CX Optimization via AI-Driven SOC over Autonomous Networks – Phase II advances the industry’s ability to deliver consistently superior customer experience by transforming network operations from reactive support into a proactive, autonomous, and continuously optimized capability. Building on the outcomes of Phase I, this Catalyst enhances OODA-based operational architectures by introducing Digital Twins and intent-led AI agents into both the execution and analysis of Closed-Loop Automations (CLAs).
In highly competitive markets, CSP differentiation increasingly depends on service quality, reliability, and the ability to anticipate and prevent customer-impacting issues. Traditional operational models struggle to manage the growing complexity of autonomous networks, often reacting to problems only after customer experience has degraded. This Catalyst directly addresses that gap by enabling predictive, intent-driven CX optimization that improves Net Promoter Score (NPS), service consistency, and operational efficiency.
Phase II extends the previous solution by embedding AI agents across the full CLA lifecycle. In the first innovation layer, CLAs defined by agents, intent-based AI continuously evaluates and evolves automation logic using insights from a Knowledge Engine and simulations in Digital Twins. This ensures that automation strategies remain effective as network conditions, services, and customer expectations change. In the second layer, agents as part of CLAs, AI agents actively participate in decision-making and execution, formulating remediation plans in response to live network events, validating them through Digital Twin simulations, and iteratively refining actions before safe deployment. Successful strategies are fed back into the Knowledge Engine, enabling continuous learning and optimization.
Aligned with TM Forum Open Digital Architecture (ODA), the VOF framework, and Autonomous Networks principles, the solution ensures scalability, interoperability, and measurable business value. Success is assessed through CX-centric and autonomy-driven KPIs, including improvements in NPS, perceived service quality, service stability, and customer-centric resolution times, alongside increased levels of network autonomy and reduced operational effort.
By combining Digital Twins, intent-led AI agents, and closed-loop learning, this Catalyst establishes a robust blueprint for AI-driven Service Operations Centers (SOCs). It enables CSPs to systematically predict, prevent, and resolve CX issues before customers are impacted—delivering seamless, reliable, and high-quality connectivity experiences while accelerating the journey toward truly autonomous networks
