AIOps combines machine learning and automation to transform IT operations.

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IT operations has become the lifeblood of all businesses today. A healthy IT organization can provide key competitive advantages for businesses in a fast-paced market. Many companies struggle to meet the high demand due to increased cloud system complexity. Distributed apps (where different parts of an app run on different systems) make it difficult to track where problems occur during an IT incident. Every minute of downtime or app failure directly impacts revenues. To mitigate these failures, IT organizations have ballooned in size. Increased cloud investments demand people that can do everything: build efficient systems, scale them to millions of users, and plug holes that lead to downtime. If businesses continue on this current trajectory, they will buckle under the burden of managing increasing complexity in IT.

Recently, a new report by Gartner suggests a new practice to reduce this burden of IT management, dubbed AIOps. AIOps translates to “Algorithmic IT Operations,” asserting that algorithms, not humans or traditional statistics, will help to make smarter IT decisions and help ensure application efficiency. AIOps shifts the burden of IT management from DevOps engineers to platforms that leverage machine learning (ML) and automation. DevOps engineers have become spread too thin. To scale themselves, they rely on a plethora of tools to report cloud system health. These tools need constant nurturing in the form of tuning alerts often, rewriting scripts, and taking time away from real work. AIOps platforms reduce the need for these tasks by using ML to set alerts and automation to resolve issues. In the long term AIOps platforms can learn patterns of behavior within distributed cloud systems, suggesting which metrics are related to one another. These relationships can be mapped against systems of record, such as log files, providing a deeper understanding of the health of cloud environments, apps or services.

AIOps practices save companies time and money. ITOps teams can spend time building scalable systems, rather than chasing down noisy alerts and doing redundant tasks. Cloud systems gain efficiency thanks to reduced app downtime. AIOps platforms predict potential IT incidents and resolve them without human intervention.

AIOps provides this capability thanks to ML algorithms learning from data within monitoring systems. AIOps platforms use ML algorithms to learn the patterns of behavior on each machine metric, like CPU Use or the amount of bytes written to disk. If these algorithms find abnormal activity, they trigger actions using automation tools IT organizations use to do initial troubleshooting or fix basic problems.

The ability to adopt AIOps depends not only on the availability of monitoring data and automation systems, but also the alignment of people and processes. Some AIOps platforms provide capabilities to make it so companies can integrate their cloud systems in a matter of days. Like any transformation effort, aligning people and processes can take more time, depending on business commitment. One area where AIOps can provide measurable value lies within an IT service management (ITSM) strategy. Most companies have some level of incident management, but a robust practice can expose key metrics used measure an AIOps success. These include mean time to ticket resolution (MTTR) and number of incidents created. Both measurements tie back to customer satisfaction, further underscoring the value of AIOps.

The rise of ML paves the way for improved operations across all areas for a business, and AIOps solves a critical problem all businesses face today. While most ML projects can take years to see value, AIOps platforms can provide excellent ROI for internal operations with minimal effort. In turn, this investment can lead to lasting value and customer advocacy in the long term.

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