From Reactive to Proactive: Revolutionizing MSO Incident Response with AI

Challenge:

A leading Managed Service Operator (MSO) was equipped with extensive monitoring capabilities across its network, applications, and infrastructure. Despite the comprehensive scope, the system was inherently reactive, with DevOps teams mobilizing only post-incident. The mandate was clear: harness AI/ML to shift from reaction to prevention, aiming to pre-empt system outages and reduce Mean Time To Repair (MTTR).

Solution:

Our solution architecture team initiated an end-to-end ecosystem buildout, integrating advanced AI to transform the MSO’s incident response capabilities:

• Infrastructure Innovation: Utilizing AWS Lambda and Google TensorFlow, we constructed an end-to-end ecosystem.
• Algorithm Advancement: We developed and refined a suite of AI algorithms capable of real-time incident prediction and root cause analysis.

Results & Benefits:

• Alert Optimization: The system now generates fewer than 10 critical alerts per day, a drastic reduction from the husystendreds previously reported.
• Predictive Outage Identification: The new AIOps ecosystem provides an average of 45 minutes’ advance notice before outages, enabling preemptive action.
• Targeted Troubleshooting: AI-driven insights direct the DevOps team to specific areas of concern, streamlining the troubleshooting process.