Artificial intelligence for IT operations (AIOps) is an emerging technology focused on the use of AI and machine learning solutions to improve IT operations teams’ response and remediation abilities, in turn reducing the time and money spent identifying and correcting IT issues.
With technical complexity increasing as digital transformations take center stage, the potential of AIOps becomes critical to avoid downtime and empower effective data analysis. Downtime is costly: A recent two-day outage caused stock prices to plunge 2.3 percent for one major financial institution. And according to BetaNews, data professionals spend more than half of their time at work searching for data or rebuilding information assets that already exist.
AIOps offers the promise of automated, agile and intelligent responses to emerging IT issues. This guide will tackle the big questions surrounding this solution: What is AIOps? How is it sold? What are the benefits? How do organizations effectively deploy it at scale?
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What is AIOps?
Gartner, which coined the term, defines AIOps as a combination of Big Data and machine learning functionalities that “support all primary IT operations functions through the scalable ingestion and analysis of the ever-increasing volume, variety and velocity of data generated by IT.”
Murali Nemani, chief marketing officer of AIOps platform provider ScienceLogic describes the concept more simply as “the use of intelligent machine learning elements in a way that allows businesses to deliver great digital experiences.”
However defined, IT operations automation is making inroads with organizations. According to Forrester, more than half of all businesses surveyed are now deploying what it calls Intelligent Application and Service Monitoring. Recent data from AIOps Exchange suggests a slightly higher number, with 68 percent of respondents actively working on AIOps projects.
In practice, AIOps attempts to bridge the divide between IT operations teams and digital innovation. By leveraging artificial intelligence to establish key relationships between individual application stacks and the network at large, the tools let IT teams quickly pinpoint problems or identify changes, allowing them to proactively identify and remediate issues. While Nemani notes that AIOps is “a little bit ahead of itself in terms of using ‘true’ AI,” ScienceLogic predicts significant increases to corporate adoption within the next four years as intelligence and learning tools mature.
How Are AIOps Tools Sold by Vendors?
AIOps offerings are typically sold as platforms designed to provide continuous insight for IT operations teams. For example, ScienceLogic’s platform empowers organizations to see data on demand, contextualize the relationships between data sets and applications and then act on this information to quickly resolve issues or make critical changes.
For Nemani, AIOps “isn’t a product or solution but a framework that helps companies move from human to machine-powered worlds.” Vendors such as Splunk and BMC also offer unique approaches to the AIOps platform, focusing on reduced complexity and the concept of the “Data-to-Everything” platform, respectively.
Some vendors do sell AIOps solutions as standalone products or services, but given the focus of AI-driven operations on creating context at scale, individual offerings may negatively impact overall integration.
How Can IT Teams Benefit from AIOps Deployments?
Deploying AIOps offers multiple benefits for IT teams, including:
- Improved mean time to recovery (MTTR): Recovery time matters. If companies can quickly identify root causes of problems and necessary corrections, the result is reduced downtime and cost savings. Nemani points to the case of one company that leveraged ScienceLogic’s platform to reduce its MTTR from four hours to just 15 minutes.
- Enhanced use of human resources: As noted above, data experts spend most of their time searching, sorting and cleaning data for use in machine learning tools. AIOps streamlines this process, allowing companies to make better use of expert human resources.
- Automatic remediation: By automating key processes in the technology stack, AI-driven platforms can quickly detect and remediate common IT problems on their own, in turn allowing operations staff to focus on deploying and integrating new technology solutions.
- Empowered transformation: As noted by Nemani, operations teams “are the custodians of the digital experience.” While developers use multiple platforms and services to deliver digital interaction at speed, operations experts must identify potential issues and remediate them in situ — all without compromising the customer experience. AIOps empowers teams to fully understand interconnected application stacks and drive digital transformation.
What Are Some AIOps Use Cases?
Two common use cases for AIOps include the transformation of data swamps into data lakes and the creation of context at scale.
Data swamps are the natural result of Big Data. The sheer velocity of information creation, storage and analysis creates what Nemani terms “noise,”and finding a signal requires the laborious application of machine learning tools and frameworks. AIOps helps dredge these swamps to create cleaner data lakes by correlating key attributes across interconnected stacks.
Context, meanwhile, relies on topology and dependency mapping: How are seemingly disparate data sets connected, and what happens when changes are applied? Nemani notes that in a digital-first world, persistent physical environments are rare. Some clients spin up workloads for less than two minutes; while fleeting, such workloads still impact IT environments at large. AIOps allows operations teams to contextually map data, empowering them to understand both what’s happening and why.
What Are the Recommended AIOps Deployment Strategies?
According to Nemani, there are two common AIOps deployment strategies: Top-down and bottom-up.
Top-down deployments are mandated by CIOs or CEOs who recognize the need for AI-supported outcomes. These deployments typically move quickly and include a general framework for AIOps adoption. Bottom-up deployments are driven by the need to solve specific problems, such as reducing MTTR, integrating algorithmic analysis or automating key IT tasks.
No matter which strategy companies choose, success with AIOps starts with self-awareness: Is data siloed? What analytics (if any) are taking place? Where does automation fit? Next, it’s critical to identify best-fit platforms or solutions that align with key operational goals and can help deliver critical outcomes.
Finally, Nemani makes it clear that there’s no need to be intimidated by AIOps because there’s no singular end state. Instead, artificial intelligence in operations is a continual progression that depends on evolving connections and emerging context to reduce complexity and empower innovation.