Automation Analytics & Intelligence (AAI) is the only platform that delivers a single-pane-of-glass SLA view across multiple workload automation vendors. Turn workload data into predictive business insights — across Automic, AutoSys, CA-7, ESP, Control-M, IWS, and Tidal — from one console.
Best for
Most enterprises run multiple workload automation platforms from different vendors. Each scheduler has its own monitoring tools, its own SLA tracking, and its own reporting — creating blind spots where cross-platform dependencies fail silently. AAI is the only solution that unifies this view.
When workloads span AutoSys, Control-M, and mainframe schedulers, no single tool shows the end-to-end picture. Operations teams piece together SLA status from multiple consoles and spreadsheets.
AAI aggregates real-time data from every supported platform into a single analytics view — giving operations teams cross-vendor visibility they cannot get anywhere else.
Traditional monitoring only tells you when an SLA has already been missed. By the time a breach is detected, business processes are already impacted and recovery options are limited.
AAI uses predictive analytics to forecast SLA outcomes before they happen. Operations teams get early warnings and can intervene proactively — turning reactive firefighting into planned action.
Schedule changes in complex multi-platform environments are high-risk. A timing change in one scheduler can cascade across dependent workloads in others, and the impact is invisible until production breaks.
AAI lets teams simulate changes before deploying to production. Model the impact of new workloads, schedule adjustments, or infrastructure changes across the entire automation landscape.
Real-time aggregation of workload data across Automic Automation, AutoSys, CA-7, ESP, Control-M z/d, IWS z/d, and Tidal Workload Automation.
No other product on the market provides this level of multi-vendor, multi-platform visibility from a single console.
Define, track, and manage SLAs that span multiple automation platforms. AAI correlates workload dependencies across schedulers so SLA tracking reflects the actual business process — not just individual job status.
Real-time dashboards show SLA health at the business-service level, not just the scheduler level.
Predictive models analyze historical and real-time workload data to forecast SLA outcomes. Operations teams receive early warnings about potential breaches with enough lead time to take corrective action.
Understand trends, identify bottlenecks, and optimize workload schedules based on data — not guesswork.
Simulate the impact of schedule changes, new workloads, or infrastructure modifications before they reach production. See how changes in one platform affect dependent workloads in others.
Reduces change-related incidents in complex multi-vendor automation environments.
View workload automation from a line-of-business perspective rather than a scheduler perspective. AAI maps technical job chains to business processes so stakeholders see what matters to them.
Bridge the gap between IT operations and business outcomes with reporting that non-technical stakeholders can understand.
Automatically identifies the critical path across multi-platform workload chains. As conditions change — delays, resource contention, failures — the critical path is recalculated in real time.
Operations teams always know which jobs are most at risk of impacting downstream SLAs, regardless of which scheduler runs them.
Large enterprises typically run three or more workload automation platforms acquired through mergers, departmental choices, or mainframe-to-distributed evolution. Each platform has its own monitoring — creating dangerous blind spots where cross-platform dependencies are invisible.
AAI connects to every supported scheduler and provides a single analytics layer. Operations teams see SLA status, workload health, and dependency chains across all platforms without switching between consoles.
Organizations with strict SLA commitments cannot afford to discover breaches after the fact. Financial services, healthcare, and manufacturing environments depend on batch processing completing on time — and the cost of late delivery can be measured in regulatory fines, missed market windows, or production shutdowns.
AAI forecasts SLA outcomes using predictive models built on historical and real-time data. Operations teams receive early warnings with enough lead time to reroute workloads, allocate resources, or escalate before business impact occurs.
Many organizations are consolidating or modernizing their workload automation environments — migrating from legacy mainframe schedulers to distributed platforms, or consolidating multiple tools into a smaller set. These transitions are risky without visibility into cross-platform dependencies.
AAI provides the analytics foundation for transformation planning. Teams can map existing workload dependencies, simulate migration scenarios, and validate that SLAs will be maintained during and after transitions.
Most organizations rely on native scheduler tools or manual tracking for SLA management. AAI is the only platform that provides cross-vendor analytics and predictive intelligence across the entire automation landscape.
Automation Analytics & Intelligence (AAI) is a predictive analytics platform from Broadcom that provides a single-pane-of-glass view of SLA performance across multiple workload automation platforms.
It is the only solution on the market that delivers cross-vendor, cross-platform visibility spanning Automic Automation, AutoSys, CA-7, ESP, Control-M z/d, IWS z/d, and Tidal Workload Automation.
AAI supports Automic Automation, AutoSys Workload Automation, CA-7, ESP Workload Automation, Control-M z/d, IWS z/d, and Tidal Workload Automation.
It aggregates data from all these platforms into a single analytics view regardless of vendor, giving operations teams unified visibility they cannot achieve with native scheduler tools alone.
Yes. AAI uses predictive analytics to forecast SLA outcomes based on historical and real-time workload data. Operations teams receive early warnings about potential SLA breaches so they can intervene before business processes are impacted.
The dynamic critical path feature recalculates in real time as conditions change, so the predicted risk is always current.
No. AAI sits on top of existing workload automation platforms as an analytics and intelligence layer. Organizations keep their current schedulers and gain unified visibility without migrating workloads or changing workflows.
This makes AAI particularly valuable during automation modernization projects where multiple platforms coexist during transition periods.
AAI provides change simulation capabilities that let operations teams model the impact of schedule changes, new workloads, or infrastructure modifications before deploying them to production.
This reduces the risk of unintended SLA disruptions from changes made in complex, multi-platform automation environments where dependencies span multiple schedulers and vendors.
VirtualizationWorks helps organizations evaluate Automation Analytics & Intelligence for their multi-platform environments, plan deployment, and understand licensing options.
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