What is AIOPS, Top 3 Use Cases & Best Tools? in 2024

Updated on Jan 4

8 min read

Table of contents

What is AIOps?What is the difference between AIOps and DevOps?How Does AIOps Work?What are the types of data sources that an AIOps platform can ingest?Why is AIOps trending now?What are common AIOps use cases?Who Is Using AIOps?Who are leading vendors?Further reading

Digital transformation forces businesses to rapidly adapt new technologies. Using AI for IT operations (AIOps) reduces monitoring and intervention efforts, enabling companies to manage a more complex set of applications with the same technology team.

Gartner predicts that AIOps and digital experience monitoring tools will become more widely available.¹ Their prevalence will rise from 5% in 2018 among IT operations tools to 30% in 2023. As IT operations form a critical part of businesses, companies need to learn more about it and identify ways to integrate machine learning into their own systems management.

What is AIOps?

AIOps stands for Artificial intelligence for IT operations, also known as IT operations analytics (ITOA),which is the integration of AI and automation tools into IT operation processes, including event correlation, anomaly detection, and causality determination. Latest advances in AI can lead to more efficient and responsive IT operations. Forrester Research ² defines AIOps as:

Software that applies AI/ML or other advanced analytics to business and operations data to make correlations and provide prescriptive and predictive answers in real-time. These insights produce real-time business performance KPIs, allow teams to resolve incidents faster, and help avoid incidents altogether.

What is the difference between AIOps and DevOps?

DevOps is a collaborative approach that emphasizes collaboration between software development (Dev) and IT operations (Ops) teams. The main goal of DevOps is to streamline the software development lifecycle, from coding and testing to deployment and monitoring. DevOps aims to

Break down silos between teams
Automate processes
Establish a continuous integration and continuous delivery (CI/CD) pipeline.

This leads to faster development cycles, quicker deployment, and more reliable software releases. DevOps also promotes a culture of continuous improvement and communication among cross-functional teams.

AIOps, on the other hand, is centered around the application of artificial intelligence and machine learning techniques to IT operations. The primary objective of AIOps is to enhance the efficiency and reliability of IT management and monitoring processes. AIOps tools analyze vast amounts of data generated by various IT systems, such as logs, metrics, and events, to identify patterns, anomalies, and potential issues. By leveraging AI and automation, AIOps can predict and prevent incidents, automate routine tasks, and provide actionable insights to IT teams. This results in improved system performance, reduced downtime, and more proactive management of IT environments.

How Does AIOps Work?

AIOps consists of three key main steps: Observe – Engage – Act. AIOps continues to process data to detect new anomalies, and these steps are taken in a continuous cycle. Below you can find a more detailed review of these steps:

Performance Analysis (Observe)

This step consists of two main tasks. The first task is the processing of real-time data from multiple data sources together, including traditional IT monitoring, log events, and more. In this layer, AI algorithms detect all significant issues automatically according to anomalies in the data.

The second task of AIOps analyzes those anomalies and clusters similar ones together. This algorithmic filtering prevents alert fatigue and reduces the workload of IT operation teams as they don’t have to do the same work again for similar situations.

Experience Management (Engage)

AIOps notifies the related IT teams about the anomalies. These teams will be aware of performance issues beforehand and understand the bottlenecks of their applications. Since similar problems are classified together, AIOps tools reduce alert fatigue.

Delivery Automation (Act)

AIOps also increases automation level by routing workflows with or without human intervention. It becomes more accurate as it continuously learns from IT operations team’s actions. It can potentially resolve issues before they reach end-users or even before businesses are aware of them.

In a case study by BMC software, Transamerica, an insurance company, has saved more than 9,000 hours of its employees’ time to enable them to work on more strategic activities. The same study also indicates that the event-driven automation function of AIOps tools have reduced the load on the level-2 staff.

What are the types of data sources that an AIOps platform can ingest?

