Generative AI is making its mark on the future of telecom network operations, offering a wide range of applications from predicting KPI values to improving customer experience. While operators and suppliers are already embracing these opportunities, challenges persist in implementing use cases quickly and avoiding isolated solutions that hinder scalability and ROI optimization.
In a previous blog, a three-layer model was introduced for efficient network operations, focusing on data understanding, foundational models, and automation. Challenges in applying generative AI in these layers include complexities in telecom network data, varying model capabilities, and the need for advanced simulation frameworks to identify optimal strategies.
Key considerations for incorporating generative AI across the three layers include optimizing telecom network data by automating data understanding, leveraging various models to gain insights into the network, and integrating AI with network simulations for optimal solutions. Establishing data expertise, building knowledge graphs, and translating data models are crucial processes for maximizing the potential of generative AI in the data layer.
For network analytics, operators can utilize large language models for understanding past and current network states and develop specialized models for predicting future states tailored to their unique operating characteristics. Collaboration with AI technology providers, establishing continuous feedback loops, and combining multiple models can enhance overall performance and reliability in the analysis layer.
In the automation layer, integrating generative AI with network simulations enables operators to determine optimal actions based on predicted network states without affecting the live network. Automated generation of scripts for executing actions using digital network twins and specialized foundational models can enhance network optimization and automate manual processes within telcos.
Generative AI is poised to transform telco operations, but addressing challenges such as data understanding, predictive analytics, and network optimization is crucial for successful implementation. IBM offers solutions in efficient data integration, specialized base models, and automated network optimization tools to help unlock the full potential of generative AI in telecom networks. For those interested in implementing generative AI use cases, reaching out to IBM can pave the way for a future of enhanced network operations and performance.
Article Source
https://www.ibm.com/blog/applying-generative-ai-to-revolutionize-telco-network-operations/