To build an autonomous network that will increase customer satisfaction and reduce costs, CSPs need to be able to accurately manage the network status. One important requirement is network configuration information. However, many CSPs track network configurations using multiple methods, and the data is siloed and not standardized. These issues, which also impact IT systems, are particularly prevalent in legacy networks. To solve this problem, this Catalyst is using AI to collect a wide variety of configuration information and process it into data formats which will enable CSPs to visualize and manage network configurations. The standardized data is also provided to the network inventory management function, which can then optimize network configurations. The ultimate goals are to enable the network to heal itself, in the event of a fault, and allow CSPs to make intent-based changes to the network configuration management. By employing AI to process and format data, CSPs don’t need to conduct manual data analysis, processing, and formatting – thereby paving the way for more autonomous networks. Furthermore, end-to-end network configuration management will improve customer satisfaction, reduce support costs, streamline operations and improve CSPs’ return on investment.