There is increasing demand on telecommunications businesses to lower operating costs, improve network resilience, and provide outstanding customer service. Amdocs and Google Cloud unveiled Amdocs Network AIOps, a new network AI operations solution that leverages data to assist communication service providers in enhancing their networks and customer support.
The goal of this solution is to improve customer satisfaction by increasing networks’ efficiency and self-sufficiency. This marks the beginning of the telecom industry’s transition to a completely autonomous network.
Network analytics and automation
The Amdocs Network The complete platform AIOps was created to handle the intricacies of contemporary telecom networks. To deliver a full observabilities and automated AI solution, it is based on vast amounts of telemetry data and leverages Google Cloud’s data and AI capabilities, such as BigQuery and Vertex AI.
The answer is a three-layer network operations Artificial Intelligence platform called Observe, Decide, and Act that uses AI and ML to improve service dependability, automate network operations, and optimise performance.
The following are some of the main functionalities of the Amdocs Network AIOps Solution framework:
Data ingestion and mediation (Observe layer)
The platform gathers information from a variety of network sources, such as probes, operations support systems (OSS), business support systems (BSS), and network components.
AI-driven insights (Decide layer)
Amdocs Network AIOps offers actionable insights to assist predictive analytics and root-cause investigation through close interaction with Google Cloud’s BigQuery, Vertex AI, and Gemini services. This connection lowers the mean-time-to-resolution (MTTR) for events by enabling operators to leverage the power of cloud-based AI/ML technologies for improved network automation that enables proactive network management.
Automated workflows (Act layer)
Routine processes like network configuration, performance optimisation, and issue resolution are automated by the platform. This lowers the possibility of human mistake and frees up important resources.
Closed-loop automation (Act layer)
Using a closed-loop methodology, Amdocs Network AIOps continually learns from past actions and data trends to increase its precision and effectiveness over time.
Actionable network insights built on AI
Amdocs Network AIOps is already being used by telecom operators to generate insights for a range of networking use cases, including predictive maintenance, anomaly detection, and root cause investigation.
For telecom operators, network outages are a big worry as they result in lost income and unhappy consumers. By using the predictive capabilities of Vertex AI’s no-code or low-code models and correlating data from several network sources, the Network AIOps solution dramatically lowers downtime.
This system can precisely forecast the probability of future network failures by training machine learning models on previous network data, such as performance measurements, fault reports, and environmental conditions. In order to reduce downtime and guarantee continuous service, operators may use this to proactively plan maintenance, replace ageing equipment, and optimise network configurations.
Amdocs Network AIOps relies heavily on Google Cloud services like BigQuery and Vertex AI. These services assist telecoms companies achieve their objectives of increased network efficiency and reliability as well as better customer satisfaction by enabling the transformation and ingestion of petabytes of data, as well as near-real-time predictive analytics, anomaly detection, and correlation.
Your intelligent network copilot
Even the most experienced network engineers may find it difficult to handle the complexity of 5G networks. A single interface for thorough network monitoring and management across the entire stack of hardware infrastructure, containers, virtualisation software, and network applications (RAN, 5G core, and IMS core) in a multi-vendor environment is offered by the Amdocs Network Platform for Operations (Act layer), which is integrated with a multimodal gen-AI-powered live network assistant.
As an intelligent agent for network engineers, this gen-AI-powered helper provides:
Your intelligent network copilot
In order to detect any problems and correlate events from the infrastructure stack and multi-vendor workload, the assistant keeps a close eye on the network and uses data from the BigQuery data lake to proactively inform engineers.
Automated troubleshooting and remediation
The gen AI assistant can automatically identify typical network issues and even take corrective action, like restarting services or rerouting traffic, by utilising its extensive knowledge base and AI capabilities.
Multimodal and natural language interaction
Network engineers may communicate with the assistant by using voice, video, and graphics in addition to natural language words and sentences. This makes it simpler to ask questions, get advice, and get succinct, understandable responses.
Embrace the AI-Powered future of telecom
For telecom operators, the combined Network AIOps solution from Amdocs and Google Cloud offers several advantages:
Reduced operational costs
Significant cost reductions are achieved through intelligent resource allocation, automation, and predictive maintenance.
Improved network resiliency
Network stability is improved and downtime is reduced by proactive problem discovery and automated resolution.
Enhanced customer experience
An integrated and dependable user experience is offered by AI-powered optimisation, which increases client loyalty and happiness.
Increased efficiency
Network engineers may work more productively and devote more time to key projects with the aid of the gen AI Network Assistant.
Amdocs and Google Cloud’s partnership combines the greatest aspects of both companies: advanced Artificial Intelligence capabilities and extensive telecom experience. Telecom operators can optimise their 5G networks, embrace the AI-powered future, and provide outstanding customer experiences with the help of the combined Network AIOps solution.
