NOC operator use case for AIOps

Follow a typical NOC operator through their daily workflow using ITOM AIOps capabilities to transform reactive operations into proactive, intelligent IT management.

This use case demonstrates how ITOM AIOps transforms a Network Operations Center (NOC) operator's daily workflow from reactive firefighting to proactive, intelligent operations management. The scenario follows a senior NOC operator at a global financial services company through a typical shift managing critical trading applications and customer-facing banking services.

Starting the shift with unified visibility

At 9:00 a.m., the operator begins the shift by logging into the Service Operations Workspace for ITOM. This unified dashboard consolidates all AIOps capabilities, providing immediate visibility into:

• Alert prioritization showing active alerts with intelligent ranking
• Service health overview with color-coded status of critical business services
• Trending anomalies detected by Metric Intelligence
• Predicted issues identified by Health Log Analytics

Instead of checking multiple monitoring tools, the operator has one consolidated view with intelligent prioritization. This enables faster situational awareness and decision-making.

Proactive issue detection with predictive analytics

At 9:15 a.m., the operator notices a warning on the Health Log Analytics dashboard indicating unusual patterns in trading application logs. The system detects:

• Increasing error rates in authentication services
• Memory utilization patterns that historically precede outages
• Database connection pool exhaustion warnings

The operator drills down into the anomaly. Health Log Analytics provides root cause analysis pointing to a recent configuration change, predicts high probability of service degradation within 45-60 minutes, and recommends specific remediation actions. This enables the operator to address the issue before it impacts users, preventing a potential trading system outage during market hours.

Intelligent alert correlation and impact analysis

At 10 a.m., multiple alerts flood in from various monitoring tools. These alerts concern network latency, application response time degradation, and infrastructure CPU spikes. Event Management uses CMDB topology data to:

• Correlate individual alerts to a single root cause: failed network switch
• Map the impact to affected business services using service topology
• Prioritize based on business criticality with trading applications marked as Priority 1

The operator sees one consolidated alert with full impact analysis. The operator uses the Service Operations Workspace to acknowledge the alert, run automated diagnostics, and initiate network team escalation workflows. This reduces multiple alerts to one actionable item with clear business context.

Performance monitoring with synthetic transactions

At 11:30 a.m., synthetic monitoring alerts the operator that the customer mobile banking application is experiencing issues. These include transaction failures and API response times exceeding SLA thresholds.

The operator uses the Service Operations Workspace to view the realted alerts and starts an investigation. The operator creates an incident with pre-populated impact assessment. Issues are detected before customers contact the help desk.

AI-powered alert analysis and resolution

At 1:15 p.m., a complex alert from the core banking system arrives with cryptic error codes. The operator uses Now Assist for ITOM to receive:

• Human-readable explanation of database connection timeout caused by connection pool exhaustion
• Historical context showing similar patterns occurred three times in past six months
• Recommended step-by-step remediation actions with risk assessment

Following the AI-recommended steps, the operator resolves the issue. Complex technical issues are resolved quickly without escalation using AI-powered insights from historical data patterns.

Proactive capacity management with machine learning

At 3:00 p.m., Agent Client Collector and Metric Intelligence alert the operator to unusual storage growth patterns, CPU utilization trending toward critical thresholds, and memory consumption anomalies.

The operator reviews anomaly details, creates a proactive capacity planning ticket, and schedules automated cleanup jobs for non-critical data. Proactive capacity management helps prevent weekend outages and emergency procurement needs.

Complex incident resolution with unified telemetry

At 3:30 p.m., a critical incident affects the loan processing application. The operator uses Service Observability to combine observability telemetry from external monitoring tools with CMDB relationship data. The operator correlates performance metrics with infrastructure health data.

The operator quickly identifies the issue in a specific microservice and its database dependency, enabling faster resolution by the development team. Complex distributed system issues are resolved with unified telemetry and topology views.

Key business outcomes achieved

This use case demonstrates how ITOM AIOps transforms NOC operations by delivering:

• Proactive issue prevention with three issues resolved before user impact
• Alert noise reduction with 70+ individual alerts consolidated into eight actionable items
• Faster resolution times reduced from 45 minutes average to 12 minutes
• Enhanced context with every alert including business impact and recommended actions
• Unified operational experience replacing multiple disparate tools
• AI-powered insights using historical patterns and machine learning predictions
• Automated workflows freeing operator time for strategic activities

The transformation from reactive firefighting to proactive, intelligent operations management enables NOC operators to maintain higher service levels. This reduces operational overhead and improves job satisfaction.