AIOps Use Cases to Enhance IT Operations Productivity

AIOps Use Cases to Enhance IT Operations Productivity

Feb 05, 2021

Data has taken the front seat in leveraging the operational efficiency of the enterprise. Today, large sets of data get generated from multiple channels and aid organizations in critical decision-making. Sometimes, failure in live monitoring of these data becomes challenging for your IT operations teams and can result in uncontrolled delays or, in some cases, critical business loss.

Conventionally approaching these unchecked issues would fail. That is the reason why businesses feel the need for disruptive solutions. The AI-enabled IT Operations empower firms to monitor the live streaming data in real-time and detect the roadblocks much before they cause any damage.

What is AIOps?

AIOps is the abbreviation for Artificial Intelligence for IT Operations. It is the multiple layered solution suite that seamlessly leverages the IT operational throughput using Big Data and ML-enabled solutions. The AIOps platform delivers groundbreaking performance by collating data from multiple sources, spotting the enterprise issues in real-time, and providing an optimized solution for them.

AIOps automates the decision-making capability of the IT operations teams.

Top research and consulting firm Gartner has forecasted that the exclusive usage of AIOps and its multi-faceted monitoring tools for infrastructure modernization and application transformation will rise from 5% in 2018 to 30% in 2023.

Why AIOps is used?

Execution of AIOps generates optimized value with its proprietary model and right governance framework. The process starts by analyzing operations-related data, where the pattern matching techniques sift through the large data sets and remove the unwanted abnormalities. The AIOps industry-specific algorithms deep dive into the root cause of the business problem and customize the solution as per the use case. Later, the ML-enabled path processes the results and triggers real-time responses through the automated system. The algorithms are further continually trained, enhanced, and made suitable to handle similar business problems in the future without any glitches.

AIOps Use Cases to Enhance IT Operations are:

  1. Real-time & modernized IT operations
  2. Enabling improved customer satisfaction
  3. Improved mean time to resolution (MTTR)
  4. Reduced operational cost
  5. Enabling prediction-based insights.


1.Real-time & modernized IT operations

AIOps brings transformative results by enabling real-time analysis of the streaming data. With AIOps, businesses can easily detect the anomalies and business threats quickly that were once considered hard to spot. AIOps has also simplified the monitoring of the standard metrics by expediting the identification of the crucial threats before they occur.

The on-demand availability of the remediation measures leaves no room for service downtime. Thus, IT teams are made aware of such technical glitches well in advance. Hence, the outages get sorted much before, without disturbing the ongoing functioning of the solution.

IT operations is challenged by the rapid growth in data volumes generated by IT infrastructure and applications that must be captured, analyzed and acted on.
–Padraig Byrne, Senior Director Analyst at Gartner
2.Enabling improved customer satisfaction

AIOps, with its proactive and predictive approach-based feature, modernizes the business architecture and delivers an unparalleled user experience. It predicts the probable future events which can hinder the smooth functioning, availability or performance of the applications and even proactively remediate those before they become an issue. Thus, AIOps enable organizations to effectively serve their customers and increases customer-satisfaction level.

3.Improved mean time to resolution (MTTR)

AIOps helps in early detection and diagnosis of IT issues and finds the root cause of the incidents. Thus, helps in quicker resolution and remedial action. The unwanted IT intervention gets reduced drastically, which in turn optimistically improves the meantime to resolution (MTTR) of the organization.

4.Reduced Operational Cost

With AIOps, auditing of the metrics that trigger the application’s performance becomes easy. A well-defined AI-driven approach improves the CPU performance and bandwidth and helps manage the vast workload throughout the IT infrastructure. Improvement in the operational throughput gives a higher business return at a low cost. The forward-looking ML models align data-driven use cases with the client’s technology landscape to enable unmatched scalability.

Read the case study to know how we reduced the operational cost of a manufacturing firm with AIOps.

5.Enabling prediction-based insights

One of the surveys conducted by a research firm states that almost 97% of the IT professionals believe that automated AI/ ML Ops help deliver meaningful insights in the most optimized and feasible manner. The integrated solution of AIOps and ML-enabled models train and automate the data collected daily from several sources such as events, logs, sheets, and metrics. The predictive insights gathered from the entire toolchain help manage the critical workloads by benchmarking the operations against historical performance observed through the trend study.

To know more about AIOps use cases and how we helped our clients’ to enhance their IT operations team. Check here

Concluding Note

The organization’s infrastructure should support all the technology landscape of the enterprise to achieve digital supremacy. AIOps solutions harness the transformative capabilities of next-gen technologies like Artificial Intelligence and Machine Learning to help shape the organization’s future. Deployment of the layered AIOps-enabled cognitive intelligence by an enterprise can take them way ahead of their competitors.


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