Artificial Intelligence for IT Operations (AIOps) is an emerging discipline in the world of IT operations. But what is it, really? What can it do for an IT organization? And most importantly, how do you get started? Here are some of the primary questions that teams may have as they learn how AIOps can fundamentally transform their IT organizations.
AIOps leverages a broad set of technology platform approaches, including machine learning, network science, combinatorial optimization, and other computational approaches, for solving everyday IT operational problems at scale.
AIOps helps enterprise IT teams to move away from siloed IT management activities to a more dynamic environment to prevent service outages, analyze real-time incidents using intelligent alerting and alert correlation, and fix problems using auto-remediation and root-cause analysis.
Enterprises can address a wide variety of IT management activities using AIOps, including intelligent alerting, alert correlation, alert escalation, auto-remediation, root-cause analysis, and capacity optimization.
The AIOps market is experiencing rapid growth with explosive enterprise adoption, accelerated revenue growth and continuous investments from digital and IT operations vendors. While standalone point tools have defined and shaped the AIOps market to date, a number of adjacent vendors are either building or acquiring companies to assemble competitive AIOps portfolios.
Given the rapid innovation in the AIOps market, enterprise IT teams will adopt intelligent correlation tools to cope with never-ending alerts and ensure faster recovery from disruptive IT outages.
Even though AIOps is an emerging discipline, there are a number of vendors who are building solutions to address a variety of use cases. Many of these tools must be continuously tuned and optimized for data sources, while others use native application, network and/or infrastructure monitoring instrumentation to provide a richer, more contextual view of service health and incident remediation workflows. Look for robust integrations and native instrumentation while selecting an AIOps provider.
AIOps helps IT infrastructure teams turn data (like alert streams) into actionable insights and anticipate problems while still delivering compelling end-user experiences at digital scale. In fact, while demands on IT continue to increase, the ability to leverage machine learning insights will be critical to reliable and responsive IT operations.
Here’s how AIOps will help IT teams run and optimize mission-critical enterprise systems at scale:
Boost key metrics for incident management including mean-time-to-detection, mean-time-to-response, mean-time-to-restoration, and incident volume handled within a service window using AIOps. The combination of machine learning and data science techniques in AIOps not only deliver faster incident coordination and response but also reduces the human time spent per alert with advanced analytics and probabilistic root cause analysis.
AIOps offers the ability to consolidate event and incident insights from different IT management tools across on-prem and hybrid, multi-cloud, and cloud native environments. A shared AIOps platform offers centralized visibility, faster impact analysis, and improved collaboration for a diverse set of stakeholders, including application owners, infrastructure teams, and business sponsors.
With greater digital infrastructure delivery in the modern enterprise, it’s only natural that IT operations teams are experiencing exponential data growth.
This rise in IT operational data volume, velocity, and variety have contributed to an exponential increase in event noise. Modern IT environments are constantly generating alerts for incorrect configurations, events, and more.
IT professionals are now drowning in ‘alert storms’ that negatively impact service availability and increase resolution time for IT outages. AIOps platforms will help navigate these alert storms and escalate mission-critical alerts to the proper teams for remediation and uptime restoration.
The Signal in the Noise: The truth on how AIOps is truly impacting business performance.
In order to understand the true impact of AIOps, OpsRamp recently published “The State of AIOps” Report that is based on data from IT practitioners who are currently using machine learning-powered event management. This survey identified the most popular high-impact use cases for AIOps tools, including:
Preventive automation can help reduce the number of incidents and help IT staff work on innovative projects. AIOps has the potential to reduce the overall mean-time-to-resolution through intelligent incident management. AIOps also helps to tame the complexity of modern IT operations with richer and deeper event insights while letting IT teams handle service disruptions before they occur with predictive insights.Learn More
Automation is no longer a choice, but a necessity for IT operations faced with holistic visibility challenges for dynamic root cause analysis. Automation, when applied intelligently, can drive service availability and performance while simultaneously delivering rapid resolution. While not all failures are auto-remediable, AIOps tools can handle a greater percentage of incidents with policy-driven automation.Learn More
IT teams need to quickly figure out the incidents that can derail their business. AIOps tools can help extract the signal from the noise so that IT pros are not overwhelmed by alert floods and can understand what's broken with the right operational insights. Integrations with popular ITSM tools can ensure immediate prioritization and attention from service delivery teams and coordinated response across organizational silos.Learn More
In order to implement AIOps, it is first important to identify the key problems that your enterprise is trying to solve.
Here are five essential steps any organization should undertake before adopting AIOps:
AIOps tools ingest a wide variety of data (logs, metrics, APIs, and text) to analyze historical behavior and predict future IT performance. Most enterprises today use AIOps to handle anomaly detection and root cause analysis. In the future, machine-learning powered insights will help transform IT operations and overall business performance by overcoming the complexities of the day-to-day IT management and free up room for greater enterprise innovation.
What's driving accelerated AIOps adoption over the next five years? Two chief developments call for a new way of doing things:
Stable and predictable data center environments have given way to dynamic infrastructures built on virtual, cloud, and software-defined environments.
Enterprises have also adopted new-age infrastructure workloads like containers, serverless, and smart machines, for fixing technical debt, unleashing agility, and taking advantage of new business opportunities.
“AIOps, the convergence of AI and ITOps, will change the face of infrastructure management. This technology will impact both enterprise data center and cloud infrastructure management.”