Demystifying AIOps for IT practitioners
What is AIOps?
If your organization is looking to improve its IT service management (ITSM) and/or IT Operations Management (ITOM) capabilities, then it’s probably considering Artificial Intelligence for IT Operations, which is commonly called “AIOps.” But what is AIOps, and how will it help your IT organization’s IT management capabilities and, ultimately, business operations and outcomes?
Let’s start with an AIOps definition.
AIOps definition
There are many AIOps definitions out there, for example:
“AIOps is the use of machine learning and big data capabilities to automate various IT operations processes.”
But what does this mean in terms of features and capabilities?
AIOps features
There are many potential AIOps capabilities or use cases, including:
- Alert correlation
- Alert escalation
- Auto-remediation
- Capacity optimization
- Intelligent alerting
- Root-cause analysis.
Some of these are covered in more detail later, but one of the key attributes of AIOps solutions is that they use machine learning to filter out the unwanted monitoring “noise” in event streams. This filtering helps to avoid IT operations staff becoming overloaded with meaningless alerts.
Another is the intelligence that machine learning brings to understanding the data that matters. This includes understanding the data in context, for example, when issues happen at certain times or suggesting new alert rules based on historical data patterns.
Plus, when employed well, AIOps solutions will detect, diagnose, and resolve issues before end-users even know there’s an issue. Thus, they lessen the impact of IT issues and remove the need for end-users to contact the IT service desk for help.
Ultimately, AIOps solutions allow IT organizations to see and do much more than they can with humans and traditional monitoring tools alone. Consequently, they better deliver against the required business’ technology enablement needs.
Why organizations need AIOps
The latest report by Constellation Research on A CIO’s guide to AIOps, predicts the growth in the AIOps field to range anywhere from $9.4 billion by 2026 to a whopping $645 billion by 2030—with an annual estimated compound annual growth rate (CAGR) of 20% to 30% in the next five to 10 years.
As with most business changes, the key drivers for AIOps relate to the triumvirate of “better, faster, and cheaper” operations and outcomes. But the complexity of modern IT environments is a driver, too – significantly increasing the alert noise level for IT operations.
However, this is the technology-provider view of AIOps. Instead, we should look at AIOps solutions through a business lens such that their introduction and exploitation link to one or more business-related business strategies, goals, challenges, or opportunities, such as:
- Increasing revenue, margins, and profit
- Reducing costs
- Improving the service experience
- Delivering against the increased expectations of IT
- The need to scale-up operations
- Speeding up change and innovation
- Addressing people-related limitations
- Meeting governance, risk, and compliance needs and legal or regulatory requirements
- Getting greater insight into IT and business operations and issues in real-time.
How AIOps capabilities help your IT operations and business outcomes
AI, or machine learning, in particular, has transformed traditional ITOM capabilities. For example, in terms of:
- Intelligent or predictive alerting – where machine learning uses historical data to understand the future, i.e. a given set of alerts or infrastructure attributes is a sign that something will eventually fail or perform unexpectedly. Corrective actions can thus be taken before an issue adversely impacts business operations.
- Event prioritization – where AIOps solutions learn from your organization’s incident and event data to understand the likely business impact of a predicted or actual event, i.e. which IT or business services are affected, how badly, and what it means to business operations and outcomes. Such that events are prioritized in real-time based on their business context, and people can focus on what matters most.
- Root-cause analysis – where AIOps uses event patterns and service topologies to identify the root causes of service issues.
- Auto-remediation – where the AIOps solution understands there’s an issue that needs fixing and what the required fix is. This fix is applied automatically, either with or without human authorization, using native tool capabilities or third-party tools via orchestration.
- Capacity and cost optimization – where AIOps solutions understand how business demand for IT services, and thus the IT infrastructure that supports them, changes over time. For instance, by recognizing seasonal peaks and troughs. As a result, AIOps solutions can automatically scale or shrink the available infrastructure and the associated costs to meet the predicted future demand.
There’s so much offered by AIOps solutions that IT organizations would be remiss not to consider its potential. It uses machine learning algorithms to detect anomalies that would otherwise go unnoticed. The contextualized data can help you build a more robust, next-gen IT operations framework for your business. Imagine marrying on-premise monitoring with the unlimited power of the cloud—this is what AIOps can help you achieve.
