In my opinion, AIOps can be highly effective at solving alert fatigue, especially in large and complex IT environments where traditional monitoring tools generate thousands of repetitive or low-priority alerts. By using machine learning to filter noise, correlate related events, and prioritize incidents based on impact, AIOps helps teams focus on the alerts that truly require action instead of wasting time sorting through constant notifications. It can also improve over time by learning normal system behavior and detecting anomalies more intelligently, which leads to faster response times and less operational stress. However, organizations may face challenges such as poor-quality data, difficult integration with existing monitoring tools, high initial setup effort, and the need for proper tuning to avoid false positives or missed alerts. There may also be resistance to trusting automated decisions without human oversight. Overall, AIOps is a powerful solution for reducing alert fatigue, but it works best when supported by clean data, strong observability practices, and gradual adoption.