Concerns and Challenges of IT Leaders Considering AIOps Solutions

Digital transformation offers a litany of long-term benefits, but also brings new layers of complexity to the IT infrastructure. IT operations teams are rapidly adopting AIOps solutions to manage these complexities.

There is no doubt that AIOps is here to stay. It promises to help transform the operation of IT infrastructure and the delivery of essential business services, as reported by Optanix.

Gartner confirms the long-term impact of AIOps on IT operations will be transformative. Gartner predicts that AIOps will have a profound impact on driving new revenues and projects that global AI-derived business value will reach nearly $3.9 trillion by 2022.

To realize these AIOps benefits, there are IT operations concerns that need to be addressed and challenges that need to be overcome. To understand these, let’s look at the issues from a capabilities and outcomes perspective.

Capabilities of AIOps Solutions

The capabilities of AIOps solutions are primarily twofold:

1. They help you glean insights from the IT infrastructure using machine learning and artificial intelligence.
2. They provide you with the ability to act upon these insights using automations.

Let’s break that down. In order to leverage machine learning and artificial intelligence to glean insights, it is necessary to have a large and carefully curated data set to provide the raw material to build upon.

There is no shortage of data that is available – in fact, there is definitely too much! Today’s IT operations teams are faced with a tidal wave of data. And herein lies the primary challenge.

Challenges of AIOps Solutions to Consider

For AIOps solutions to be effective, it is absolutely essential to have first, a data management framework, and second, an accompanying business strategy. The architecture and desired business outcomes of the framework help define the kinds of data that needs to be stored, how different sources are normalized, and what kind of enrichment needs to be performed. Below are three AIOps challenges to consider:

AIOps Solution Challenge #1: The Data Management Strategy

The data management strategy must define what the goals of the analysis are and what kinds of insights are expected.

Insights by themselves are useful. But the long-term promise of AIOps – such as proactive remediation and predictive orchestration – can only be realized when there is a concerted effort to automate subsequent actions.

The other perspective when it comes to concerns and challenges are around outcomes. At the end of the day, it is necessary for AIOps solutions to provide real-world business value. As with any emerging technology, IT organizations face challenges aligning investments with goals and lining up the right skill set to be successful.

There are a myriad of options and it can be quite daunting to choose a solution that is suited to the specific needs of the business. The effective use of AIOps solutions requires a team that has the right blend of knowledge and experience and tools tailored to the mission.

AIOps Solution Challenge #2: Build the Strategy with Use Cases

With the right team in place, the strategy now needs to be expanded to enable positive outcomes to the business. There needs to be a balance between the capital/staffing investments and the derived value to the business.

The most effective way to do this is to extend the strategy to define specific AIOps use cases, examples and models that can quantify the value generated by the expansion of the AIOps initiatives within the business.

Based on existing resources, the strategy could outline a specific set of use cases as the initial objective. This will have the benefit of limiting the amount of data points, resulting in a more efficient data framework and automation deployment.

You have to start somewhere. It pays to start small, take deliberate steps and evolve your AIOps solutions deployments as you go along.