July 20, 2023

How Artificial Intelligence Supercharges IT Operations

This article kicks off a 4-part series on leveraging AIOps to provide a more efficient, cost- and resource-saving, reliable, and agile IT infrastructure.

How Artificial Intelligence Supercharges IT Operations (AIOps)
How IT Teams Leverage AIOps’ Capabilities
Pump the Brakes: Some Key Considerations in Your Journey to AIOps
The Road Ahead: 4 Ways AIOps Will Build More Resilient IT Operations

In today’s interconnected digital landscape, IT Operations teams have the herculean task of building smooth, well-constructed connections for networks, applications, infrastructures, and environments and then deploy the tools that will give visibility into how all those systems are performing. This is where Artificial Intelligence can play the role of an early warning system and advisor for your IT Operations (AIOps). AIOps use artificial intelligence and machine learning to intelligently predict slowdowns, warn of impending bottlenecks, and remove redundant, mundane, or repetitive roadblocks. AIOps reduce outages, issue early warnings ahead of slowdowns, and reduce Mean Time to Resolution (MTTR). When an organization uses AIOps, they’re signaling a desire to prioritize efficiency, reliability and agility. Integrating artificial intelligence is the difference between a desert road trip without air conditioning, and that same road trip with a/c and a cooler of water within arms length of both the passengers and driver.

So buckle up and explore the key components of AIOps, look at a real-world use case which illustrates its benefits, and discuss how AI could shape the future of IT operations.

Key Components of AIOps: Building the Road to Success

AIOps are highly effective at collecting a myriad of data from a multitude of sources to detect patterns, gain predictive insights, and automate solutions with the end goal of reducing the length of downtime episodes. Platforms that include AIOps use fewer resources, processes, and tools while experiencing better visibility, improved response times and better consistency within their infrastructure and networks. Most AIOps deployments include three main components:

Data Collection and Aggregation
Machine Learning
Automation and Remediation

Data collection and aggregation: The volume, variety, and vastness of data is beyond the capabilities of human interpretation, but machines are built to handle this velocity. AI effortlessly analyzes information from logs, applications, user behavior analytics, and other valuable insights, to create a virtual map of your technology stack. The drawback here is if the data is compromised or outdated, the result will be inaccurate analysises, false positives, false negatives, and incorrect predictions. Data governance and quality monitoring are critical to ensure the accuracy of AI-driven operations.

Machine Learning: Organizations that pair machine learning algorithms and AI’s evolutionary self-learning techniques enable their operational systems to recognize patterns, detect anomalies, and even predict system issues so IT teams can resolve them before they become a negative disruption to the developer or user experience.

Automation and remediation: AIOps incorporates intelligent remediation capabilities with root cause analysis that can automate a resolution. Like a road crew working through the weekend in shifts, self-learning AI is always detecting mundane work and automating it. With AIOps determining which issues are a priority and need to be escalated, IT teams can pave the way for innovative growth and high-priority projects.

0 to 60mph: AIOps Accelerates A Real-World Use Case for AIOps

Let’s take a look at how AIOps resolved a fragmented and inefficient IT operation at a global energy management leader.

Challenge: The organization was operating across 30 platforms responsible for 25,000 network devices. This created inefficiencies, painful complexity, and no unified view into the tech stack. Efforts to fix visibility only resulted in adding redundant tools that further complicated data aggregation, operational communications, and issue detection and resolution.

Solution: The organization defined its prioritization of unified observability as “everybody looking at the same set of metrics.” Efforts to add platforms to improve monitoring only worsened tool sprawl and complexity. So the company invested in a single platform which is able to deploy their entire IT cloud and network infrastructure. Now the organization has reduced all their clusters, pods, and projects from 30 different platforms to just five. They have unified observability in a single pane of glass and the organization has seen massive wins in downstream operations, including:

40% reduction in false alerts (went from 17,000 to 10,000 after AIOps deployment)
83% consolidation in monitoring tools
Full stack visibility across:
- Network infrastructure
- Cloud infrastructure
- Kubernetes
- On-Premises

In the 2-minute video below, hear first-hand how the organization built a roadmap to better observability, reduced MTTR, and is closer than ever to achieving their goal of “self-healing” with AIOps.

The Road Ahead: The Future of AIOps

Artificial intelligence and machine learning tools will pave the way for exponential growth as future business workloads require improvements to operating systems both in the cloud, on-prem, and in hybrid environments.

As the implementation of AIOps grows, the technology sector will benefit from:

Data Silo Demolition
Less Operational Noise
Improved Customer Satisfaction
Improved Financial Performance

Data Silo Demolition: AIOps processes and aggregates inputs at speeds the technology industry has never seen before. Putting this cumbersome task on the plates of AI frees up business-critical IT teams for innovation as their work streams become partially automated and the removal of redundant tasks allow them to focus on “the next big thing.”

Less Operational Noise: This is both the literal sense of fewer false alarms and warnings being issued, and the philosophical sense of AIOps prioritizing and filtering alerts based on relevance. With fewer alerts to tend to, and levers being pulled for more relevant issues, IT teams will have more time to focus on the most critical issues within their operations.

Improved Customer Satisfaction: AIOps proactively mitigate issues which could disrupt the overall user experience, service, or delivery processes. This mitigation practice provides customers and prospects with a digital experience that surpasses the competition.

Improved Financial Performance: AIOps improve resource usage, remove roadblocks and inefficiencies, and forecast demands or possible performance issues. All of this improves an organization’s bottom line. AIOps can also be deployed to identify patterns, trends, and obstacles within an organization’s financial management, improving cost savings through better management of financial strategies, investments, and operational costs associated with any expansion plans.