Admin configuration use case for AIOps

  • Release version: Australia
  • Updated April 1, 2026
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    Summary of Admin configuration use case for AIOps

    This use case guides IT administrators in configuring and optimizing ITOM AIOps for complex enterprise environments, demonstrated through a multi-cloud financial services organization. It highlights the transformation from reactive to proactive operations by implementing AIOps in a hybrid cloud infrastructure supporting critical trading, customer-facing banking, and internal systems with stringent availability requirements (99.9%). The configuration aims to reduce alert noise, enable predictive issue detection, unify visibility across environments, and empower NOC operators with AI-driven workflows and insights.

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    Configuration Journey

    • Phase 1: Event Sources and Intelligent Correlation – Integration of diverse monitoring tools (network, application, infrastructure, cloud-native) with event parsing and severity mapping. Use of CMDB relationships and business service dependencies to establish correlation rules that consolidate alerts into root cause events. Prioritization of alerts based on business criticality, ensuring immediate escalation for high-impact systems like trading applications.
    • Phase 2: Predictive Analytics and Machine Learning – Setup of Health Log Analytics to parse logs and detect anomalies, training machine learning models on historical metrics for baseline behavior, and configuring synthetic monitoring for continuous testing of critical user journeys. Service Reliability Management and SLO Management are configured to automate incident response, define reliability targets, and monitor service levels with early warning alerts. Service Observability integrates telemetry with CMDB data for rapid issue localization.
    • Phase 3: Unified Workspace and User Experience – Customization of the Service Operations Workspace to deliver role-based dashboards and workflows for NOC operators, incident managers, and executives. Integration with ITSM processes streamlines alert escalations and notifications. AI-powered assistance (Now Assist for ITOM) provides intelligent alert analysis, recommended actions, and contextual guidance with approval workflows for high-risk actions.
    • Phase 4: Performance Tuning and Optimization – Continuous refinement of correlation rules and alert thresholds based on operator feedback to reduce false positives and noise. Machine learning models are tuned with expanded data and adjusted sensitivity to improve prediction accuracy and adapt to seasonal patterns.

    Key Outcomes

    • Alert Reduction: Significant decrease in alert volume through intelligent correlation and noise suppression.
    • Proactive Detection: Early identification and resolution of critical issues before customer impact.
    • Faster Resolution: Reduced mean time to resolution via AI insights and automated workflows.
    • Unified Visibility: Single-pane-of-glass monitoring across hybrid cloud and on-premises infrastructure.
    • Predictive Capabilities: Anticipation of capacity problems and performance degradation.
    • Business Alignment: Alert prioritization and impact analysis aligned with business service criticality to focus on revenue and regulatory priorities.

    This configuration supports operational workflows for NOC operators and enables intelligent, proactive service management within complex IT environments.

    Learn how IT administrators configure and optimize ITOM AIOps for enterprise environments. This use case demonstrates the setup journey for a multi-cloud financial services organization.

    This use case demonstrates how IT administrators configure ITOM AIOps to transform operations from reactive to proactive. The scenario follows an IT administrator implementing AIOps capabilities for a global financial services company with hybrid cloud infrastructure.

    Configuration scenario

    The IT administrator is responsible for implementing ITOM AIOps across a complex environment including on-premises data centers and cloud services. The organization runs critical trading applications, customer-facing banking services, and internal business systems that require 99.9% availability.

    The configuration goals include reducing alert noise, enabling predictive issue detection, and providing unified visibility across all environments. The goals also include empowering NOC operators with intelligent workflows and AI-powered insights.

    Phase 1: Event sources and intelligent correlation

    The administrator configures Event Management to aggregate and intelligently correlate alerts from multiple monitoring tools and data sources.

    Event source integration
    The administrator integrates existing monitoring tools including network monitoring systems, application performance monitoring platforms, and infrastructure monitoring solutions. The administrator also integrates cloud-native monitoring services. Each integration is configured with appropriate event parsing rules and severity mappings.
    Correlation rules configuration
    Intelligent correlation rules are established based on CMDB relationships, time-based patterns, and business service dependencies. The administrator configures advanced correlation algorithms that consolidate related infrastructure alerts into a single root cause alert.
    Alert prioritization
    Trading system alerts are automatically designated as Priority 1 with immediate escalation during market hours. Customer-facing banking services receive Priority 2 classification with 15-minute response requirements. Internal tools receive lower priority during business hours with standard response times. Business service criticality scoring uses revenue impact, customer count, and regulatory requirements as weighting factors.

