In my conversations with IT leaders and practitioners, one topic comes up over and over: observability. Your organization is adopting powerful tools like Datadog, Splunk, and Grafana, and that’s a fantastic step. You're capturing logs, metrics, and traces to understand what’s happening across your complex IT estate.
But I want to pose a question: What are these tools leaving on the table?
Most observability solutions provide a good view of what happened and when. But they have a giant blind spot when it comes to workload automation. For example, they can show that events happened close in time—a correlation—but they can’t tell you that one caused the other. That’s because the deep, explicit dependencies—the real causation—live inside your automation.
This is the gap in which true automation intelligence provides its greatest value. We see this every day as we help customers move beyond monitoring to solving tangible business problems.
I like to think of this journey in three stages: Fix, maintain, and improve. A helpful metaphor to compare this to is buying and maintaining a home that grows as your family grows.
Fix: Moving from reactive troubleshooting to proactive insight
When you first move in, there may be some things to fix: You need to add window coverings, maybe change the leaky faucet, or do more serious work on the roof.
This is similar to our customers: They need to first solve their immediate pain points. They're struggling with manual tasks, digging through logs to answer ad-hoc questions from application teams, and spending far too much time in post-mortem "war rooms."
We recently worked with a large telecommunications company that was in this exact situation. Their leadership was asking critical questions about failures in their invoicing and critical file transfer processes, and the operations team simply couldn't answer them quickly. They didn’t have the data.
Once they found Automation Analytics & Intelligence (AAI), our advanced analytics solution purpose-built for automation, they went from concept to production within three months. Their goal wasn't just to report on failures, but to identify potential issues before they occurred. They're using AAI's historical run averages and machine usage analytics to see if deteriorating processes are heading toward an SLA breach, which would result in a delay in revenue capture. They’re fixing problems before they have an impact on the business.
Maintain: Gaining unified visibility to tame complexity
To continue with the house metaphor, once you’ve fixed the immediate issues and you’ve lived in the place for a while, regular maintenance is needed to keep it in order. Your family may have grown and so has the complexity of home maintenance.
The same is true for automation. As environments grow, so does complexity. Mergers, attrition, and new technologies lead to tool proliferation. You probably have distributed and mainframe automation, as well as home-grown tools, and everything in between. This creates silos, and no one has a single, holistic view.
We're seeing our customers make a big push to address these challenges. The VP of global IT operations at a major bank wanted to demonstrate how their services were improving business outcomes. They are using AAI dashboards to provide a single pane of glass in their global systems operations center, which shows cross-scheduler workflows from both CA 7 and AutoSys. They were doing SLAs "informally" before, but now they're formalizing and standardizing how they visualize and manage their services.
This isn't just about technical data; it's about packaging information so business leaders can understand the real-world impact of delays. For example, they can see precisely when a delay in a data processing job will affect the availability of information in the online customer portal or delay financial reporting. This context allows them to prioritize resources to fix the problem, communicate proactively with stakeholders, and make data-driven decisions that protect revenue and improve customer satisfaction.
For instance, another large retail bank used AAI to replace a 20-year-old, home-grown reporting engine for its critical mainframe jobs. Their old system was manual and fragile, making it difficult to get reliable daily reports. This not only created a significant audit risk but, more importantly, meant they couldn't guarantee that the critical jobs required to open their bank branches would be completed on time. Consequently, the business was constantly challenged with potential delays to branches’ daily opening times. With AAI, they now have the forecasting and visibility to proactively manage these jobs, mitigating audit risk and ensuring branches open on time, every time.
Improve: Optimizing workloads to drive business value
Back to the home metaphor: Once problems are fixed and you’ve gotten on a regular maintenance schedule, you may uncover opportunities for improvement. You may want to upgrade the kitchen or you may want to replace the HVAC system with one that is more energy efficient.
This is the same with automation. Once you have proactive insight and unified visibility, it is time to start actively making the business better. This is where our customers use deep historical analysis and predictive analytics to optimize their most critical workflows.
A global asset management firm, which processes 3.5 million jobs a month, came to us with this goal in mind. They shared with us that there is constant tension and frequent disagreements between IT and the business about priorities and performance. There was no objective way to measure performance or hold teams accountable. By standardizing on AAI, they were able to agree on key performance business metrics or SLAs, how these would be measured, and which services would be prioritized. They saw the value in AAI not just for monitoring, but for its predictive performance management and workflow optimization capabilities.
The outcomes were greater alignment between the business and IT, optimized processes, and a significant improvement in overall performance and customer satisfaction.
This is the highest level of maturity—when you use analytics to help the business leverage new opportunities. We're seeing that maturity in a range of customers, such as a major pharmaceutical company. Their IT organization is looking to build an automation center of excellence with AAI at its core, offering automation as a service to the business.
From pain points to possibilities
The path from tactical firefighting to strategic value creation is a journey of transformation. It begins by solving the immediate challenges you face today—the manual reporting, the risky home-grown tools, the endless post-mortem meetings. AAI provides the capabilities to address these initial pain points, but its real power lies in what comes next.
This is about transforming your operational data from a reactive troubleshooting tool into a proactive, strategic asset. It’s about gaining the business context needed to see not just what failed, but what the business impact is and what might fail next. By understanding the true causal relationships across your entire automation estate, you can move beyond simply keeping the lights on and begin to optimize processes, reduce risk, and deliver undeniable value back to the business.
