Artificial Intelligence for IT Operations (AIOps) is an emerging discipline in the world of IT operations. But what is an AIOps platform? How does AIOps work? What can it do for an IT organization? And most importantly, how do you get started? This AIOps market guide reviews the definition of AIOps and the primary questions that teams may have as they learn how it can fundamentally transform their modern IT organizations.
AIOps stands for Artificial Intelligence for IT Operations. AIOps leverages a broad set of technologies, including machine learning, network science, combinatorial optimization, and other computational approaches, for solving everyday IT operational problems at scale. In simple terms, AIOps collects data from various sources and analyzes that data to give actionable insights to organizations.
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. Traditional AIOps tools are not real-time and require manual intervention, however modern AIOps tools can be integrated with any existing platform. Modern AIOps not only analyzes data but also enables IT to make quick decisions based on actionable insights.
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 AIOps vendors are either building or acquiring companies to assemble competitive AIOps portfolios.
By 2022, Gartner predicts that 40% of large enterprises will adopt AIOps solutions to cope with never-ending alerts and ensure faster recovery from disruptive IT outages.
There are a number of AIOps vendors whose AIOps tools address a variety of use cases. Many of these must be continuously tuned and optimized for data ingestion, 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 solutions help IT infrastructure teams turn data (like alert streams) into actionable insights and anticipate problems while still delivering compelling end-user digital experiences. In fact, as demands on IT continue to increase, the ability to leverage AI as a service will soon be critical to successful operations.
Here’s how AIOps solutions help enterprises run and optimize mission-critical systems:
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 platforms. The combination of machine learning and data science techniques in AIOps not only delivers faster incident coordination and response but also reduces the human time spent per alert with advanced analytics and probabilistic root cause analysis.
AIOps solutions offer the ability to consolidate event and incident insights from different IT management tools across on-prem and hybrid, public, and multi-cloud 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 ITOps teams are experiencing exponential data growth.
This rise in ITOps data volume, velocity, and variety have contributed to an increase in event noise. Modern ITOps 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 solutions, OpsRamp recently published “The State of AIOps” Report that is based on data from AIOps practitioners who are currently using machine learning-powered event management analysis. This survey identified can help to identify the most popular high-impact use cases for quick AIOps, including:
In this new vision for modern IT management called ITOSM, the relationships between infrastructure elements and business services are more transparent. Preventive automation can help to reduce the number of incidents and push IT staff towards innovation. AIOps has the potential to reduce the overall mean-time-to-resolution through intelligent alert and ticket management.Learn More
Automation is no longer a choice, but a necessity for ITOps teams faced with holistic visibility challenges for root cause analysis and the need to move faster with reduced resources. While not all failures are auto-remediable, the increasing adoption of AIOps solutions means that you can handle a greater percentage of incidents with automation.Learn More
Can you quickly figure out the incidents that can derail your business? With so many point tools, alert management can easily get overwhelming. The combination of machine learning and data science techniques in AIOps platforms not only delivers faster incident coordination and response but also reduces the human time spent per alert with advanced analytics and probabilistic root cause analysis. Using AIOps to extract the signal from the noise is one of the primary use cases.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:
While AIOps adoption is gaining steam, there are a few apprehensions which could prevent wider adoption. The accuracy of prediction models (54%), quality of large datasets (52%) for machine learning models and the IT talent (48%) needed for building machine learning algorithms are all key constraints for scaling AIOps solutions.
“AIOps is gearing up to become the next big thing in IT management...When the power of AI is applied to operations, it will redefine the way infrastructure is managed.”
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.
AIOps adoption is also critical for new-age infrastructure workloads like containers, serverless, and smart machines, for fixing the 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.”