# Vodafone's Network Operations AI Transformation Journey ## Company Overview and Challenge Vodafone, a major telecommunications provider, faced several challenges in managing their network operations and customer experience: - Complex legacy Operations Support Systems (OSS) infrastructure with hundreds of siloed tools - Difficulty in correlating network performance with customer experience - Slow incident response times and complex troubleshooting processes - Challenges in making data-driven network investment decisions - Limited ability to integrate device-level analytics with network performance data ## Technical Infrastructure and Data Platform ### Cloud Migration and Data Integration - Partnership with Google Cloud Platform established approximately 5 years ago - Successfully integrated hundreds of network data sources to the cloud - Currently managing over 2 petabytes of network data - Implementation of unified data platform combining: ### Key Technical Solutions ### Net Perform Platform - Advanced device analytics capability - Recently migrated to Google Cloud Platform - Enables real-time monitoring of customer device network experience - Integrates with traditional network monitoring systems - Provides correlation capabilities across multiple data sources ### Unified Performance Management - Consolidation of over 100 traditional Performance Management systems - Standardized data presentation in Google Cloud - Designed for AI model consumption - Enables cross-functional data access and analysis ## AI and GenAI Implementation Strategy ### AIOps Implementation - 4-5 year journey in AIOps development - Focus areas: ### GenAI Integration - Used as a complementary technology to traditional AI/ML approaches - Key applications: ### Smart CapEx Initiative - GenAI-powered network investment planning - Integration of multiple data sources: - Cross-functional team collaboration for improved decision making ## Organizational and Process Changes ### Team Structure and Collaboration - Promotion of cross-functional working methods - Breaking down of traditional data silos - Emphasis on data sharing across departments - Integration of commercial and technical expertise ### OSS Modernization Program - Ambitious three-year transformation plan - Target of 50% reduction in OSS tools (approximately 600 applications) - Focus on simplification and modernization - Creation of unified systems replacing multiple legacy solutions ## Results and Benefits ### Operational Improvements - Enhanced ability to pinpoint network interventions - Faster problem resolution through AI-assisted troubleshooting - Improved field engineering efficiency - Better correlation between network performance and customer experience ### Data Capabilities - Unified view of network performance - Real-time device-level analytics - Enhanced data quality and consistency - Improved accessibility of complex network data insights ### Customer Experience - More personalized service delivery - Improved network performance monitoring - Better guided diagnostics journeys - Faster incident resolution ## Future Roadmap ### Short-term Goals - Complete OSS modernization program - Further integration of GenAI capabilities - Expansion of AI-powered network investment planning ### Long-term Vision - Transform entire network lifecycle management - Further reduce operational complexity - Continue building on the Google Cloud partnership - Enhance AI-driven decision making across all operations ## Key Learnings and Best Practices ### Technical Considerations - Importance of strong foundational infrastructure - Need for unified data platforms - Value of cloud-based solutions for scale and integration - Significance of data quality and consistency ### Organizational Aspects - Critical nature of cross-functional collaboration - Importance of breaking down traditional silos - Value of empowering teams with data access - Need for cultural change in data sharing ### Implementation Strategy - Start with strong infrastructure foundations - Focus on data integration and quality - Gradual introduction of AI capabilities - Balance between modernization and operational stability - Importance of long-term partnerships with technology providers