CX optimization via AI-driven SOC over autonomous networks - Phase II
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
