As CSPs begin to deploy cloud-native systems and other advanced technologies, their networks are becoming more complex, putting operation and maintenance (O&M) personnel under pressure. In the radio network, for example, configuring base station parameters can now be a lengthy procedure making it difficult for CSPs to keep pace with rapid changes in the network environment, which can quickly result in deteriorating end user experiences. This Catalyst is looking to use generative artificial intelligence (genAI) to address these challenges in a number of ways, while applying zero-trust and zero-risk principles in the network management resource layer. For example, the project is developing a series of digital assistant and digital expert solutions to ensure high stability in the core network and greatly improve routine O&M efficiency. For the radio network, the Catalyst is developing an assurance system which will use data compression technology to quickly identify network status changes and accumulate core data. This mechanism will be supplemented by a decision-making system, based on deep reinforcement learning and large AI models, which will be able to rapidly optimize the network to meet multiple objectives and perform closed-loop management. The Catalyst will also employ machine learning to automatically expand parameter ranges and optimization objectives, while absorbing expert optimization experience to improve the model’s performance. For the bearer network, the Catalyst will employ natural language processing technology to automatically identify customer intentions, select APIs and set parameters. The proposed solution will be able to query fault-related information through a mobile application running on end users’ cellphones, greatly reducing the time it takes to obtain fault information and the mean time to repair. The Catalyst team plans to measure the project’s feasibility and effectiveness by tracking the work order automation rate, the work order processing duration, the fault handling duration and other indicators. The goal is to automate 80% of service fault diagnoses, while enabling real-time responses to fault information queries, leading to a 60% improvement in O&M efficiency. Challenges * High Expenditures Due to Manual Processes: * Manual processes on business and operational levels, such as planning and optimizing networks, lead to high costs. * Use of Various Platforms and Tools: * The current approach involves using multiple platforms and tools for network optimization, which are often less accurate and slow. * Inefficient Data Processing and Analysis: * The existing data processing and analysis methods are inefficient, leading to high expenditures and slower decision-making. Operational and Commercial Impacts * Failure to Achieve Target EBITDA: * Not achieving the target EBITDA increase of 30%, impacting financial performance. * Inadequate Improvement in Customer Experience: * Less improvement in customer experience due to suboptimal network performance and slower response times. ##The Solution ## Core Capabilities of GenAI for Autonomous Networks (AN): 1. Intent/Experience: * Understanding Telecom Know-how: GenAI leverages deep domain knowledge to understand telecom-specific needs and operations. * Conversational O&M: Utilizing conversational AI for operations and maintenance, enhancing user interaction and reducing manual intervention. 2. Awareness/Analysis: * Multi-modal Perception: GenAI integrates various data sources to perceive network conditions accurately. * Complex Network Issue Analysis: Advanced analytics capabilities to identify and diagnose complex network issues. 3. Decision/Execution: * Unified Performance Prediction: Predicting network performance under various conditions to pre-emptively address potential issues. * Precise Network Simulation and Decision-making: Running simulations to test different scenarios and making precise decisions based on data-driven insights. Autonomous Network Levels and Effectiveness: * Level 1 (L1) to Level 5 (L5): * Progression from manual operation (L1) to high autonomous networks (L5), with increasing effectiveness through automation and autonomy. * GenAI and AI+ enhancements significantly improve network effectiveness, marking a value leap at Level 4 (L4). GenAI's Role in Enabling Intelligent Functions: 1. Intelligent Decision-Making: * Collaborators: China Mobile, ZTE, Huawei, Whale Cloud * GenAI supports intelligent decision-making by analyzing data and providing actionable insights, enabling informed and timely decisions. 2. Intelligent Analysis: * Collaborators: China Mobile, Telkomsel, Huawei, Sand Technologies * GenAI enhances data analysis capabilities, enabling deeper insights into network performance and customer behavior, leading to more effective optimization strategies. 3. Intelligent Intents: * Collaborators: China Mobile, MTN, AsiaInfo, Huawei * GenAI facilitates the understanding and execution of network intents, aligning operations with strategic goals and improving overall network management. Value Leap at L4: * Enhanced Effectiveness: * The integration of GenAI drives a significant leap in network effectiveness at Level 4, bridging the gap between basic automation and full autonomy. * This leap is characterized by more accurate predictions, efficient resource allocation, and improved network performance. * Strategic Impact: * Achieving Level 4 autonomy translates into strategic advantages, such as reduced operational costs, increased service reliability, and enhanced customer satisfaction. * By automating complex processes and enabling real-time decision-making, Telkomsel and other collaborators can achieve substantial improvements in operational efficiency and financial performance. These insights underline the transformative potential of GenAI in enhancing network operations, driving strategic planning, and optimizing site performance, ultimately leading to improved financial outcomes and customer experiences. ##Collaboration## #Diverse Champion Business Use Cases List# 1. Telkomsel with Sand Technologies Telkomsel and Sand Technologies Collaboration for Enhanced Strategic Planning and Site Optimization Telkomsel, Indonesia’s leading telecommunications provider, has embarked on a groundbreaking collaboration with Sand Technologies to revolutionize its strategic planning and site optimization processes. This partnership aims to leverage the advanced capabilities of generative AI and large language models (LLMs) to develop intelligent analysis solutions, driving significant improvements in operational efficiency and financial performance. At the core of this collaboration is the deployment of an innovative solution designed to address the complex challenges of strategic planning and site optimization in the telecommunication sector. By integrating Telkomsel's extensive network data with Sand Technologies' cutting-edge AI algorithms, the joint solution aims to provide actionable insights and predictive analytics that are critical for informed decision-making. Key Aspects of the Solution: * Intelligent Analysis: Utilizing generative AI and LLMs, the solution offers a sophisticated analysis of network performance, user behavior, and market trends. This enables Telkomsel to predict future demand accurately and optimize site deployments accordingly. * Strategic Planning: The AI-driven insights facilitate strategic planning by identifying optimal locations for new sites and necessary upgrades for existing infrastructure. This ensures that Telkomsel can meet the growing demand for high-speed connectivity while maintaining cost efficiency. * Site Optimization: The solution continuously monitors network performance and customer usage patterns, providing real-time recommendations for site enhancements. This proactive approach minimizes downtime and enhances user experience.