Assess AIOps benefits and challenges for enterprise IT teams

To successfully reap the benefits of an AIOps implementation, IT teams first have to navigate some crucial cultural and technical shifts.

Before enterprises adopt IT management suites armed with machine learning and other AI functionality, they deserve to be clear on both the advantages and challenges these tools present.

IT operations staff should consider AIOps platforms a force multiplier that augments -- but doesn't replace -- their skills. Used properly, these tools enable IT to improve its quality of service and reserve human ingenuity for more strategic tasks, such as process improvements, service development and better alignment with the business.

AIOps benefits

A fundamental benefit of AIOps is that of any automated process -- namely, a significant reduction in overhead for IT staff, as software handles routine monitoring and problem-identification tasks. However, unlike traditional process automation, where a system programmatically executes a preset recipe, the machine learning models in AIOps tools regularly update with new data, facilitating the system to learn, adapt and improve as it adjusts to dynamic conditions.

These properties yield the following advantages.

More efficient problem resolution. One key AIOps benefit is how these tools enable IT teams to react quickly to operational problems through real-time data analysis, proactive system alerts and the automated correction of routine incidents. Through multisystem data analysis and event correlation, these tools also improve system and network anomaly detection and provide more thorough problem diagnosis and root cause analysis.

Improved performance from IT generalists. AIOps platforms embed subject matter expertise into management software in a way that empowers IT professionals to expand their sphere of competence.

Better decision-making. Other AIOps benefits include the ability to use data-driven insights, predictive analysis and sophisticated data visualizations to make more informed IT operations decisions. AIOps tools also inform better IT decisions at a strategic level. Graphical summaries of data across IT silos bring important issues around resource utilization, application performance and customer service to the attention of management.

Defining AIOps

AIOps marries big data analytics, machine learning, data visualization and programmable process automation to IT operations management. AIOps tools collect, aggregate and analyze IT operations data from a wide range of sources to perform tasks, which include the following:

  • system and application monitoring and reporting to summarize overall performance; automatically set baseline performance norms; detect performance, infrastructure and security anomalies; and correlate events from multiple systems to aid troubleshooting and root-cause analysis;
  • end-to-end analysis of application performance and UX; and
  • predictive modeling of infrastructure and application performance and resource usage trends to assist with or trigger automatic remediation of capacity constraints and misconfigurations.

These functions are typically associated with IT operations software in the following categories, most of which are more commonly known by their acronyms:

  • application performance monitoring and management (APM), including proactive analysis;
  • anomaly detection and security information and event management (SIEM);
  • event correlation and analysis (incident management); and
  • IT service management (ITSM).

AIOps challenges and barriers

Despite the range of AIOps benefits, the implementation of AI-driven IT operations tools isn't seamless.

As with any system that introduces significant changes to IT processes and transforms IT team members' responsibilities, AIOps can seem threatening to employees. Indeed, the threat of job loss or reassignment, whether real or implied, means AIOps implementation presents organizational challenges. Expect technical hurdles to also arise.

The following AIOps issues aren't showstoppers, but enterprises should address them before they pursue adoption. New software is seldom a quick and painless cure for systemic IT problems.

New to AIOps?

Check out this brief, introductory video to learn more about key AIOps benefits, challenges and use cases.

Poorly documented IT operations processes and systems. It's impossible to automate something that you don't understand. AIOps tools augment existing IT workflows, but they aren't designed -- at least not yet -- to replace them with a fully autonomous system.

Inadequately skilled operations employees. Like other automation tools, AIOps eliminates routine, low-skill tasks, which ideally frees up IT personnel to focus on higher-value activities, such as system optimization and process improvements. But, if an IT staff's skills are limited to those quotidian monitoring and first-level debugging tasks that AIOps eliminates, it will create challenges. Admins need to be able to pick up where AIOps leaves off -- and that requires deeper expertise in systems analysis, application debugging, programming and data analysis.

Personnel resistance and cultural backlash. Staff members who feel threatened by the AIOps software might resist change through both active means, such as not engaging in project tasks, and passive means, such as ignoring available training opportunities.

As with any disruptive technology, be slow and deliberate with AIOps implementation.

Focusing on the wrong goals. Even well-meaning IT managers and staff can get sidetracked by obsessing over tactical measures of AIOps success -- such as the number of performance anomalies detected -- rather than larger, strategic goals, like lower costs, improved resource utilization, better application performance, faster time-to-problem resolution and higher system availability.


AI will eventually be an integral component of every IT operations process and automation tool. As with any disruptive technology, be slow and deliberate with AIOps implementation. Start with tasks and processes, such as performance and security monitoring, incident response and forensic problem analysis, where the ability of machine learning models to digest and correlate immense quantities of data is beyond that of a person.

Work to increase staff knowledge and skills, and communicate how, exactly, AIOps will benefit IT processes and workflows. Initiate training programs on machine learning and deep learning algorithms, as well as how to develop analytical models that customize out-of-the-box AIOps capabilities. As staff grows more familiar with and confident in AIOps systems, begin to introduce predictive models with proactive automation capabilities that diagnose and fix potential problems before they become serious.

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