Alert fatigue is a persistent problem in operations, often leading to missed critical issues. The author argues that simply reducing the number of alerts, known as "alert on less," creates dangerous blind spots. Instead, the proposed solution is to maintain comprehensive alerting while implementing an AI-powered layer that processes these alerts locally. This layer groups related alerts into incidents, remembers past alert patterns, and generates situation reports. Human triage often fails because alerts are treated individually without historical context, leading to manual re-evaluation and excessive escalations. Tuning alerts, while sometimes necessary, risks losing valuable signal. A better approach involves organizing alerts, defining "sacred alerts" that always trigger a human, and providing alerts with essential context. The core innovation is "pattern memory," where previously investigated alerts are flagged with their outcomes, preventing redundant work. Grouping alerts into a single incident provides a clearer picture than a cascade of individual notifications. Generating situation reports offers management visibility without burdening engineers. Running the AI model locally ensures sensitive alert data never leaves the organization's network. Crucially, any alert processing layer must be fail-open, ensuring alerts still reach their destination even if the AI system fails. This approach integrates with existing monitoring tools and does not require altering current alert rules.