Key Capabilities of AAI

Broadcom's Automation Analytics & Intelligence (AAI) transforms enterprise workload automation through numerous features that provide three strategic capabilities: Unified Automation Observability, Advanced SLA Management, and Predictive Analytics for Workload Automation.

Unified Automation Observability

Cross-Platform & Cross-Vendor Visibility

AAI provides the industry's only single-pane-of-glass view across multiple workload automation platforms including Automic Automation, AutoSys, CA7, Control-M, Tidal, IWS, and Apache Airflow, spanning mainframe, distributed, and cloud environments. This unified visibility eliminates automation silos that create fragmented views and increase risk for outages, addressing the critical need identified by 91% of surveyed organizations for centralized workload automation visibility. Organizations gain complete understanding of cross-platform dependencies, shortening times to resolution and enabling more strategic automation governance while supporting digital transformation initiatives that require coordinated automation across diverse technology stacks.

Data-Driven Performance Forecasting

Historical trends, current conditions, and planned changes are analyzed to predict future automation performance, considering seasonal variations, infrastructure changes, process modifications, and business growth patterns. This forecasting supports effective capacity planning and resource management by providing quantitative predictions about future performance requirements, enabling infrastructure investments and optimization initiatives based on data-driven predictions rather than reactive responses. The strategic value extends to supporting business confidence in automation services by providing predictable performance characteristics that business teams can rely on when developing new processes, enabling informed decisions about service level commitments and customer-facing capabilities while supporting competitive advantage through reliable and scalable automation services.

Business Process Visualization

AAI can identify and group all jobs related to each critical business process to build jobstreams, shifting your enterprise automation approach from traditional technical object hierarchies to business-centric jobstreams that provide stakeholders with intuitive views of end-to-end business workflows even when they span multiple systems. This business process perspective improves communication between IT and business teams by presenting automation performance in familiar business terms rather than technical jargon, enabling more effective prioritization based on business impact. The visualization capability supports strategic planning by clearly demonstrating how automation contributes to specific business outcomes, facilitating better decision-making about process optimization and resource allocation while supporting compliance activities through clear business process documentation.

Real-Time Dashboards & Reporting

Interactive, personalized dashboards provide role-appropriate views for IT operations, scheduling managers, business process owners, and executives with self-service access to current automation status and performance trends. The platform reduces reporting bottlenecks by enabling stakeholders to independently access relevant information through web interfaces, customizable views, and ad-hoc report generation capabilities. This self-service approach improves organizational efficiency by eliminating dependencies on central IT teams for routine performance inquiries while supporting strategic decision-making through executive dashboards that demonstrate automation business value and enable data-driven discussions about optimization priorities.

Critical Path Visualization

Dynamic critical path analysis continuously identifies the specific sequence of jobs that determine overall business process completion times, adapting to changing conditions rather than relying on static dependency mapping. This real-time analysis enables teams to focus troubleshooting efforts on jobs that genuinely impact SLA compliance, dramatically reducing time-to-resolution for performance issues while revealing optimization opportunities where improvements deliver maximum business benefit. The capability supports strategic planning by providing quantitative insights into automation performance bottlenecks and enabling data-driven decisions about process optimization priorities and automation architecture changes that will deliver measurable business value.

Job Status & Processing Load Monitoring

Comprehensive monitoring tracks individual job execution states, resource utilization patterns, and processing load distribution to support both operational management and strategic capacity planning. With this, organizations have the detailed insights into infrastructure utilization patterns they need to be able to optimize resource allocation, identify load balancing opportunities, and support charge-back models by tracking resource consumption by business unit or application. This operational intelligence reduces costs through more efficient resource utilization while providing the data foundation for capacity expansion decisions based on actual usage patterns rather than theoretical requirements or vendor recommendations.

Advanced SLA Management

Predictive SLA Management

Statistical modeling analyzes in-flight automation processes against historical performance patterns to forecast completion times and identify potential SLA violations before they occur, enabling proactive intervention rather than reactive damage control. This predictive capability fundamentally changes SLA management by providing advance warning and specific remediation recommendations, such as which jobs to restart or prioritize, resulting in measurable improvements like the 68% reduction in SLA breaches achieved by AAI customers. The proactive approach reduces operational stress and costs associated with emergency response procedures while improving business relationships through more reliable service delivery and better alignment with digital transformation objectives that require predictable automation performance.

Dynamic Critical Path Discovery

Real-time critical path identification automatically adapts to changing execution conditions, resource availability, and performance variations to ensure troubleshooting efforts focus on jobs that genuinely control business process completion timing. Unlike static dependency analysis, this dynamic approach reflects actual operational conditions, enabling more effective optimization efforts and revealing opportunities where performance improvements deliver the greatest business impact. The capability supports strategic initiatives by providing historical critical path analytics that identify systemic bottlenecks requiring architectural changes, enabling data-driven decisions about automation improvements that will deliver measurable business benefits while supporting continuous improvement programs.

Intelligent SLA Alerting & Integration

Intelligent alerting analyzes the statistical likelihood that specific events will impact SLA compliance, generating notifications only when intervention is needed to prevent business impact, thereby reducing alert fatigue while maintaining high sensitivity to genuine risks. The platform integrates with downstream systems including ServiceNow for automated ticket creation, Slack for team coordination, and other collaboration tools through APIs and webhooks, ensuring alerts reach appropriate stakeholders through preferred communication channels. This comprehensive approach to alert management reduces operational overhead while ensuring critical issues receive timely attention and appropriate escalation, supporting faster resolution and better coordination across multiple teams involved in incident response.

