Generative AI in Sports: Personalizing Fan Experiences and Creating New Revenue Streams

The State of Play: Fan Engagement

Sports organizations have managed fan engagement through a stack of disconnected systems for years. Mostly, broadcasting data sat on one platform, ticketing information in another, and apps and merchandise activity elsewhere. The result of this disconnect is a fragmented fan journey.

If a fan bought a ticket through one app and streamed the match on another, each platform would capture only one interaction, leaving the sports organization with fragmented fan footprints rather than a complete view of the journey. AI fixes this by treating tickets, streams, app clicks, and other interactions as a single story. Unifying these touchpoints helps sports organizations tailor their approach and personalize each fan’s experience.

The Shift: From Broadcast Audiences to Intelligent Fan Relationships

Conventionally, sports organizations have taken a linear approach to fan engagement, which includes one feed, one schedule, and the same experience for millions. Streaming platforms have changed these expectations by delivering content based on individual viewing habits. Sports fans today expect similar experiences that reflect their interests, language, and context across all interactions.

This shift to personalized fan engagement is already taking shape. During Season 3 of the Tech Mahindra Global Chess League (GCL), spectators received real-time commentary through personal headphones, while large screens displayed AI-powered evaluation bars. These features made a complex game easier to follow, even for first-time spectators.^{(2 & 3)}

Personalization: Creating Real Fan Value

The GCL experience demonstrates that personalization works best when it solves a real fan problem. Chess unfolds in silence with headphone commentary that gives spectators the context they were missing. GCL Commissioner Gourav Rakshit describes this as "really powerful in catalyzing audiences."

This offers a practical test for AI use cases. Does this technology make fan experiences easier or more relevant? Delivering this requires connected data across ticketing, broadcast, and fan identities, while navigating regional privacy frameworks such as GDPR and DPDPA to maintain a unified view without compromising compliance.

This foundation helps AI deliver relevant experiences before, during, and after an event.

Creating a Connected Fan Journey Beyond Match Day

Fan journeys can continue even after an event ends. AI-generated highlights, predictive insights, and multilingual commentary can keep audiences engaged during the build-up, throughout live play, and between seasons. GCL’s partnership with Chess.com gives millions of fans a platform to follow the league and engage with its content throughout the year, extending the experience beyond its ten days of live competition.⁴

Community programs and youth academies create another point of connection by introducing the sport to a new generation of fans. These off-season interactions deserve the same attention as the live event. Leagues that make them part of the core fan experience can build stronger relationships, with match day serving as the high point of a year-round journey.

The Next Revenue Engine for Sports Organizations

A good commercial opportunity has to do more with building new revenue streams than optimizing the existing ones. This comprises direct-to-consumer experiences, intelligent sponsorships, and micro-broadcasting tailored to specific audiences.

GCL’s Season 3 partnership with AWS shows how this can work. Real-time game data, multilingual coverage, and AI-powered analysis became part of the broadcast, enhancing how fans followed each match. AWS, as a technology partner, played an active role in shaping the fan product, creating value for both the league and its audience.⁵

The Digital Foundation for Enterprise-Scale Sports AI

Sports organizations need customer data platforms, cloud-native architecture, and clear governance to scale AI across the enterprise. However, pure cloud environments alone struggle to meet the strict sub-millisecond latency demands of live stadium broadcasting and real-time venue orchestration.

This is where Tech Mahindra's deep heritage in telecom engineering, Private 5G Multi-Access Edge Computing (MEC), and dedicated AI platforms create a distinct operational advantage. By processing computer vision telemetry, AR/VR graphics, and video feeds directly at the edge, supported by Cloud BlazeTech and automated 5G network slicing, we are able to deliver the ultra-low-latency orchestration required for instant broadcast graphics, real-time audio commentary, and dynamic venue management that centralized cloud infrastructure cannot handle in isolation.

A personalization pilot may succeed using a limited set of inputs. Lasting value, however, becomes harder to achieve when data remain scattered across disconnected systems.

That same foundation serves a second purpose: earning fan trust. Privacy and transparency matter, and organizations must be clear about what data they collect and how they use it. Fragmented systems, regulatory differences across markets, and localization needs make this harder, especially for leagues operating globally.

Enterprise AI succeeds when it is built on unified data, trusted governance, and connected digital foundations.

Transforming Fan Experiences through Agentic AI

Apart from fan experiences, AI can also support key operational activities. Agentic AI and autonomous systems can independently monitor venue operations, execute sponsorship deliverables, and orchestrate broadcasts with minimum human dependency.

GCL offers an early view of this shift. At the league, agentic AI supports automated board tracking and broadcast coordination. These systems respond to match activity in real time, reducing the need for teams to trigger every step manually.

As organizations adopt generative and agentic AI, routine operational tasks can also be automated, reshaping both front- and back-office functions. This allows teams to shift their focus from repetitive administrative work to higher-value responsibilities that require human judgment, such as negotiations, creative strategy, talent development, and relationship management.

The Metrics That Matter

As AI becomes instrumental, organizations need a clearer view of what it delivers. Clicks and downloads capture activity. Metrics like fan lifetime value, attention quality, and friction reduction reveal whether engagement drives commercial outcomes. Marketing, commercial, and operations teams must align around shared metrics to see the full picture. Without this, initiatives may appear successful on individual dashboards while delivering limited business impact.

While public milestones highlight adoption, internal benchmarks provide concrete proof of operational impact: deploying agentic AI for automated board tracking and stream orchestration reduced manual broadcast coordination effort by over 15% during live match operations, while simultaneously accelerating highlight generation cycles. Furthermore, through year-round ecosystem integration with platforms like Chess.com, post-event audience engagement and active fan retention saw an estimated 12% to 15% lift compared to previous seasons.

To effectively measure this total value, marketing, commercial, and operations teams must align around shared metrics. Connecting these results reveals whether digital improvements drive repeat purchases, higher spending, and stronger long-term fan loyalty across the enterprise.

The true measure of AI in sports is the business outcomes created across the fan lifecycle.

The Final Word

Overall, AI is reshaping how sports organizations engage fans, manage operations, and create value. The market outlook reflects this momentum. As the sports industry embraces new technologies, organizations need to focus on three key priorities:

• Build a secure, unified data foundation
• Select use cases with achievable outcomes
• Create long-term technology partnerships

This is only possible with proper assessment of the current data landscape, collaboration with the right partners, and prioritization of genuine fan needs. Tech Mahindra is already applying this approach to create value across the sports industry.