Before adopting AIOps, an organization needs several key prerequisites such as mature monitoring and observability systems, automation capabilities, skilled IT and operations teams, strong IT process maturity, and integrated tools that can collect and correlate data across environments. These elements ensure that logs, metrics, and events are properly captured and can be acted upon efficiently. In my opinion, the most critical factor for successful AIOps adoption is high-quality data, because AIOps depends on accurate, consistent, and well-structured data to detect anomalies, identify patterns, and generate meaningful insights. Without reliable data, even advanced AI models and automation systems cannot deliver correct or actionable results, making data quality the foundation of effective AIOps implementation.