Self-Service Dashboards for Business Users

Business process owners, executives, and line-of-business managers gain independent access to automation performance data through intuitive, role-appropriate dashboards that eliminate dependency on IT teams for routine performance reporting. This democratization of automation data improves organizational efficiency by enabling faster business decision-making while reducing the operational burden on technical teams, allowing IT resources to focus on complex issues and strategic improvements. The self-service approach supports better business alignment by providing stakeholders with direct visibility into automation performance, enabling them to demonstrate value to senior management, identify optimization opportunities, and take ownership of automation outcomes while facilitating more effective collaboration during process improvement initiatives.

Business Process-Based Views

AAI shifts from traditional object-oriented monitoring to business-centric automation management by aligning jobstreams with business functions like financial closing, order processing, or regulatory reporting. This alignment enables stakeholders to understand automation performance in business terms rather than technical components, improving communication between business and IT teams while supporting more effective prioritization based on business impact rather than technical metrics. The business process perspective facilitates governance activities by providing clear documentation of how business-critical processes are automated and monitored, supporting regulatory compliance while enabling strategic planning that considers complete business workflows rather than individual technical components.

Proactive Alert Management

Statistical models analyze automation execution patterns against in-depth behavioral baselines to identify early warning signs of potential issues before they impact business operations, enabling IT leadership to develop effective alert strategies that move operations away from firefighting to an equilibrium of timely corrective actions. This proactive approach maintains high sensitivity to genuine risks while dramatically reducing false positives and irrelevant notifications that lead to alert fatigue among operations teams. The capability delivers strategic business value by providing more predictable and reliable services to customers, reducing costs associated with emergency interventions, and enabling better resource planning through advance warning of potential issues that require specialized expertise or additional resources to resolve effectively.

Predictive Analytics for Workload Automation

Predictive Analytics & Statistical Modeling

Proprietary algorithms analyze deep contextual data, derived metrics, and execution patterns to forecast future performance and identify potential issues, with models that continuously learn from operational feedback to improve accuracy over time. This analytical foundation enables quantitative confidence in automation management decisions by replacing intuition-based troubleshooting with precise, up-to-the moment data-driven analysis leading to more intelligent resource allocation and proactive intervention strategies. The predictive capabilities extend beyond immediate operational benefits to support strategic planning and digital transformation by enabling confident investments in automation expansion, infrastructure improvements, and process optimization based on quantitative analysis of likely outcomes and business impact rather than speculation or vendor recommendations.

Root Cause Analysis

For integrated AutoSys and CA7 engines, automated correlation of performance data and events across complete business processes pinpoints underlying causes of deviations and failures, considering dependencies and interactions that may not be obvious from individual automation engine perspectives. This intelligent analysis dramatically reduces the time and expertise required for issue resolution by providing immediate insights into likely root causes, enabling faster resolution while identifying patterns that indicate systemic issues requiring architectural changes. The capability supports organizational learning and continuous improvement by documenting issue resolution processes and contributing factors, enabling knowledge transfer to less experienced team members while providing insights that support preventive measures and process optimization rather than repeated tactical fixes to symptoms.

Historical Data Repository

The platform's unlimited historical data repository serves as the analytical foundation for all advanced capabilities, storing job execution data, definitional metadata, derived metrics, and comprehensive audit trails. This centralized data warehouse enables sophisticated trend analysis, baseline performance establishment, and regulatory compliance reporting while supporting predictive modeling through long-term pattern recognition. Organizations can demonstrate automation value to stakeholders through historical performance metrics, identify optimization opportunities through trend analysis, and provide scheduled audit trail reporting for AutoSys compliance requirements, to transform operational data into strategic business intelligence.

Process Optimization through Safe Simulations

Simulation capabilities for integrated AutoSys engines, enable IT teams to model proposed job definitional changes including runtime modifications, schedule adjustments, dependency changes, and resource allocation modifications to understand their performance impact on the overarching business process and SLA compliance before production implementation. AAI lends its extensive historical data and complex algorithmic prediction capabilities to its simulations to transform change management from risk-prone guessing into data-driven optimization, enabling confident implementation of improvements while providing quantitative validation of business cases for automation investments. The simulation capability extends beyond risk mitigation to support innovation by enabling teams to explore optimization opportunities that might be too risky without prior analysis, test different improvement approaches, and validate strategic automation decisions with concrete data about business impact and performance outcomes.

Process Optimization through Trend Analysis

Analytics track critical path evolution, job execution duration trends, and resource utilization patterns to identify both immediate optimization opportunities and long-term patterns requiring strategic attention or infrastructure investment. This analysis enables data-driven improvements by prioritizing optimization efforts based on potential business impact rather than intuition, while trending data supports proactive capacity planning by revealing automation workload evolution over time. The optimization insights support digital transformation objectives by enabling automation environments to scale efficiently while supporting cost optimization through identification of efficiency improvements, resource waste reduction, and better alignment between automation infrastructure capacity and actual business requirements based on quantitative analysis rather than theoretical projections.

Machine & Resource Utilization Analysis

Comprehensive monitoring of resource consumption patterns across machines, platforms, and time periods supports optimization initiatives, capacity planning, and cost management by identifying load balancing opportunities, underutilized resources, and efficiency improvements. Understanding processing load by machine or overall scheduler over time at various levels of detail enables smart infrastructure optimization by redistributing workloads for better resource balance, consolidating processing where appropriate, and making informed capacity expansion decisions based on actual usage patterns. AAI's utilization data supports optimization individual business process performance as well as cost allocation at a system level and charge-back models by providing detailed consumption data by business unit and application, enabling fair infrastructure cost distribution while supporting vendor management and contract negotiations through concrete usage data that informs purchasing decisions and service level negotiations for efficient cost management.