An AIOps platform can apply various data collection methods and data analytics to aggregate data from disparate data sources within an IT environment. This way, it can provide critical information about the performance, health, and security of the infrastructure and applications. Here are some common types of data sources that an AIOps platform can ingest:

System Logs: AIOps can ingest logs generated by servers, applications, and network devices. These logs provide valuable insights into system events, errors, and activities.
Application performance data: AIOps platforms collect data on application performance metrics, including response times, latency, throughput, and error rates. This data helps in monitoring and optimizing application performance.
Infrastructure metrics: AIOps can ingest infrastructure metrics from servers, switches, routers, and other network components. Metrics such as CPU utilization, memory usage, disk I/O, and network bandwidth provide visibility into resource utilization and health.
Network traffic data: AIOps can analyze network traffic data, including flows, packets, and connections. This data helps in detecting anomalies, identifying security threats, and optimizing network performance.
Event data and Alerts: AIOps platforms can ingest events and alerts from various monitoring tools, notifying IT teams about system events and potential issues.
Configuration data: AIOps may collect configuration data from devices and applications to understand the setup and dependencies in the IT environment.
User interactions: Some AIOps platforms can analyze user interactions with applications and services to identify user experience issues and patterns.
Security logs: AIOps can ingest security logs from firewalls, intrusion detection systems (IDS), and other security devices to detect and respond to security threats.
Service desk data: AIOps may integrate with service desk tools, ingesting incident and change management data to analyze IT service performance and incident resolution times.
Cloud infrastructure data: AIOps can collect data from cloud service providers, including AWS, Azure, and Google Cloud, to monitor and optimize cloud infrastructure.
Internet of Things (IoT) data: AIOps platforms may ingest data from IoT devices to monitor and manage IoT ecosystems, providing insights into device health and performance.
Business metrics: In some cases, AIOps platforms may ingest business-related metrics to understand the impact of IT operations on business performance.

The graph shows the increasing popularity for AIOps on Google search over the 5 years period. — **Figure 2:** Google Trends for AIOps

With the rise of machine learning algorithms, AI algorithms can perform manual tasks with less errors, faster, cheaper, and at scale. While IT operations teams have a hard time to fulfill particular challenges like processing increasing amounts of big data, or finding root-cause identification, AIOps will handle these challenges by addressing the speed, scale, and complexity challenges of digital transformation. In deed, the popularity among AIOps as a term has been increasing over the last 5 years period (See Figure 2).

Here are the reasons why businesses need AIOps tools:

1.) More performance data to analyze

Performance monitoring generates increasing amounts of data with the introduction of IoT devices, APIs, mobile applications, and digital or machine users into businesses. Splunk, an AIOps vendor, indicates that 73% of data remains unused by ITOps teams. While the amount of data multiplies, AIOps can solve this issue by processing the data automatically, as manual data analysis often can’t be easily performed.

By leveraging this unused data, AIOps can provide a better understanding of an incident’s impact. For example, if an ERP system is down, AIOps can put this in priority owing to the machine learning algorithms. This method will be much more useful than relying on employee feedback, which may also be subjective.

2.) Shorter Response Time Expectations

User expectations are increasing as B2C apps become more responsive. Thus, companies need to detect and respond to problems immediately and shorten their mean time to resolution (MTTR).

3.) More Complex Structures

ITOps teams take responsibility for the overall health of the IT ecosystem and the interaction between applications, services, and infrastructure. They need to support their insights with tangible evidence. As digital businesses are getting more sophisticated, understanding situations in IT systems becomes more challenging. However, AIOps can provide insights by analyzing data and running root-cause analysis.

4.) Dynamic Environments

Traditional ITOps technologies require human intervention for dynamic environments because any changes will require adjustments to the infrastructure. As new technologies emerge, more tools will necessitate integration with ITOps tools. These integrations can be automatically completed by AIOps tools.

5.) Reducing Monitoring Noise

IT operations tools need to deal with thousands of events called monitoring noise from across the IT estate, both on-premise and in the cloud. According to a Forbes article, AIOps can reduce monitoring noise by 99% and helps businesses focus on the main issue. AIOps leverages technologies like vent correlation, pattern recognition, and anomaly detection to present only the critical few alerts that need to be addressed.

What are common AIOps use cases?

