Modern alerting systems face the challenge of excessive noise due to static, threshold-based alerts that are ill-suited for dynamic environments, often leading to alert fatigue and delayed issue resolution. Intelligent alerting, as discussed in the article, leverages AI and machine learning to move beyond static rules by learning the natural behavior of systems, adapting to patterns, and distinguishing between expected fluctuations and genuine anomalies. New Relic's AIOps enhances this by applying algorithms that monitor, correlate, and route telemetry signals, reducing noise through anomaly detection, correlation, and predictive alerting. This approach allows teams to focus on meaningful alerts, anticipate potential issues, and improve mean time to resolution (MTTR), thereby minimizing customer frustration and financial losses. The implementation of intelligent alerting results in context-rich, accurate, and actionable alerts, enabling engineering teams to maintain system reliability at scale.