Building a Long-Term AIOPs Strategy

Introduction

Complex data available in the enterprise systems has increased the demand for Artificial Intelligence for IT operations (AIOPs). AIOPs not only enables organizations to automate recurring ordinary tasks, but it also processes a vast volume of data.

Various organizations consider AIOPs as a tool. While large number of AIOPs platforms exist, the value that AIOPs bring should be thought of as more of a long-term plan for organizations. Enterprise teams can significantly increase their performance, foresee outages, automate recurring mundane tasks, generate actionable reports and improve their risk management while enabling an organization and its clients to accomplish their end goals.

Building a Long-Term AIOPs Strategy

As enterprise leaders understand how and when to invest in AIOPs, it is important for them to consider the commitment and investment as a strategy instead of one solution. Following are a few steps for kick start a long-term AIOPs strategy:

• Identify the AIOPs maturity level

AIOPs has the following five levels of maturity:

• Reactive
• Integrated
• Analytical
• Prescriptive
• Automated

Business executives should understand the AIOPs level their organization stands at to understand how AIOPs aligns with their overall business requirements.

Teams have to encounter siloed operations at the reactive stage and collate logs and events for reactive initiatives. These teams are in a continuous battle mode and do not communicate with other parts of the business. Data sources are merged into a unified architecture in the integrated stage. The integrated stage provides enhanced ITSM processes and communication slowly starts, which helps the business improve.

As teams embark on the prescriptive and analytical stages, data transparency increases, comparative analytics measure business value and improvements, and automation and Machine Learning (ML) comes into the picture. The last automated stage accomplishes complete automation without any human interaction and teams take decisions depending on the business value. Irrespective of the stage one is in, it is possible to advance to the next stage through long-term commitment and patience.

• Evaluate tools and their capabilities

Another essential factor for a long-term strategy is to assess the present set of tools and check where AIOPs can create more advantages to the organization. Organizations should identify their challenges within the infrastructure and how AIOPs can address these challenges. Understanding where and how AIOPs can be most useful amongst other tools is essential when considering the AIOPs as a strategy versus just a siloed tool.

As organizations evaluate these capabilities and tools, they realize that there are various tools that overlap and fundamentally do the same thing, otherwise termed as a tool sprawl. As organizations begin to evaluate their current toolset and how AIOPs can fit into it, they should think of a tools rationalization process for assessing capabilities and evaluating tools decide on the ones that need to be sopped being used. With the tools rationalization process, organizations can save millions every year by removing redundant tools from their tech stack.

• Determine use cases and best practices within the organization

Before beginning integrated AIOPs, business executives should recognize why they require an AIOPs strategy. They should how it will help the organization in both the long and short terms.

Conclusion

As per a 2020 report, the top three use cases for AIOPs and ML are performance, incident, and availability management, change impact and capacity optimization, business impact, and IT-to-business alignment.

The executives need to understand if these are the issues that need addressing in their organization. Even though these are the most common issues, it is essential for them to understand the specific use cases their business is hoping to resolve with AIOPs and how it can support their long-term business objectives.