Enterprise AIOps is quietly becoming a reality

Buzz in the IT industry around AIOps has weakened sharply over the past three years. However, amid waning satisfaction, the real use of IT automation based on machine learning algorithms has emerged in businesses.

AIOps-or AI for IT Operations-refers to the use of machine learning algorithms to automate routine IT tasks. This may include filtering IT monitoring alerts, responding to incidents or handling the so-called “undifferentiated heavy lifting” required to perform regular maintenance on infrastructure systems.

The term appeared in the mainstream in 2018, and in 2019, AIOps became a common buzzword in the industry, prompting several mergers and acquisitions among IT vendors and among them, a lot of speculation about a very automate, AI-driven future for computing. Some AIOps vendors, such as Dynatrace, have publicly accepted the concept of “NoOps,” envisioning a world of complete self-healing, self-management systems that will completely eliminate the need for human intervention.

Then the COVID-19 pandemic hit. IT spending plans have changed, and digital transformation has gone from a long -term goal to an immediate need. Sweeping futuristic ideas like “NoOps” has never gotten the same attention.

However, amid this turmoil, the expanding adoption of cloud computing and cloud-native infrastructure has brought new IT observation tools and an excess of IT tracking data, which in turn has fed IT algorithms. AIOps machine learning and helped them be more effective. The pandemic also tightened IT budgets as cloud migration increased the complexity of systems, and IT teams turned to automation tools to compensate for staff shortages.

Obviously there are some areas where machine statistical analysis can be extremely important, in a very targeted way.

Arun ChandrasekaranAnalyst, Gartner

“Some of the newer applications are written in a fundamentally different way, where it’s not enough if you track it the way you track things before,” says Arun Chandrasekaran, an analyst at Gartner. “Another trend, I would say, is a change from [reactive monitoring] in more real-time and predictive [tools]. “

As a result, while most enterprise IT shops have not yet come close to “NoOps,” AIOps is gradually becoming an everyday reality.

“The ability to collect more metrics and more machine data is growing, and the ability to process this data at scale is growing, thanks to time series databases and open source parallel processing engines,” he said. Chandrasekaran. “There are clearly some areas where statistical machine learning can be extremely important, in a very targeted way.”

Accenture AIOps discusses routines amid pushing for data quality

Accenture, a multi-national IT professional services and consulting company, is among the businesses where AIOps has begun handling over the past two years. Accenture has deployed IT service management (ITSM) and IT operations management (ITOM) software to its business partners, which use machine learning algorithms to link IT monitoring alerts and reduce as appears to IT pros, a major use case for AIOps.

Within Accenture, these tools also automate some routine remediation tasks, which for some IT ops teams has freed up time used to respond to small incidents.

“It’s a huge storage cleanup, every time rogue logging tools are just starting to fill up disks,” said Bryan Locke, global IT operations management lead at Accenture. “Many of those have to do with disk cleanup scripts pre-configured by our operating system standards team-our orchestration platform can run them on either our servers or the environment we manage , and if the problem is reduced, suppress incident alerts. “

It’s a step toward the goal of widely using AIOps to run self -healing systems, but that’s still something Accenture is doing, Locke said. Most of the work that has gone into the AIOps system has become the basis to ensure data quality, such as transferring data from six previously separate ITSM tools and implementing ServiceNow’s Common Service Data Model (CSDM).

CSDM is a standardized data model first introduced by the vendor in 2017 to support the Configuration Management Database at the core of its Now Platform. The model also standardizes data formatting across all ServiceNow products.

Data quality controls have also matured within ServiceNow’s ITOM product in recent years, which, along with conversion to CSDM, will help Accenture ensure that AIOps algorithms are fed with reliable data.

“ServiceNow is gradually adding more, I’ll call them data patrol insights, about how [with policy] and how complete the data sets are, “Locke said.” They have a series of out-of-the-box rules on the platform that we use a lot. “

AIOps use cases
The use of real-world AIOps has grown in businesses in recent years, but IT pros must plan carefully and, with specific goals in mind for it to be effective.

Locke said that once Accenture has a standardized set of ITSM data shared across all of its business lines on the Now platform, he wants to proactively fix more IT incidents. He added that he also wants to automate more routine IT tasks, including DevOps deployments.

“Where we want to go is to get artifacts released within Azure DevOps automatically approved, rather than manual approvals or semi-manual approvals,” Locke said. “But that was a gradual shift.”

Accenture is not alone in making the relatively long journey toward AIOps-driven extensive auto-remediation; gradually describes the overall growth state of AIOps in enterprise IT, according to Gartner’s 2021 report.

Although AIOps technology has existed for several years, successful deployment requires time and effort, including a structured end user roadmap, “according to the report, Market Guide for AIOps Platforms, published in April 2021. “Implements typically have a lot of problems, including data ingestion, that provide context-relevant analysis and a long time to value.”

Gradually or not, however, Gartner expects AIOps growth to remain stable at a compound annual growth rate of 15% through 2025.

There is no future of IT operations that do not include AIOps, ”the report says.“ It is simply impossible for people to make sense of the thousands of events per second generated by their IT systems.

The acquisition of Atlassian strengthens the AI, DevOps tie-in

AIOps has also begun to play a more prominent role in DevOps toolchains, especially with the growing popularity of soup-to-nuts DevOps platforms coming from major IT vendors. Among such vendors, Atlassian has expanded the AI-based feature for its Jira Service Management ITSM tool over the past three years to include predictive issue assignment and triage, AI-driven IT automation and personalized search results for individual users. This month it got Percept.AI to add to that mix, which automates tier-1 service desk tasks.

Tier 1 incident resolution is a separate part of IT management from AIOps, but this move signifies a deepening of dedication to AI automation across the IT stack, says Forrester Research analyst Will McKeon- White.

“AIOps is very focused on signal-driven resolution and Tier 1 is often more human-driven,” McKeon-White said. “AIOs and automated resolutions have a bit of a weird relationship, [but] it allows more people to trust those directions. “

The acquisition of Atlassian also reflects that AI-driven automation is becoming a more vendor-dominated market, as businesses often struggle with do-it-yourself approaches, says Chandrasekaran of Gartner.

“Success with AIOps depends largely on having the right use case as well as on having the right data and implementation,” he said. “This is one of the reasons why DIY efforts have been less successful and there has been a step to consume these capabilities from commercial vendors.”

Beth Pariseau, senior news writer at TechTarget, is an award-winning veteran of IT journalism. He will be reached at [email protected] or on Twitter @PariseauTT.

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