Five Common Mistakes That Can Trip Up AIOps Plans

eWEEK DATA POINTS: Artificial intelligence for IT operations (AIOps) is an umbrella term for the use of big data analytics, machine learning (ML) and other artificial intelligence (AI) technologies to automate the identification and resolution of common information technology issues.

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If IT operations teams want to deliver maximum business value from AIOps (artificial intelligence operations) deployments, they should pay attention to these five common mistakes that can trip their best-laid plans. 

Artificial intelligence for IT operations (AIOps) is an umbrella term for the use of big data analytics, machine learning (ML) and other artificial intelligence (AI) technologies to automate the identification and resolution of common information technology issues.

Large volumes of alerts, significant IT noise and signals distributed across disparate tools are holding DevOps professionals back, anaysts have reported. Gartner has predicted large enterprises' use of tools like AIOps will grow from 5% in 2018 to 30% in 2023.

The following industry information was contributed to this eWEEK Data Points article by Deepak Jannu of OpsRamp. San Jose, Calif.-based OpsRamp makes an operations management platform designed to simplify the management of diverse computing environments in order to accelerate the speed of enterprise IT.

Data Point Mistake No. 1: Not analyzing your current state of IT operations. 

Technology leaders planning to purchase an AIOps platform should take a close look at how their teams handle incidents. They should start with a playbook that documents how teams respond to problems and analyzes the effectiveness of incident resolution processes. Otherwise, IT might buy an AIOps tool that’s a terrible fit without understanding how existing event management workflows, staff skills and tooling are hampering business outcomes and customer experiences.

Data Point Mistake No. 2: Not measuring the business outcomes you wish to achieve with AIOps. 

IT teams should assess the effectiveness of current incident resolution processes to determine how much they can improve infrastructure availability, enhance operational agility and reduce management complexity with AIOps.

While there are clear benefits to a data-driven approach for event and incident management, IT leaders should also consider the tradeoffs involved in a successful implementation, including time savings, data requirements and staff training. 

Data Point Mistake No. 3: Not drafting a tools selection criteria driven by organizational priorities. 

IT professionals gravitate toward feature comparison checklists while evaluating different AIOps tools. While technical tradeoffs are a useful exercise, tool selection should rest on specific use cases that contribute to business outcomes such as better customer support or quicker problem resolution.

Data Point Mistake No. 4: Not staffing a center of excellence. 

Organizations that wish to deliver a successful and scalable AIOps adoption should build a cross-functional tiger team known as the Center of Excellence (CoE). The CoE ensures alignment with business requirements, delivers an incremental approach for deployment and shares best practices for accelerating the AIOps journey. 

Data Point Mistake No. 5: Not marrying human insights with machine data intelligence. 

An implicit goal of AIOps deployments is to shrink overall staff working on incident management. While IT leaders can redeploy existing staff working on incident resolution once their AIOps platform has matured, headcount reduction should not be the major focus of modernizing incident management workflows.

Before investing in an AIOps solution, technology leaders should first analyze their current state of IT operations, measure the business outcomes that they wish to achieve and select tools driven by organizational priorities. This will avoid costly missteps and ensure business priorities are the driving force behind enterprise AIOps deployments.

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