While AIOps enables enterprises to efficiently manage their IT operations, implementing it is still a major hurdle. For CIOs, it means that they should give it their 100% attention to overcome the AIOps challenges.
Since the emergence of AIOps, IT operations have been excited to implement it into their infrastructure. From decreasing the false positives to identifying issues before they occur using machine learning and much more, AIOps enables IT leaders to get a holistic view of their enterprise.
As per ZK Research and Masergy’s “2021 State of AIOps Study” survey, 65% of the enterprises have incorporated AIOps to help with their IT operations. Additionally, 94% of the respondents stated that AIOps is “important or very important” for managing network and cloud application performance. Furthermore, 84% view AIOps as a path to a fully automated network environment.
While AIOps is still new, it is already providing enterprises the bang for their bucks. In fact, as per “AI(work)Ops 2021: The State of AIOps” from Enterprise Management Associates (EMA), 62% of organizations view AIOps as “very high” or “high ROI from their AIOps investments”. However, the integration of AIOps technology in the infrastructure is not straightforward and often does not go as intended.
The EMA study highlighted that over 50% of the respondents agreed that AIOps was “challenging” or “very difficult” to implement. The issues include, data quality, cost, lack of skills and much more.
Here are five AIOps challenges that require the immediate attention of CIOs:
Not having a clear strategy before adoption
As per industry experts, today’s organizations are time and resource-constrained. Therefore, they are more likely to make decisions without having the broader picture of their implementation, which serves their short-term goals but fails to translate to long-term overtime.
This forces enterprises to make drastic switches to platforms that not only cost a significant monetary investment but a large proportion of time to complete. To achieve success from their AIOps projects, IT leaders need to ensure that their AIOps projects are thorough and planned in advance. They should first define what challenges they are trying to address and understand how it will impact their overall business. This will allow them to confirm that conventional solutions will not be appropriate or effective.
Poor data quality
The second biggest challenge to successfully implementing AIOps solutions is associated with data. Since AI and ML feed and train on the available data, having a poor quality of data or incomplete data will not yield the desired results.
Today the data that is available is not necessarily in the format that is needed for a good training data set. Therefore, before proceeding with the deployment of their AIOps services, CIOs should ensure it has proper data to feed on. Additionally, CIOs should continually monitor the insights obtained from the AIOps technologies to understand what information they should collect. This will help them to improve accuracy and precision as they begin to generate data sets that will fit their purpose.
● Hefty expense
Another hurdle in the way of CIOs is asking individuals or departments within the enterprise who have their own preferred toolset that they are not willing to abandon. It can be a political nightmare for CIOs to get rid of the monitoring solutions. Therefore, many organizations, as a compromise, keep their existing system while adding an AIOps platform on top of it. As novel as the approach may seem, it creates duplication of functionality and increases integration challenges along with additional expenses.
To tackle this situation, CIOs can opt for AIOps that are built into domain-specific systems. However, while it makes it easy to extract some AIOps features, it is still at the expense of a multidomain, multi-cloud view of operations.