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. 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 systems.

What is AIOps?

Artificial intelligence for IT operations, also known as AIOps or IT operations analytics (ITOA), 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 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.

How Does AIOps Work?

AIOps consists of three 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:

Source: Gartner

Performance Analysis (Observe)

This step consists of two main tasks. The first task is the processing of real-time data from sources, 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 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.

Why is AIOps trending now?

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 data or finding root-cause identification, AIOps will handle these challenges by addressing the speed, scale, and complexity challenges of digital transformation. Here are the reasons why businesses need AIOps tools:

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 analysis 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.

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).

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 running root-cause analysis.

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.

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 applications?

AIOps tools are primarily used for IT operations, 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 are leading vendors?

AIOps vendors provide a wide range of services that continues to grow with advancements in AI. While AIOps is a trending solution, vendors differ in their data ingest and out-of-the-box use cases made available with minimal configuration. Here are two vendors that are specialized in AIOps and can provide related services for your business.

Moogsoft: Moogsoft aims to track the increasing amount of data where manual monitoring isn’t enough. While monitoring your applications’ health, it correlates important alerts and groups them into contextual situations, runs root-cause analyses, and prescribes solutions.

Splunk: By combining AI and ML algorithms, Splunk continuously provides predictive analytics, prediction, and forecasting, event management and analytics, clustering, adaptive and statistical thresholding, anomaly detection, root cause determination for businesses.

Below is a longer list of AIOps vendors:

  • Anodot
  • Big Panda
  • BMC Software
  • Broadcom
  • Corvil
  • Devo
  • Digitate
  • Loom Systems
  • Resolve
  • Sumo Logic
  • Stack State

To learn more about how AI can be integrated to application monitoring, feel free to look at those articles:

If you want to read more about AI, these articles can also interest you:

If you have questions about how AIOps can help your business, don’t hesitate to contact us:

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