In my opinion, AIOps can be highly effective in reducing alert fatigue, especially in complex environments where traditional monitoring tools generate an overwhelming number of repetitive or low-value alerts, because it intelligently filters noise, correlates related events, and highlights the incidents that truly matter. By learning normal system behavior over time, it can also improve alert accuracy and help teams respond faster and more efficiently. However, to maximize its benefits, teams need to focus on having clean and well-structured data across logs, metrics, and events, properly tune alert thresholds, and continuously review and refine the system to reduce false positives and missed issues. It’s also important to integrate AIOps with existing observability tools and maintain human oversight so engineers can validate insights and improve decision-making. Overall, AIOps works best as a support system that enhances visibility and efficiency, rather than a fully autonomous solution, when combined with strong monitoring practices and continuous optimization.