Causal AI is an advanced artificial intelligence approach that focuses on identifying the exact causes and effects of events, distinguishing it from traditional correlation-based AI, which only predicts outcomes based on statistical relationships. Unlike conventional data science methods that often fail to explain why events occur, causal AI employs fault-tree analysis and a top-down approach to determine system-level failures by examining component-level issues. This method enables precise root-cause analysis, automatic anomaly detection, and business impact assessments, enhancing decision-making in DevOps, CloudOps, and SecOps by providing a clear understanding of cause-and-effect relationships. Causal AI's deterministic nature allows for more accurate, unbiased analysis and recommendations, making it crucial for solving complex human and business problems, particularly in the era of generative AI. This approach is exemplified by solutions like Dynatrace Davis® AI, which integrates causal analysis to enhance observability and automate responses, thereby improving system reliability and compliance with service-level agreements.