, the ‘beta’ baseline is informed by home market, brand, and team performance. In the case of TOps or BOps, the excess of team performance or monetization above this baseline is the positive alpha generated. Through our Insights practice, we’ve created the Arctos Sports Alpha framework to measure the prevalence of alpha across the sports ecosystem.
In this piece, we’ll focus on AI and BOps alpha. In future installments, we’ll discuss the Arctos Sports Alpha framework in more detail.
For business operations teams to unlock alpha from AI initiatives, we recommend:
Ensuring executive buy-in and organizational alignment for AI initiatives
Building embedded AI applications that leverage existing data architecture effectively
Chasing differentiated business value through revenue and fan experience uplifts – not just seeking internal productivity and efficiency gains
Executive Buy-in and Organizational Alignment:
Unlike other corporate settings, sports organizations do not have large engineering departments, let alone “engineering-first” constructs. The technology teams within franchises are lean and operationally focused. They support the technological infrastructure to run core ticketing and customer analytics, marketing and sales outreach, social and digital, and other IT services. In these leaner settings, it’s important to identify and empower an internal leader to be responsible for experimentation, progress, and results around AI initiatives.
These leaders do not need to be experienced in GenAI or machine learning to be effective. Through the conversations we have had across the sports ecosystem, we’ve seen two main archetypes be effective:
A commercial-oriented leader that grasps how AI and technology can shape revenue opportunities and operational efficiency. This isn’t necessarily someone with an engineering background, but rather someone that understands how different departments work together and has a proven track record for adopting new technologies. For example, this may be a strategy or business intelligence individual with exposure to season ticket sales, sponsorship analytics, and real estate initiatives. The horizontal scope ensures that AI initiatives aren’t siloed and use cases apply across the organization.
A technology-oriented leader who excels at translating business opportunities into practical solutions. This may be someone within your existing team (e.g., Head of Technology) who has a track record of translating technological innovation into commercial value.
If there aren’t obvious candidates internally to serve this role, you may need to look beyond your organization. Such leaders can be a boon for your AI ambitions if they can accomplish the following:
Assess Internal AI Readiness: If you work with a consultant, this is the first thing they’ll suggest. Empower a leader to interview departmental heads, hunt for pain points, and identify solutions that AI can help solve. This is routinely how we begin engagements with franchises around AI.
Establish a Framework for AI Governance: We view this as the rules of engagement covering baseline data access and security controls, approved tools (e.g., where it’s safe to use with proprietary information), procedures for requesting new tools, and training. Such a framework can spur engagement for the more reticent, while providing guardrails for the more adventurous.
Foster a Culture of AI Coaching and Experimentation: Junior talent has the most experience working with GenAI tools. They might be the best equipped within your organization to evangelize and teach others. Pair them with a central AI lead to help senior leaders and workers understand what AI can do.
Identifying an AI lead is only half the battle. What is equally important is empowering and equipping them. This has less to do with the resources provided and more to do with the environment around them. The most ambitious franchises treat AI as an organizational priority. AI leads who have owner and/or team president buy-in are given license to experiment and fail. High value ideas don’t need to be perfect on day one. The easiest AI applications will soon be table stakes, and will not be a source of differentiation or alpha. Working teams that do not have the latitude to occasionally fail will not break through the experimentation phase to deliver on the higher ROI opportunities.
Leveraging Existing Data Architecture Effectively
AI applications are made possible by a strong data foundation. Yes, GenAI models play a significant role, but these models are widely available. How organizations integrate these models into an existing technology stack create alpha opportunities. This is only possible with a clean and well-maintained data architecture.
To help you assess if your data architecture is “AI-ready” ask yourself the following question: What would it take to pull data for a report today? If the answer requires an assist from a technical member of your team, it’s unlikely your data architecture is AI-ready – data that is not cleaned or organized into centralized golden data sets. Expecting a GenAI model to not only access but also interpret and analyze this type of data is unrealistic. Fragmented and disorganized data structures will result in LLMs prone to hallucinations and limit viable use cases.
Other questions to ask yourself as part of a readiness assessment:
Can you access your data with natural language? If you can, this means that your data access is not reliant on technical staff, you have established golden data sets, and an LLM can access your data asset securely. You may be ready to explore “agentic” AI applications.
Are you utilizing the AI features from your existing vendors? If not, that’s a quick way to explore AI’s possibilities within your technical suite.
Are you anchored to vendor-provided solutions or is your team developing its own workflows? Overreliance on vendors in the long term will inhibit your ability to customize solutions for the unique aspects of your business. AI tools for outbound sales engagement is an easy example. Franchises may begin with vendor sales augmentations tools; but those who are forward-thinking are now developing their own AI agents to create more tailored sales experiences.
Chasing Differentiated Business Value
Many conversations around AI focus on cost cutting and efficiency. Such efforts are important, but using AI to enhance the fan experience and grow revenue creates true differentiation. What does this look like in practice? Take the previous example of sales team augmentation. While a byproduct may be leaner sales teams, if that’s the only goal, you may miss an opportunity. Think in terms of improving the ticket buying experience, adding higher-quality leads, and enhancing local market coverage.
Connect your sales teams with fan feedback loops as additional prospecting sources. An LLM can mine your feedback for themes like:
First time visitors – e.g., “Had a great time at my first game!”
Fan data enrichment – e.g., “Just moved to the city and…“ or “first time bringing my kids to a game!”
Crowd incidents – e.g., “There was an accident in my section [x], made the game hard to watch.”
Utilizing feedback like this can turn first-time visitors into repeat ticket buyers, update your customer profiles and demographic data, and follow-up with fans with negative experiences. This creates more fans, deepens the connection with existing fans and enriches your data asset for more use cases in the future.
How can you leverage GenAI capabilities to better connect to your local market? Think of the intersection of your content and marketing teams and partnerships efforts. GenAI effectively scales the production of personalized content. Every social media post, flyer, poster, advertisement, etc. can be created in any language - catered toward your local demographics and international audiences. You’re then reaching a greater and more diverse set of fans in a more effective manner. This narrative should fuel your partnership efforts showing how your franchise’s brand is bolstered through alpha-oriented goals beyond efficiency and cost-cutting measures.
There are other less obvious use cases of AI. Think about your current interview procedures for event staffing. Adding part-time workers today is time consuming. AI-native interview platforms can widen the funnel by interviewing candidates 24/7 in any language and provide evaluations before they ever speak with stadium operations. By filtering for helpful, enthusiastic – and potentially bilingual ushers – you can be more efficient in your hiring and simultaneously improve the fan experience.
Conclusion
Alpha is a clear sign of operational excellence and managerial skill. We assess alpha using our Arctos Sports Alpha framework and use this framework to strategize on how to harvest alpha in the future. We see real opportunity for AI to be foundational in generating alpha in both the qualitative aspects of fan experience and the quantitative aspects of growing revenue and profits. Wherever you are on the AI journey, whether you’re just getting started or you already have operational AI-workflows operations, we’d love to chat about where we can help, what we can learn, and the connection to generating alpha.
1 Conversely, the performance or monetization below this baseline is negative alpha generated