CA Technologies marked a major milestone in the company’s autonomous strategy with the availability of CA Digital Experience Insights which is now combined with the power of CA Operational Intelligence and CA Automic Service Orchestration. Together they form the new CA Artificial Intelligence for IT Operations (AIOps) platform, to enable IT teams to automate and eliminate key tasks and make self-healing applications a reality.
The CA AIOps-driven platform leverages new, innovative AI, machine learning and automation capabilities. The platform normalizes, correlates and analyzes the rapidly increasing volume and variety of IT operational data across the entire digital delivery chain. Seamlessly spanning cloud to mainframe, it provides for superior user experiences, while speeding innovation and increasing IT efficiency.
In a recent survey conducted by TechValidate, 76 percent of customers indicated that predicting probable future events, that may impact availability and performance, is the primary benefit of AIOps platforms.
“Speed, scale and customer experience define value in today’s digital economy, and AI-driven systems of intelligence that increase revenue and improve operations are critical to our customers’ digital transformation,” said Ashok Reddy, GM of DevOps Solutions at CA Technologies. “With the advent of nondeterministic, ‘self-driving’ apps, only CA can deliver the AI-driven analytics and machine learning required to autonomously predict and remediate incidents whereby transforming IT into a strategic competitive advantage.”
According to Gartner, “By 2023, 30 percent of large enterprises will be using artificial intelligence for IT operations (AIOps) platforms and digital experience monitoring (DEM) technology exclusively to monitor the nonlegacy segments of their IT estates, up from 2 percent in 2018.” Furthermore, “by 2023, AIOps platforms will become the prime tool for analysis of monitoring data. Today's domain-specific monitoring tools will become specialist, midlevel managers, who, while continuing to exist, will feed their important data into AIOps for consolidated, higher-level analysis.”
To create apps that are truly self-healing, IT teams need systems that can automate problem recognition and the execution of multiple corresponding steps to fully remediate issues across complex hybrid IT environments. CA Digital Experience Insights uses machine learning and complex pattern matching to automatically identify the root cause of common app performance issues such as limited computing capacity and Java memory errors. Through a seamless connection to CA Automic Service Orchestration, this root cause identification automatically triggers a cascade of corresponding actions such as scaling up additional resource, initiating restarts or failing over to active resource. The end result is that many common performance issues can be remedied before they ever impact end users, often without any human intervention.
Capabilities and benefits of the CA AIOps platform include:
- Comprehensive Contextual Operational Intelligence. CA Digital Experience Insights ingests structured and unstructured data from IT performance monitoring tools spanning mainframe to the cloud and any third-party source into a single, resilient data lake. Supported coverage includes metric, alarm, log, topology, text and API data.
- Proactive Closed Loop Remediation. CA Digital Experience Insights combined with Automic Service Orchestration offers predictive analytics to help solve complex IT problems like performance and capacity. Configuration issues can be detected proactively (before they impact users) and remediated automatically.
- Vendor-agnostic Integrations. Customers can more quickly and easily stream metric, event, log and topology data to and from any third-party monitoring, management, analytics, and visualization tools, including Splunk, IBM, Elastic, ServiceNow, Dynatrace, AppDynamics, SolarWinds, Puppet, Chef, Tableau and more.
- Pre-packaged Algorithms and CA Integrations. Built-in machine-learning-driven algorithms, dashboards, and integrations speed time to value for customers. The CA AIOps platform also integrates with a wide variety of CA solutions.
- Powerful open source-based engine. Built on top of CA Jarvis, a powerful analytics engine that leverages open technologies such as Elasticsearch, Apache Kafka®, and Apache Spark™, the solution scales and allows teams to more easily integrate with third-party business or IT data sources to further enrich the data set.
Unlike point monitoring and incident management tools, CA offers cross-tier correlated insights, enabled by AIOps, that provide IT Ops teams with streamlined insight into the state of the customer experience. Understanding how underlying apps and infrastructure affect performance and having the availability and intelligence to predict and prevent future problems is critically important in this day and age. In TechValidate’s research, 82 percent of surveyed customers agreed that CA has the breadth and depth of monitoring expertise to deliver the cross-correlation of data from app to infra to network[
The Latest
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.