If business and IT executives are not clear on the goals they hope to achieve, they will find that investment in AIOps may do more harm than good.
Complex data coming into enterprise systems has driven up the demand for artificial intelligence for IT operations (AIOps). Not only does it enable organizations to automate repetitive mundane tasks, but it also processes a massive volume of data.
Most organizations view AIOps as a tool. While there a significant number of AIOps platforms exist, the value AIOps bring to the table should be viewed as more of a long-term strategy for organizations. Through an AIOps strategy, enterprise teams can dramatically increase their performance, predict outages, automotive repetitive mundane tasks, build actionable reports and enhance their risk management while helping the organization and its customers to achieve their end goals.
As enterprise leaders determine when and how to invest in AIOps, it is crucial for them to view the investment and commitment as a strategy instead of a single solution. Here are a few steps for them to kick start a long-term AIOps strategy:
Identify the level of AIOps maturity
AIOps has five levels of maturity: reactive, integrated, analytical, prescriptive and automated. Before embarking on their AIOps journey, business executives should determine which AIOps level they are currently in to better understand how AIOps aligns with their overall business needs.
In the reactive stage, the teams face siloed operations and gather events and logs for reactive initiatives. They are in constant fire-fighting mode and do not have any communication with other parts of the business. In the integrated stage, data sources are incorporated into a unified architecture, offers improved ITSM processes and communication slowly begins, which helps to improve the business.
As teams embark on analytical and prescriptive stages, transparency of their data increases, machine learning as well as automation comes into the picture and comparative analytics measures improvements and business value. The final automated stage achieves full automation with no human interaction and teams make proactive decisions depending on business value. No matter which stage one is currently in, it is possible to advance to the next stage through patience and long-term commitment.
Evaluate tools and their capabilities
Another vital element for a long-term strategy is to evaluate the current set of tools and see where AIOps would yield more benefits to the organization. Organizations should take steps to identify where they have exposure in the infrastructure and how AIOps can address them. Understanding how and where AIOps can be most impactful among other tools is crucial when seeing this as a strategy versus just a siloed tool.
As organizations review these tools and capabilities, they realize that they have many tools that overlap and essentially do the same thing, otherwise called tool sprawl. As organizations begin to evaluate their current toolset and how AIOps can fit into it, they should consider a tools rationalization process for evaluating tools and capabilities and determine which ones they should stop using. With the rationalization process, organizations can often save millions of dollars every year by eliminating unnecessary tools in their tech stack.
Determine use cases and best practices within the organization
Before starting integrated AIOps, business executives should identify why they need an AIOps strategy. They should think critically about how it will help the organization in both the short and long term.
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As per a 2020 report from EMA titled, “EMA Radar Report: AIOps,” the top three use cases for machine learning and AIOps are incident, performance as well as availability management, change impact and capacity optimization as well as business impact and IT-to-business alignment.
The executives should ask themselves whether these issues they are trying to address. Even though these are the most common, it is crucial for them to understand what specific use cases their business is hoping to solve with AIOps and how it can support their long-term business objectives.