The demand for AIOps has accelerated as organizations struggle with the increased complexities of IT systems, a disparate workforce, and the explosive growth of operational data. Gartner even recently stated that "there is no future of IT operations that does not include AIOps." For the modern enterprise, relying solely on traditional analysis or humans results in missed opportunities and potentially increases risk.
So, what is AIOps?
It's about leveraging intelligent technologies, including AI and ML, to automate an organization's operations to provide a real-time understanding of issues in order to improve the reliability and quality of services. In addition, once adopted, it minimizes the time spent firefighting.
Another key benefit of AIOps is that it removes the barriers and wasted cost of siloed IT operations and provides enterprises with a platform to increase agility incrementally.
By using insights from uncorrelated data across systems, organizations can predict and fix operational problems before they occur. As a result, enterprises can reduce the time spent resolving these problems along with minimizing the impact. In addition, the intelligent technologies provide data-driven insights that help inform better decision making and improve the quality of services. By utilizing the power of AI and ML, this ultimately becomes fully automated and should prevent major outages from occurring.
Starting Your DevOps Journey
When it comes to starting your AIOps journey, it's best to focus on visibility. First, organizations should use the insights to hone in on critical operational indicators such as reliability, quality, mean time to resolution, and identify the major stumbling blocks.
Then IT teams should plan changes around those vital areas with additional, more proactive, AIOps capabilities while continually using the data generated to demonstrate progress and validate the shift to AIOps.
One of the advantages of AIOps adoption is that you can do it incrementally. With this approach, the insights initially uncovered can highlight otherwise unknown problems without disruption. Then augment the visibility aspects with more automation and more powerful analytics across a more comprehensive set of data sources. This allows the enterprise to become familiar with AIOps while proving its value before rolling it out further.
At the same time, there are some common pitfalls that enterprises need to be cognizant of. For example, the common problem of inertia with IT teams also applies to AIOps. Existing IT teams are overstretched and lack the time to investigate new technologies and initiatives, especially ones with more advanced AI and ML capabilities. However, given the automation and efficiency benefits of AIOps, this incremental migration should be seen as a good investment for a much-improved result.
As Abraham Lincoln put it, "give me six hours to chop down a tree and I will spend the first four sharpening the axe."
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