Imagine your telecom network as a huge, complex city with roads, traffic lights, and repair crews. AI here is like a smart traffic control center that watches everything in real time, predicts where traffic jams and accidents will happen, and automatically sends crews or reroutes cars before customers even notice a problem.
Telecom operators struggle with complex, heterogeneous networks, rising traffic and service expectations, and high operations costs. The paper focuses on how to adopt AI technologies to automate and optimize network operations (fault management, performance management, resource optimization) as part of broader digital transformation, reducing outages, manual interventions, and OPEX while improving service quality and agility.
For an operator, the moat comes from proprietary network telemetry, historical fault/performance data, and domain expertise encoded into models and processes. This data plus tight integration into OSS/BSS and operational workflows makes the AI operations stack hard to replicate quickly.
Hybrid
Vector Search
High (Custom Models/Infra)
Processing and storing massive volumes of high-frequency network telemetry (time-series and logs) in near real time, while keeping model inference latency low enough for closed-loop automation and adhering to telecom-grade reliability and security requirements.
Early Majority
This work focuses on the organizational and technological adoption path of AI within network operations as part of digital transformation, rather than just pitching a single vendor product. It emphasizes how telecom operators can systematically integrate AI across assurance, optimization, and automation workflows—highlighting change management, data readiness, and architecture patterns that differentiate mature AI-ops programs from isolated pilots.