What Is AIOps? Meaning, Examples, and Use Cases

Artificial Intelligence for IT Operations (AIOps) pairs advanced analytics with IT operations. Businesses have become more reliant on digital technologies, resulting in more complex digital problems and an increased need for IT professionals prepared to deal with them using such modern techniques as AI and machine learning. 

In this article, you’ll learn more about AIOps, how organizations use them, and the benefits they provide IT professionals and businesses. 

What is AIOps? 

Artificial Intelligence for IT Operations, or AIOps, joins advanced analytics with IT operations. Businesses have become more reliant on digital technologies in recent years. As a result, organizations experience more complex digital problems and an increased need for IT professionals prepared to deal with them using such modern techniques as AI and machine learning.

Why AIOps is important

At its core, AIOps is all about leveraging advanced analytics tools like Artificial Intelligence (AI) and machine learning (ML) to automate IT tasks quickly and efficiently. Rather than replacing workers, IT professionals use AIOps to manage, track, and troubleshoot the complex issues associated with digital platforms and tools.

AIOps also provides a way for IT professionals to parse through the vast amounts of data produced by businesses’ many digital platforms. It allows them to resolve problems quickly and (in some cases) design solutions before they even arise.

According to a survey conducted by McKinsey & Company, businesses “accelerated the digitization of their customer and supply-chain interactions and of their internal operations by three to four years” during the COVID-19 pandemic [1].

The takeaway: Modern businesses rely on digital technologies to run virtually every aspect of their operations.

AIOps use cases 

Artificial Intelligience can help IT professionals streamline many operation processes. Common ways that AIOps solutions are used day-day include: 

• Anomaly detection. AIOps work to detect anomalies without human intervention and flag them for relevant personnel. 

• Root cause analysis. AI-powered root cause analysis traces back information from processing to identify the reason a problem occurred. 

• Event correlation. Machine learning models efficiently scan large volumes of data and detect the most important events within them for predictive insights. 

• Automated remediation. Some problems are solved by intelligent automation systems that troubleshoot issues without human intervention 

• Performance modelling. AI is used to model performance and design potential solutions. 

• Cohort analysis. User data is analyzed to understand better when errors occur, why and how they can be fixed for improved performance. 

AIOps benefits

AIOps brings the power of AI and machine learning to the IT domain, providing real-time performance monitoring, continuous insights,  and a faster time to resolution.  Artificial intelligence for IT operations enables IT professionals to improve operations through descriptive, diagnostic, prescriptive, behavioral, and predictive analytics.

Additional benefits that businesses and IT professionals can expect include:

• Lower operational costs and increased ROI for IT solutions 

• More efficient remediation due to intelligent monitoring tools 

• Improved customer experience online resulting from improved digital systems 

• Swifter service management due to AI capable of managing high-volume data sources 

• Predictive modelling capable of identifying problems before they arise and assisting in the design of solutions to stop them from occurring 

AIOps example: minimizing alert fatigue

As workplaces become more reliant on interdependent digital platforms connecting one department to another, the likelihood of a critical technical failure like system shutdown increases. As a result, IT operations management must maintain a real-time view of how digital technologies function within a business. This necessity can result in constant notifications. A high volume of alerts can conceal the most important problems within a wave of routine reports.

To highlight only the most important notifications, AIOps can be used to monitor notifications and only flag the most important issues to IT operations teams, ensuring that the most pressing problems are resolved swiftly.

AIOps tools and platforms 

Many AIOps platforms, tools, and business services are available to organizations today. Some of the most popular platforms include: 

• IBM Instana Observability. IBM Observability is an enterprise application performance monitoring tool with automation capabilities. It can be deployed on-premises or as a SaaS solution.

• Cisco AppDynamics. AppDynamics is a full-stack app performance management tool. It uses analytics to measure application performance with key business metrics for deeper insights.

• Datadog. Datadog specializes in observability for cloud-scale applications. It also offers AI capabilities and cross-team collaboration tools.

Keep learning about AIOps with Coursera

Prepare for your future in AIOps by taking an online, self-paced course through Coursera today from an industry leader such as Google. With Google's IT Support Professional Certificate on Coursera, you'll learn IT skills like Cloud computing, encryption algorithms and techniques, and network protocols. You can also learn more about AI fundamentals with visionary Andrew Ng’s Machine Learning Specialization.