Enhancing Site Reliability Engineering Through AIOps: A Framework for Next-Generation IT Operations

The increasing complexity of modern IT infrastructures has pushed traditional operational approaches beyond their limits. This paper explores the integration of Artificial Intelligence for IT Operations (AIOps) within Site Reliability Engineering (SRE) practices to address this challenge. I present a framework for enhancing core SRE concepts such as Service Level Objectives (SLOs), Service Level Indicators (SLIs), and error budgets through AI-driven capabilities. Our approach enables more dynamic reliability targets, intelligent anomaly detection, and automated remediation while maintaining the engineering rigor of SRE. Case studies demonstrate significant improvements in key operational metrics: 87% reduction in alert noise, 73% decrease in mean time to detection, and 62% of common infrastructure issues resolved automatically. The proposed framework provides a systematic path for organizations to evolve from traditional SRE to AI-enhanced reliability practices while addressing common implementation challenges including data quality issues, skills gaps, and organizational resistance. This integration represents a fundamental shift in IT operations from reactive human-centered approaches to proactive AI-augmented engineering disciplines capable of managing unprecedented scale and complexity.

Aims: To develop and validate a framework that integrates Artificial Intelligence for IT Operations (AIOps) within established Site Reliability Engineering (SRE) practices, addressing the growing complexity of modern IT infrastructures.

Study Design: A mixed-method research approach combining case studies, controlled experiments, and quantitative analysis across multiple industry sectors.

Place and Duration of Study: The research was conducted across three major organizations in financial services, healthcare technology, and e-commerce sectors between January 2023 and February 2024.

Methodology: I developed an integrated framework enhancing five core SRE functions with AI capabilities. Implementation followed a four-phase methodology addressing technical, process, and organizational aspects. Effectiveness was measured through comparative analysis of key operational metrics pre- and post-implementation, including alert volumes, detection times, resolution rates, and operational burden.

Results: Implementation demonstrated significant operational improvements across all organizations. Key results include: 87% reduction in alert noise while maintaining critical issue coverage, 73% decrease in mean time to detection for system anomalies, 62% of common infrastructure issues resolved automatically without human intervention, and 47% reduction in SRE on-call burden. The financial services organization identified five previously unmonitored SLIs that significantly impacted user experience, while the e-commerce platform successfully predicted capacity-related incidents 30-45 minutes before impact.

Conclusion: The integration of AIOps with SRE practices creates a powerful combination capable of managing the scale and complexity of modern IT environments. The framework enables organizations to progress from reactive to predictive operations while maintaining the engineering rigor of traditional SRE. Future research should explore incorporating emerging technologies such as large language models and developing industry-specific implementations for sectors with unique reliability requirements.