How can AI drive value for teams now?
Eyebrows were raised when Laura Harvey, the head coach of the NWSL's Seattle Reign, revealed that she used ChatGPT to help devise game strategies. It started an instant debate over whether that was a forward-looking use of technology to support coaching or a mark of managerial desperation. It certainly was an effective way to create controversy on the use of AI in sport, but there is a more impactful discussion to be had about the front office application of AI. To wit, how can teams use AI to improve their revenues and operations? Sports teams are like most other companies in that they are excited about it, experimenting with it, and trying to figure out how it can add tangible business value. Here’s a practical digest of where AI could have the most immediate-term business impact off the field.
Ticketing
Most major teams have instituted some form of dynamic pricing (see our archive on the topic here). But the current dynamic pricing models are limited by the amount of data that can be taken into account. Demand is influenced by an almost limitless number of factors: the day of the week, the start time, the team’s record, etc. Incorporating and weighting all those possible factors into a rules-based algorithm is challenging. An AI-based model can adjust ticket prices in real-time based on all of the above, plus the star appeal of the opponent, weather forecast, competing local events, rivalry status, player availability, and even social media sentiment. This boosts revenue for high-demand games and helps fill more seats for lower-demand events.
Concessions and Merchandise
The ability of AI-based models to account for a far higher number of factors than existing methods also applies to in-stadium sales. Sunday afternoon games are likely to have a different purchase dynamic than a Friday night. AI could digest historical sales data, cross-referenced with external factors (like expected weather, start time, and opponent type), to predict how much of each item (e.g., hot dogs, craft beer) will be needed at each concession stand. These more detailed predictive analytics can be used to better project demand for specific food/drink offerings and merchandise, optimize inventory, reduce waste, and prevent stock-outs of popular items.
Marketing
Many teams have sophisticated segmentation and targeting programs in place for maximizing season tickets, renewals, packages, and single-game sales. AI could naturally add more detail to those approaches. But perhaps the bigger impact is how AI can help deliver on these segmentation schemas. AI makes hyper-personalized content possible that wasn't achievable before. A video from the team’s star player or coach that mentions a potential fan by name and invites her to see them play next week can be created for thousands of different fans in hours (with the appropriate permissions) using generative AI. The ability to build in anything you know about a fan into a dynamic video and/or audio communication extracts more value from the CRM system and data the team has at its disposal.
Operations
Just as AI can be used to better anticipate fan attendance and concessions inventory, it can be similarly applied to game-day staffing. Predictive models can more accurately forecast required staffing levels for game days (security, ticket takers, ushers, concessions) based on predicted attendance, gate traffic, and historical data, leading to potential labor cost savings without sacrificing service.
AI chatbots could also be used to take some of the administrative burden off existing staff. Implementing conversational AI on the team website or app to handle common fan inquiries (parking, game times, refunds, team stats, ticket purchasing) around the clock frees up human staff for more complex, high-value tasks that create a better fan experience.
Contracts and Player Negotiations
AI can help through all phases of contract negotiations. At the beginning, it can help quantify the range of a player’s potential value. By analyzing large datasets of player performance metrics, injury history, market trends, and peer salaries, it can generate a more objective valuation of a player's worth.
For drafting a contract, AI can generate industry-specific contract drafts (for players, staff, sponsors) based on pre-approved templates and clauses, significantly reducing the time spent on creating initial documents. AI tools can review external contracts (from agents or other teams) and rapidly compare their clauses against the team's internal "playbook" or historical agreements. This highlights non-standard terms, potential risks, and areas that require human review. It could also flag clauses that may violate league regulations (e.g., salary cap rules, collective bargaining agreement terms), local employment laws, or internal policies, ensuring legal compliance from the start.
Across multiple contracts, for leagues with salary caps, AI can run complex simulations of various contract scenarios (e.g., different salary structures, bonuses, trade options) to find the optimal way to allocate resources while remaining compliant with league rules.
What You Need
AI needs robust data to do any good. The first step to making AI work is having a solid data foundation. If your data is spread over several unconnected sources, it’s best to get that in order via a multi-cloud infrastructure or similarly accessible environment before undertaking AI projects. It’s a typical GIGO situation.
Where It’s Going
The practical ideas shared above represent achievable projects using existing tools. But they still represent a collection of one-off ideas for which you need to utilize separate AI applications. AI hawkers are promoting the next wave of agentic AI, where AI tools work together to complete a multi-step series of tasks against a higher objective. So instead of prompting an AI tool to do a specific task, you outline a higher strategic goal and have AI figure out how to deliver on it. Like all things AI, the hype is ahead of the reality. But if you can get some positive return use cases off the ground now, you’ll be in a better place to take advantage of the next wave.