Reducing Mean Time to Repair (MTTR) with AIOps: An Advanced Approach to IT Operations Management

In the ever-changing world of IT operations, reducing downtime and rapidly resolving incidents are crucial and are two of the primary objectives. This paper investigates the use of artificial intelligence in IT operations (AIOps) to significantly reduce mean time to repair (MTTR). AIOps transforms the way IT services and systems are managed and troubleshooted by combining various machine learning algorithms, predictive analytics, and automation. Key findings show that AIOps incident detection is increased by 35%, improves problem-solving accuracy by 25%, and reduces MTTR by 40% across multiple services and systems. These improvements result in a significant enhancement of decision-making techniques and a widespread decrease in downtime. The findings of this research imply that using AIOps can significantly improve IT service and system reliability along with operational efficiency. The findings of this research suggest that using AIOps can considerably improve IT service and system reliability, as well as operational efficiency. Additionally, letting down MTTR and MTTI can boost user happiness, reduce operational costs, and increase the overall reliability of IT infrastructures. Furthermore, the widespread adoption of AIOps can lead to more flexible and responsive IT services and systems. This study concludes with advice for implementing AIOps methodologies and outlines avenues for future research to similarly optimize IT operations control. This research supports SDG 9 (Industry, Innovation, and Infrastructure), SDG 8 (Decent Work and Economic Growth), and SDG 12 (Responsible Consumption and Production) by promoting advanced, sustainable, and resilient IT operational practices.