CSPs face multiple challenges in network operations. These include long delays in diagnosing the root cause of faults and identifying the right solutions, making and rectifying unnecessary changes in the network, and an inability to be proactive in assurance. The net effect can be poor customer experience and inefficient operations. This Catalyst is developing a solution that combines and correlates all the relevant data in a common data layer to immediately identify the root causes of faults and potential solutions, and support intent-based service assurance. The common data fabric will aggregate data from alarms, performance, configs, topology, logs and results through a single pane of glass, using both predictive and generative AI to learn with minimal input from network experts. The solution can then recommend accurate, immediate, detailed and automated resolutions to network issues. The Catalyst is employing TM Forum best practices, including the Open Data Architecture and APIs. The team will directly measure the impact of the project by tracking how the resulting solution reduces costs and effort in level 1, 2 and 3 operations. The solution should also reduce the wastage resulting from incorrect or unnecessary changes to the network, time spent on service operating manuals and the poor performance of the network due to incorrect or sub-optimal configurations.