AIOps tools are primarily used for IT and operations management, including monitoring and IT infrastructure observation. Compared to traditional tools, they can automate IT operations, improve the overall efficiency and decrease error rates. Here are the main applications of AIOps:

Performance Monitoring

Proactive performance monitoring in real-time: AIOps connects tracking insights to business outcomes by collecting the application performance data continuously in real-time.

Handling Performance Issues

Intelligent alerting: AIOps filters and correlates the meaningful data into incidents to reduce alert fatigue. It also helps with prioritization based on user and business impact. For example, a failure in system X triggers an alert, impacting system Y, which also triggers an alert, and so on. AIOps prioritizes the alarm from system A to prevent the alarm from system B and inhibit the domino effect.
Automated root-cause analysis: Once a problem is detected, AIOps presents the top suspected causes and evidence of the problem. Providing evidence helps to build trust between AI tools and humans. Humans can also give feedback enabling the AI engine to learn from human expertise.
Automated recovery: AIOps can identify problems from the historical data from past issues and automate the fixing process to solve these problems rapidly.
Reduced Mean Time to Repair (MTTR): AIOps rapidly solves problems, including outages. Compared to manual processes, it reduces MTTR and costs caused by performance issues.

Business Analytics

Cohort analysis: AIOps can handle increasing amounts of data, run thousands of instances, and identify outliers in configuration to conduct cohort analysis in businesses.
Providing a better understanding: AIOps creates causalities from the data collected. It gives IT teams an overview of what is going on and demonstrates a better understanding of the situation.
Better decision making: AIOps provides insights from performance metrics to IT professionals for better decision-making.

Who Is Using AIOps?

Here are some examples of who is using AIOps and for what purposes:

IT operations teams: IT operations teams in enterprises and large organizations use AIOps to monitor and manage their complex IT infrastructures. AIOps helps these teams with real-time monitoring, incident detection, root cause analysis, and automated remediation, enabling them to maintain service availability and performance.
Cloud service providers: Cloud service providers leverage AIOps to monitor their vast and distributed cloud environments efficiently. AIOps can facilitate cloud management andby optimizing resource allocation, ensuring service reliability, and managing the scalability.
Managed service providers (MSPs): MSPs use AIOps to efficiently manage multiple clients’ IT environments. AIOps assists in automating routine tasks, reducing response times, and delivering better service levels to their customers.
DevOps teams: DevOps teams integrate AIOps into their continuous integration and continuous deployment (CI/CD) pipelines to gain insights into application performance and identify potential issues early in the software development lifecycle.
Security operations centers (SOCs): SOCs employ AIOps to detect and respond to security threats more effectively. AIOps can analyze security logs, network traffic, and other data sources to identify suspicious activities and potential breaches.
Financial institutions: Banks and financial institutions utilize AIOps to monitor critical financial systems, detect anomalies in transactions, and ensure compliance with security and regulatory requirements.
Telecommunications providers: Telecommunications companies use AIOps to monitor their network infrastructure, optimize network performance, and deliver better quality of service to their customers.
E-commerce and retail: Online retailers leverage AIOps to monitor their e-commerce platforms, detect performance issues, and provide a seamless shopping experience for customers.
Healthcare: Healthcare organizations implement AIOps to manage their complex IT systems, ensuring the availability and reliability of electronic health records and critical medical applications.
Internet of Things (IoT) providers: Companies in the IoT space utilize AIOps to monitor and manage the vast amount of data generated by IoT devices, enabling predictive maintenance and optimizing IoT ecosystem performance.
Energy and utilities: AIOps helps energy and utility companies monitor and optimize their power generation and distribution infrastructure, reducing downtime and ensuring energy reliability.

Who are leading vendors?

AIOps vendors provide a wide range of services that continues to grow with advancements in AI. For example, AIOps solutions can offer application performance monitoring, predictive analytics, prediction, and forecasting, event management and analytics, clustering, adaptive and statistical thresholding, anomaly detection, root cause determination for businesses.

While AIOps is a trending solution, vendors differ in their data ingest and out-of-the-box use cases made available with minimal configuration.

Check out our comprehensive and objective vendor benchmarking for AIOps solutions to learn how to identify your ideal AIOps platform.