As workload automation scales across complex hybrid environments, it is no longer sufficient to treat it merely as a backend operational function. Organizations require an intelligent control plane so they can precisely measure automation’s financial impact, effortlessly access underlying data, and seamlessly adapt deployments to modern infrastructure standards.
Today, I am pleased to announce the general availability of Automation Analytics & Intelligence (AAI) v26. This release delivers structural capabilities designed to give organizations absolute control over their infrastructure.
What new features are included in Automation Analytics & Intelligence v26? This release delivers dedicated financial analytics, a conversational interface for data exploration, modernized cloud-native container support, and advanced AI-driven security. With these capabilities, teams can align technical execution directly with strategic business planning.
Here is a closer look at the primary advancements in this release and the practical value they bring to your operations.
Historically, automation has been categorized as a flat, opaque IT expense, making it difficult to justify costs or plan budgets effectively. AAI v26 solves this by introducing a robust financial intelligence model that enables organizations to calculate the actual total cost of ownership (TCO) for their automation practices. Administrators can now establish clear financial visibility.
How can Broadcom AAI v26 help measure workload automation costs? With this release, administrators can define specific cost variables, such as software, infrastructure, and labor. They can then use rule-based filters to map job executions directly to the cost centers that utilize them.
By visualizing these execution costs through configurable chargeback and showback reporting, IT leadership can attribute workload expenses accurately to specific business units. This transforms automation into a measurable service, providing the concrete data required to support formal internal chargeback models and responsible IT financial planning.
Investigating performance trends and system health often requires users to have specialized knowledge so they can construct complex queries. AAI v26 removes this technical barrier by integrating a natural language chat interface. With this new interface, users of all technical skill levels can query workload data simply by asking questions.
To ensure low-latency performance and strict accuracy, the system uses generative AI exclusively to construct the query, while the actual data retrieval relies on direct, deterministic access. This significantly reduces the time it takes to go from initial question to actionable insight.
By safely embedding generative AI within deterministic data retrieval, AAI v26 embodies the core principle of the intelligent control plane: Operationalizing AI with absolute trust and strict governance. Furthermore, operators can save their conversation histories and seamlessly publish these conversational queries as standard data insights. This democratizes data access across the organization, helping teams proactively address creeping performance issues before they have an impact on downstream processes.
AAI v26 is the first release to use frontier AI models to scan the source code for potential security vulnerabilities and remediate them before the release's general availability. This AI-driven scan-and-remediate pass adds a proactive layer of defense on top of our existing security testing, catching and fixing issues earlier than traditional methods alone.
As enterprise infrastructure evolves, deployment flexibility is critical to maintaining operational efficiency. To align with scalable infrastructure standards, AAI v26 now provides official container images for all AAI components, enabling seamless deployment on OpenShift.
How does container support on OpenShift simplify AAI deployment? This containerized approach offers an automatically configured, single-instance setup that drastically reduces the manual effort required for initial installation. Plus, this container support establishes a clear path for future high availability (HA) architectures.
Importantly, this flexible model protects your existing infrastructure investments. Customers can configure the containerized AAI server to connect directly to their existing databases (such as Oracle, Microsoft SQL Server, and PostgreSQL). The server can also connect to automation engines running outside the cluster. For customers maintaining their current footprint, traditional installation and upgrade processes remain fully supported to ensure zero disruption.
This release also delivers a suite of targeted, customer-driven enhancements—including secure Airflow proxy support, extended 18-month SLA trend analysis, and streamlined dependency tracking.
We are eager to show you how these advancements can optimize your workload automation strategy and bring complete transparency to your IT operations. To learn more, watch the "What's New in AAI" webinar.
A: AAI v26 introduces a new financial intelligence model. With this model, administrators can define cost variables (software, labor, infrastructure) and map job executions to specific cost centers. This enables leaders to leverage configurable chargeback and showback reporting.
A: The chat interface uses generative AI exclusively to build the user's search query. Actual data retrieval relies on direct, deterministic access to guarantee precise results.
A: AAI v26 provides official container images for OpenShift deployments. These deployments can connect to customer-managed databases like Oracle, Microsoft SQL Server, and PostgreSQL. Traditional, non-containerized setup and upgrade methods also remain fully supported.
A: Broadcom used frontier AI models to perform a scan-and-remediate pass on the source code prior to releasing the software for general availability.