The successful operation of a modern company relies on the dependability of its software infrastructure. However, ensuring a robust and dependable software infrastructure can be challenging, as software applications are subject to continuous updates that can introduce bugs and performance regressions. To mitigate this challenge, many companies use Application Performance Management (APM) tools to monitor their digital devices and identify potential issues that could affect business operability. However, the large volume and heterogeneity of the data collected by these tools can make it difficult to effectively analyze and exploit the rich source of information available. In this paper, we propose RADig-X, a tool designed to support the identification and analysis of digital experience issues. RADig-X leverages AI algorithms and a ranking heuristic to: (i) detect anomalies in runtime metrics collected by APM tools, (ii) assess the relevance of these anomalies based on their impact on the overall IT infrastructure, and (iii) rank problematic software updates that may be the root cause of relevant anomalies. We report on the adoption of RADig-X by a large company that monitors over 30,000 digital devices around the world. Our results demonstrate that RADig-X is able to improve the effectiveness of the identification process of digital experience issues, by enabling to identify and address potential anomalies that could impact business operations. RADig-X is currently used in production within the case company to support the diagnosis and problem resolution of digital experience issues.