AIOps is an emerging technology that, as its name suggests, is the practice of applying artificial intelligence (AI) to IT operations.
It combines the characteristics of AI and machine learning (ML) to dramatically improve IT analytics and deliver both predictive and prescriptive insights throughout an organization’s enterprise ecosystem.
These insights are arrived at by accessing application data and leveraging it to create a baseline learning model. This model is then used to identify real-time anomalies, create actionable alerts, and generally cut down on the noise IT operations routinely need to cut through.
The benefits of AIOps go beyond the traditional measure of cost-reduction and cost-optimization.
For one, it helps IT teams prioritize where they need to spend time—a common concern, especially for IT operations in a large organization.
Two, it reduces mean time to detect (MTTD), mean time to failure (MTTF), and mean time to resolve (MTTR) metrics.
That’s a lot of words (and acronyms), but what it boils down to is when failures or outages occur, the time it takes an organization to solve the issue and get back up and running shrinks, while the time between failures or outages expands.
In addition, many organizations that adopted AIOps have been able to reduce the number of tools they relied upon prior to integrating the technology into their ecosystem. Tools such as application performance monitoring (APM), real user monitoring (RUM), and others.
Given all these benefits, it’s no surprise that a survey from the Enterprise Management Association ranked AIOps as the most successful IT analytics investment. In fact, 81% of those surveyed said the value they received from AIOps exceeded the cost of the technology.
Implementing AIOps
Any adoption of AIOps should begin with identifying a business case, such as how much impact something like downtime or equipment failures has on a particular product.
This impact, which should focus not just on dollars but also on blemishes to your organization’s reputation—can be used for a cost bid of an analysis to determine how much budget you should set aside for the implementation and run rate of an AIOps solution.
Once you’ve decided to go forward with AIOps and have identified a platform suitable for your organization, the next step is to conduct a small use case, which includes a handful of data ingest sources that already exist, then elevating more meaningful tickets and alerts for people who are already monitoring those systems.
As you build upon this initial step, you will quickly begin to see improvements, especially if your organization is already leveraging infrastructure as code or other configuration management tools.
AIOps and AWS
While AIOps plays well with all cloud platforms, AWS has a number of tools already integrated that greatly assist with AIOps adoption—tools that can immediately be used not just by organizations fully in the cloud but those just starting their journey.
Tools like:
- CloudWatch for anomaly detection
- DevOps Guru to improve application availability
- GuardDuty for intelligent threat detection
- SageMaker for building ML models
These and other tools are constantly being improved upon by the AWS team, which means as AIOps continues to mature in its capabilities, it will also continue to have an innovative and advanced playground in which to work.
AIOps is just the beginning
As digital transformation sweeps through virtually every industry vertical, AIOps is only growing in relevance.
That relevance is translating into a fast-growing market. BCC Research has estimated that the global market for AIOps will triple by 2026—from three billion today to more than nine billion. This suggests that, in the future, it will be unimaginable for IT operations to not utilize AIOps in some form or another.
Because of this, the earlier organizations kickoff the AIOps adoption process, the better. So if you’d like to learn more about the technology and how it can benefit your operations, schedule some time with one of our experts today.