    Phase 2: Predictive analytics and machine learning

    The administrator configures advanced analytics capabilities that enable proactive issue detection and intelligent insights for operational teams.

    Health Log Analytics setup
    HLA is configured to ingest log data from critical applications and infrastructure components. The administrator sets up log parsing rules, anomaly detection thresholds, and predictive models. These models learn normal operational patterns and identify deviations that indicate potential issues.
    Metric Intelligence configuration
    Machine learning algorithms are trained on historical performance data to establish baseline behaviors for key metrics. The administrator configures anomaly detection sensitivity.
    Synthetic monitoring
    Synthetic transactions are configured to continuously test critical user journeys through API endpoints. The administrator sets up monitoring with 5-minute frequency intervals.
    Service Reliability Management configuration
    Service Reliability Management is configured to establish incident response workflows, collaboration channels, and automated remediation capabilities. The administrator sets up on-call schedules, escalation policies, and integration with communication platforms. This enables rapid response when alerts require immediate attention.
    Service Level Objective Management setup
    SLO targets are defined for critical business services based on customer expectations and business requirements. The administrator configures SLO monitoring for availability, performance, and error rate metrics. The administrator establishes error budgets and burn rate alerts that provide early warning when services risk missing reliability targets.
    Service Observability integration
    Service Observability is configured to combine Observability telemetry from external monitoring tools with CMDB relationship data. This data integration enables rapid identification of issues in specific microservices and their related entities without having to use an external tool.

    Phase 3: Unified workspace and user experience

    The administrator configures the Service Operations Workspace to provide role-based dashboards and workflows that enable efficient operations management.

    Dashboard customization
    Role-specific dashboards are created for different user types including NOC operators, incident managers, and executive stakeholders. Each dashboard displays relevant metrics, alerts, and service health information appropriate for the user's responsibilities and decision-making needs.
    Workflow integration
    Automated workflows are configured for common operational scenarios including alert escalation, and notification routing. The administrator establishes integration with IT Service Management (ITSM) processes. This creates seamless handoffs between monitoring and service management.
    AI-Powered assistance
    Now Assist for ITOM is configured to provide intelligent alert analysis, recommended actions, and contextual guidance. AI models use organization-specific knowledge including historical incident patterns, common error code translations, and proven remediation procedures to provide insights. Approval workflows are established for high-risk AI-recommended actions while allowing automatic execution of low-risk remediation steps.

    Phase 4: Performance tuning and optimization

    After initial deployment, the administrator continuously optimizes the system based on operational feedback and performance data to maximize effectiveness and user adoption.

    Alert tuning
    Based on operator feedback and false positive analysis, the administrator refines correlation rules and adjusts thresholds. The administrator optimizes alert prioritization to reduce noise while maintaining comprehensive coverage of critical issues.
    Machine learning model refinement
    Anomaly detection models are tuned based on operational patterns and seasonal variations. The administrator adjusts sensitivity settings, incorporates feedback on false positives, and expands training data sets. This improves prediction accuracy.

    Configuration outcomes

    The comprehensive ITOM AIOps configuration delivers measurable improvements in operational efficiency and service reliability:

    Alert reduction
    Alert volume reduced through intelligent correlation and noise suppression
    Proactive detection
    Critical issues identified and resolved before customer impact during typical operational shifts
    Faster resolution
    Mean time to resolution reduced through AI-powered insights and automated workflows
    Unified visibility
    Single pane of glass for hybrid cloud and on-premises infrastructure
    Predictive capabilities
    Capacity issues and performance degradation predicted in advance
    Business alignment
    Alert prioritization and impact analysis directly tied to business service criticality

    This configuration enables the operational workflows demonstrated in the NOC operator use case for AIOps. The configuration transforms reactive operations into intelligent, proactive service management.