How AIOps Tools Revolutionize IT Operations

Only AIOps tools can fill this massive need; that’s why Gartner calls the impact of AIOps for IT operations “transformative.” Only two years ago, in 2018, the use of digital experience monitoring tools stood at a mere five percent; by 2023, 40 percent of IT operations teams expected to employ AIOps tools to monitor applications and infrastructure.

Driving this momentum is the explosive growth in data volumes generated by new applications and infrastructure. To keep the lights on, enterprises continue adding whatever domain-specific monitoring tools they need. However, these tools are often reactive, and not nearly robust enough to handle complex application topologies. Data processing is limited to conventional filtering, normalization, threshold-crossing, and basic correlation.

With huge numbers in play, and with IT teams working in unconnected silos, incident damage can quickly run out of control. As the technology stack evolves, managing these issues quickly becomes an urgent—even emergent—mission for ITOps and DevOps teams.

Domain-specific monitoring tools are fine at delivering insights within their own realm. However, ITOps staff lack a holistic view of enterprise apps to support their triaging and troubleshooting. By contrast, AIOps platforms focus the power of AI and Machine Learning (ML) on big data. That delivers holistic, enterprise-wide insight—accessing machine-generated data, human-generated ITSM, and the whole massive knowledge base of the private and public domain.

Fully 68 percent of those who have deployed it credit AIOps tools as key to their success. With such an inarguable record, what could possibly throw sand in the gears of AIOps transformation? Yet, IT/Cloud operations lag behind where they ought to be, often continuing to manually triage major incidents and outages. They still rely on the personal knowledge and expertise of individuals, who answer calls, respond to support tickets, and draw from knowledge bases. Current IT and cloud operation tools continue to be rules-based, domain-dependent, and costly to deploy and manage. They lack automated AI learning and must contend with black box methodology. Such shortcomings hurt performance, disrupt revenue, and damage the brand—not to mention stressing and exhausting the teams tasked with managing application availability and performance. Clearly, ITOps and DevOps teams need a better way to gain insights and predict outages, reducing Mean Time to Isolate (MTTI) and Mean Time to Resolution (MTTR) before customers start reporting a degraded experience. 

AIOps platforms are powerful engines that address a comprehensive span of AI in IT Operations use cases, including major incident detection, AI/ML alerting, dynamic Configuration Management Database (CMDB), root-cause analysis, and event correlation. AIOps tools for DevOps specifically address monitoring alerts and anomaly reduction; AIOps for Incident Management covers major incident detection and prediction. The capabilities and benefits that AIOps platforms bring to enterprises a valuable tools for ITOps and DevOps teams.

In a nutshell, cloud-native Aisera delivers the real-world benefits of AIOps platforms to Cloud and DevOps—the ability to autonomously predict, locate and identify major incidents and outages before they occur. Customers’ support and operations teams enjoy a holistic, single-pane view across L1 to L4 with over 300 pre-built remediation actions for IT and DevOps environments. Aisera proactively identifies major incidents and automates root cause analysis to prevent outages and quickly remediate service disruptions.

We’ve already discussed the financial impact when those abilities are lacking. Now let’s look at the other side—the benefits of being able to proactively identify and resolve outages and major incidents in these complex enterprise and cloud environments. Aisera’s AIOps dramatically increases business uptime and lights-on operations.

By identifying major incidents in real-time, Aisera reduces alerts by 70 percent, predicts 70 percent of outages, and cuts man-hours by 80 percent. Customers can identify 80 percent of P1, P2, or P3 major incidents with 80 percent accuracy, offering advance warnings of three to four hours—or better—in detecting major incidents before they become outages or P1 Incidents. With 65 percent of major incidents predicted, customers can expect an over 60 percent reduction in operations costs and 50 percent improvement in IT productivity.  

The transformative value of Aisera shows up strikingly in its Robotic Service Automation (RPA) capabilities. Aisera auto-populates its system with intents and utterances that trigger RPA actions—and customers can actually improve and augment these to make them even more specific.

Remarkably, Aisera accomplishes all of this without a need to code: Aisera is a revolutionary low-code platform with easy out-of-the-box capabilities; you won’t need experts to run Aisera’s solutions.

Aisera’s Generative AI for IT operations comes with pre-built remediation actions to speed up resolution and eliminate outages. It includes real-time dynamic CMDB and probabilistic service maps to resolve root causes and reduce man-hours in war rooms.

Aisera offers more than 100 built-in integrations to alerts, ticketing, and remediation systems, along with auto-correlation and real-time dynamic CMDB for servers, networks, storage, apps, and services. Real-time service maps make it swift and simple to automate root cause analysis.

Aisera’s customer Autodesk achieved the following measurable AIOps results: 95 percent reduction in alerts volume; 50 hours earlier outage detection; and 80 percent reduction in MTTR. This worked out to a 90 percent increase in uptime, 80 percent reduction in man-hours, and savings of $10 million! 

Read the case study to learn more about how Autodesk achieved these numbers with Aisera’s AIOps solution.