# AI-Powered Fan Experience at Golden State Warriors ## Overview The Golden State Warriors, a major NBA franchise operating the Chase Center in San Francisco, implemented an AI-powered recommendation system to enhance fan experience across digital platforms. The project demonstrates how traditional sports and entertainment organizations can leverage modern AI infrastructure to deliver personalized experiences at scale. ## Business Context - Warriors operate an 18,064-seat arena (Chase Center) hosting both sports and entertainment events - Manage multiple business units including: ## Technical Challenge - Massive content generation from multiple sources: - Need to deliver personalized content to right users at right time - Limited technical resources (organization of ~600 people) - Required seamless integration across multiple platforms: ## Solution Architecture ### Core Components - Google Cloud Infrastructure ### Data Flow - Input Sources: - Processing Pipeline: ### Integration Layer - Built abstraction layer (GSW API) to enable: ## Implementation Strategy ### Key Principles - Focus on customer experience over technology - Data-driven decision making - Emphasis on personalization - Resource-efficient implementation ### Development Approach - Started with proof of concept ~2 years ago - Piloted recommendation engine 6 months ago - Iterative development with Google Cloud team - Leveraged existing Google Cloud infrastructure ## Technical Features ### Personalization Capabilities - Individual content recommendations for: - Context-aware suggestions based on: ### Infrastructure Benefits - Scalable data warehouse solution - Easy API integration - Robust analytics capabilities - Future-proof architecture ## Results and Impact ### Business Benefits - Enhanced fan engagement - Improved content discovery - More efficient resource utilization - Better event promotion ### Technical Achievements - Successfully handling large-scale data processing - Seamless integration across platforms - Efficient personalization at scale - Resource-efficient operation ## Lessons Learned ### Key Takeaways - Importance of focused implementation - Value of strategic partner selection - Need for clear organizational goals - Benefits of data-driven decision making ### Best Practices - Start with clear business objectives - Focus on customer experience - Maintain disciplined project selection - Leverage partner expertise - Regular tracking of NPS and customer satisfaction ## Future Directions ### Planned Developments - Integration with more platforms - Enhanced personalization features - Expansion to new venue displays - Integration with Gemini for workspace ### Strategic Vision - Continued focus on fan experience - Expansion of AI capabilities - Integration with new business units - Scaling to support growth ## Technical Infrastructure Details ### Data Management - BigQuery as central data warehouse - Historical data preservation - Real-time analytics processing - Scalable storage solution ### Integration Architecture - Custom API layer for flexibility - Modular design for scalability - Platform-agnostic approach - Future-ready infrastructure ### AI Implementation - Vertex AI for recommendations - Machine learning model integration - Automated content processing - Personalization engine ## Operational Considerations ### Resource Management - Efficient use of limited technical resources - Strategic partner collaboration - Focus on core competencies - Scalable infrastructure ### Maintenance and Support - Google Cloud platform management - API maintenance - Content pipeline monitoring - Performance optimization