AIOps, or Artificial Intelligence for IT operations, integrates big data and machine learning to enhance automation and transform digital operations by providing actionable insights for IT teams. It aims to automate processes across the enterprise, particularly in complex environments driven by cloud-native technologies like microservices and serverless platforms. AIOps is applied in CloudOps for incident and event management, in DevOps to streamline and accelerate software delivery, and in SecOps to enhance application security. The technology processes data through four stages: collection, aggregation, analysis, and execution, with the analysis stage being the most challenging due to its complexity in identifying root causes. Deterministic AI, an alternative to traditional machine learning, employs fault-tree analysis for precise root-cause identification by modeling the topology of data centers or applications, allowing for quick and repeatable detection of technical and foundational issues. AIOps supports human operators by filtering data to reduce noise and alert fatigue, thereby enabling IT professionals to focus on critical tasks. The evolution of AIOps, especially with a deterministic approach, promises greater automation and efficiency in managing modern multicloud environments, enhancing the ability of DevOps teams to innovate rather than just maintain existing systems.