Three Reasons AIOps Is the Future of ITOps

An ITOps team’s current work environment may have shifted over the past year due to the ongoing pandemic. Many companies have seen their staff move from an office setting to a remote setting. This rise in remote work creates even more uncontrolled variables in an already complex IT infrastructure. IT environments are growing more chaotic as companies move to modular microservices architecture.

Additionally, IT infrastructures are consistently churning out record amounts of new data. Gartner, a global research and advisory firm, estimates that the average enterprise IT infrastructure generates two to three times more IT operations data every year. 

Many modern enterprises are already benefiting from advanced analytics and operational processes when it comes to IT problem resolution, performance, cybersecurity and connection. However, rapid response expectations from end-users alongside an increasingly dynamic hybrid cloud IT landscape creates a service gap that only intelligent tools can bridge.

This is where artificial intelligence comes in. 

A natural evolution to ITOps, AIOps is the application of artificial intelligence (AI) to enhance IT operations. 

Gartner, which coined the term in 2017, explains further, stating that, “AIOps combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination.”

Utilizing AI tools can transform the process of an IT operations workflow — significantly reducing the mean times to resolve (MTTR) an incident. AIOps tools provide IT operations teams with the artificial intelligence, big data and machine learning they need to detect issues early, predict future issues, reduce event noise in order to pinpoint a problem source, make holistic conclusions regarding its impact and recommend and take automated actions. 

1. AIOps provides complete visibility 

AIOps big data platforms give enterprises complete visibility across systems and correlate varied operational data and metrics. IT leaders can utilize an AIOps platform to gain advanced analytics and deeper insights across the lifecycle of an application.

Getting complete visibility into real-time operations data allows IT operations teams to identify a problem faster, ideally before it occurs. In other words, enterprises can be predictive in their problem resolution and take action faster via real-time anomaly detection. This observability and full visibility into operations data is also necessary for enterprises to optimize by implementing AIOps tools like machine learning to put automations to work.

2. AIOps improves IT service management

Cloud infrastructures that include multicloud environments create more complex stacked systems that need to be monitored, managed and acted-upon in real-time. Traditional monitoring tools are reactive, which can slow down response time by not being able to get ahead of an incident.

AIOps solutions improve performance monitoring and incident response time with monitoring tools that correlate to transform an ecosystem by delivering insights that are driven by environment-specific algorithms. This predictive analysis detects anomalies so IT operations management (ITOM) and DevOps teams can fully understand what is or isn’t working in real time.

When utilizing artificial intelligence for IT operations and the management tools available, end users can also benefit from algorithms that can structurally read and link topology input. IT organizations can then improve application performance by being able to visualize and interpret patterns and connections with less effort and fatigue. These data science solutions allow ITOps teams to understand the advanced analytics coming in from huge volumes of data without needing a data scientist. 

3. AIOps reduces noise through AI automation

The amount of service tickets and alerts that can occur from just a single incident can overwhelm an IT operations team. Traditional IT management processes struggle to keep up with massive amounts of incoming data, and important signals are often unable to be sorted from the noise. This volume of noise can result in decreased user experience functionality and lengthy downtimes that can impact customer experience. 

Implementing artificial intelligence for IT operations and applying machine learning to historical and real-time data reduces alert noise and isolates the incident that is causing the problem.

AIOps tools can correlate and isolate events to create actionable insight and identify the root cause of what’s not working, locate where the issue is and suggest automation solutions for faster remediation. 

AIOps tools can increase automations across workflows. By automating the analysis of event tools, log tools and metric data, IT operations teams can benefit from this anomaly detection and root-cause analysis in real time to enhance cybersecurity, implement a remediation plan or process an automated fix. 

Advanced AIOps solutions can transform enterprises from a dependence on vendors and experts to becoming self-learning and self-healing operations. 

Advanced AI models can help a system continually learn about its environment from its data and improve itself and its recommendations, all while adapting to changes.

With the right technical tools, support and integration, IT operations environments can not only thrive from autonomic computing (by removing workers from having to deal with ongoing complexities) but build a personalized knowledge base over time that exceeds what humans can achieve alone.

Explore IBM Cloud Pak® for Watson AIOps and find out how we can help you deploy advanced, explainable AI throughout your entire IT operations toolchain to assess, diagnose and resolve incidents across your IT infrastructure.