Developing an AI-Based Incident Response Automation Framework to Minimize Downtime in IT Service Operations

Minimizing downtime in IT service operations has become a strategic imperative as digital infrastructures grow in scale, complexity, and interdependency. Traditional incident response processes are often reactive, labor-intensive, error-prone, and limited by human bandwidth, resulting in prolonged Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR). This review examines recent advancements in artificial intelligence (AI), machine learning (ML), and automation technologies that are reshaping incident response frameworks within modern IT service management environments. The paper evaluates the integration of AI-driven anomaly detection, predictive incident forecasting, automated triaging, root-cause analysis, and self-healing mechanisms across cloud-native, hybrid, and on-premises ecosystems. Emphasis is placed on architectural design considerations, knowledge-based reasoning models, natural language processing for alert interpretation, and reinforcement learning for adaptive automated remediation. The review synthesizes state-of-the-art findings, industry best practices, and emerging frameworks such as AIOps and autonomous IT operations. Additionally, it identifies challenges related to data quality, model drift, interoperability, explainability, cybersecurity alignment, and human oversight. The study concludes by proposing a conceptual AI-driven incident response automation framework that enhances operational resilience, reduces downtime, and supports continuous service reliability in dynamic enterprise environments.