---
sourceDocument: Australia IT Operations Management
sourceDocumentLink: https://www.servicenow.com/docs/r/australia/it-operations-management

 Release :

    - australia

ft:locale :

    - en-US

ft:publication_title :

    - Australia IT Operations Management

ft:clusterId :

    - itom

bundleId :

    - itom

workflow :

    - Technology


---

# NOC operator use case for AIOps

# NOC operator use case for AIOps {#ariaid-title1}

Release version: Australia  
Updated April 1, 2026  
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## Summary of NOC operator use case for AIOps

This use case illustrates how ServiceNow ITOM AIOps enhances the daily workflow of a Network Operations Center (NOC) operator, shifting from reactive incident handling to proactive, intelligent IT operations management.
It follows a senior NOC operator at a global financial services firm managing critical trading and banking applications, showcasing how unified visibility, predictive analytics, and AI-driven insights enable faster, more efficient incident detection and resolution.
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## Key Features

* **Unified Visibility:** The operator uses the Service Operations Workspace to access a consolidated dashboard that prioritizes alerts, displays service health, trending anomalies, and predicted issues from multiple monitoring tools in one place.
* **Proactive Issue Detection:** Health Log Analytics identifies warning signs such as authentication errors and database connection warnings, predicts service degradation, provides root cause analysis, and recommends remediation before user impact.
* **Intelligent Alert Correlation:** Event Management correlates multiple alerts into a single actionable incident based on CMDB topology, maps business impact, and prioritizes alerts by criticality, simplifying operator response.
* **Performance Monitoring:** Synthetic transactions detect issues like transaction failures and API response delays early, enabling incident creation with impact assessment before customers report problems.
* **AI-Powered Resolution:** ServiceNow Otto interprets complex alerts, explains errors in simple terms, provides historical context, and recommends step-by-step remediation, allowing operators to resolve issues without escalation.
* **Proactive Capacity Management:** Machine learning detects abnormal resource usage trends, prompting the operator to initiate capacity planning and automated cleanups to avoid outages.
* **Unified Telemetry for Complex Incidents:** Service Observability combines telemetry data with CMDB relationships to pinpoint issues in distributed systems rapidly, facilitating faster problem resolution.

## Key Outcomes

* Prevention of service disruptions by resolving issues proactively before user impact occurs.
* Significant reduction in alert noise by consolidating over 70 alerts into 8 actionable items, improving operator focus.
* Faster incident resolution, decreasing average resolution time from 45 to 12 minutes.
* Enhanced alert context including business impact and recommended actions, improving decision-making.
* Streamlined operations through a unified management experience, replacing multiple disconnected tools.
* Increased operational efficiency and job satisfaction due to AI-driven insights and automated workflows.  
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.{#noc-operator-aiops-use-case__noc-operator-aiops-use-case-intro}

## Starting the shift with unified visibility {#noc-operator-aiops-use-case__morning-shift-startup}

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:{#noc-operator-aiops-use-case__morning-shift-startup-para-1}

* 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

{#noc-operator-aiops-use-case__morning-visibility-list}

Instead of checking multiple monitoring tools, the operator has one consolidated view with intelligent prioritization. This enables faster situational awareness and decision-making.{#noc-operator-aiops-use-case__morning-shift-startup-para-2}

## Proactive issue detection with predictive analytics {#noc-operator-aiops-use-case__proactive-detection}

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:{#noc-operator-aiops-use-case__proactive-detection-para-1}

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

{#noc-operator-aiops-use-case__detection-indicators}

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.{#noc-operator-aiops-use-case__proactive-detection-para-2}

## Intelligent alert correlation and impact analysis {#noc-operator-aiops-use-case__alert-correlation}

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:{#noc-operator-aiops-use-case__alert-correlation-para-1}

* 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

{#noc-operator-aiops-use-case__correlation-actions}

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.{#noc-operator-aiops-use-case__alert-correlation-para-2}

## Performance monitoring with synthetic transactions {#noc-operator-aiops-use-case__synthetic-monitoring}

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.{#noc-operator-aiops-use-case__synthetic-monitoring-para-1}

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.{#noc-operator-aiops-use-case__synthetic-monitoring-para-2}

## AI-powered alert analysis and resolution {#noc-operator-aiops-use-case__ai-powered-analysis}

At 1:15 p.m., a complex alert from the core banking system arrives with cryptic error codes. The operator uses ServiceNow Otto for IT Operations Management (ITOM) to receive:{#noc-operator-aiops-use-case__ai-powered-analysis-para-1}

* 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

{#noc-operator-aiops-use-case__ai-analysis-features}

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.{#noc-operator-aiops-use-case__ai-powered-analysis-para-2}

## Proactive capacity management with machine learning {#noc-operator-aiops-use-case__capacity-management}

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.{#noc-operator-aiops-use-case__capacity-management-para-1}

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.{#noc-operator-aiops-use-case__capacity-management-para-2}

## Complex incident resolution with unified telemetry {#noc-operator-aiops-use-case__service-observability}

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.{#noc-operator-aiops-use-case__service-observability-para-1}

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.{#noc-operator-aiops-use-case__service-observability-para-2}

## Key business outcomes achieved {#noc-operator-aiops-use-case__business-outcomes}

This use case demonstrates how ITOM AIOps transforms NOC operations by delivering:{#noc-operator-aiops-use-case__business-outcomes-para-1}

* 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

{#noc-operator-aiops-use-case__outcomes-list}

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.{#noc-operator-aiops-use-case__business-outcomes-para-2}