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Almost overnight, the pandemic exposed many of the weaknesses of IT departments in the modern business environment.
CIOs and their teams were left scrambling to keep operations up and running with a socially distanced skeleton crew, while others faced significant security concerns due to the sudden shift to working remotely.
A fully remote workforce triggered an explosion in the amount of data being produced by corporate systems, overwhelming IT departments already struggling to adapt to new work-from-home routines alongside their existing applications and monitoring systems. The data produced by an organization has not only risen in volume, but in variety as well.
As these new working conditions began overwhelming organizations around the world, a new field of computing was accelerated to help mitigate the strain on CIOs and IT departments: AIOps.
AIOps uses AI to automate laborious tasks like problem resolution or service outages. Put simply, it uses the power of AI to automate the heavy lifting of IT operations.
What Is AIOps?
In a survey of 1,300 IT professionals last year, 42 percent of respondents had either never heard of AIOps or had thought that applying machine learning to IT operations was “not a thing.”
At its core, AIOps uses machine learning and natural language understanding to correlate data and utilize it to provide automated recommendations far faster than could be done manually. In some cases, AIOps can identify the root cause of an IT incident and even automate the solution before a service outage occurs. This frees up operations team members to focus on higher-value work, lessening the time spent on labor-intensive tasks that don’t serve the end goals of the business.
We leveraged decades of research and development in AI to bring AIOps to market last year, a product that can uncover hidden insights for clients seamlessly integrating into their IT processes.
What Can AIOps Do?
AIOps uses data ingestion methods, machine learning, and natural language processing to find trends and relationships within your structured and unstructured data. These trends and relationships are then utilized as a baseline for identifying any abnormal events or anomalies that occur that could have a negative impact on your cloud service or application.
For example, application and infrastructure logs and performance metrics can be parsed and processed to detect anomalies before they occur. Anomalies are then raised and are grouped within events and alerts to reduce noise and churn for the engineer. With AIOps, instead of having to navigate through a dozen tools to understand a potential issue and the impact of that issue, engineers can quickly grasp and triage complex situations, teeing them up to act more decisively.
From there, prior similar incidents are identified to engineers, with next-best actions derived and recommended by AI models, further accelerating the resolution process. We’ve been working with customers like Austrian Rail (ÖBB) and CaixaBank to streamline their existing IT processes with AIOps.
What Is the Future of AIOps?
As we emerge from the pandemic, AIOps will continue to play a critical role, if not an even greater role, within the digital infrastructures of businesses that have adapted. The widespread organizational transformations we saw last year highlight the benefits of a flexible IT infrastructure and the value of automating lower-level activities to focus on mission-critical tasks.
We can expect that AI will be applied to more IT processes such as service cost management and compliance. AIOps will be able to automate and expedite more and more day-to-day tasks, as a trusted advisor for IT.
As the complexity of modern IT environments grows, and the use of AI technologies expands, enterprises will increasingly look to use AI to transform how they develop, deploy, and operate. With the capabilities of AIOps, businesses can finally manage their IT operations in a way that scales with their growing needs.