What Is AIOps?

AIOps is an acronym for Artificial Intelligence in IT Operations. The term was coined by research firm Gartner in 2016 in response to the emerging intersection of AI and IT operations. AIOps integrates AI-based tools into observability platforms, bringing greater insight and efficiencies to processes and reducing the cost of IT operations. Using your data to train machine learning (ML) models specific to IT can deliver higher performance, faster anomaly detection and problem-solving, and enable more effective automation. 

AIOps for all

Leveraging AIOps in an observability platform can accelerate problem-solving and solution implementation across a wide fleet of on-site and cloud-based infrastructure. With the rapid rise in diverse IT systems and their distribution, AIOps plays a key role in making IT operations more efficient. With this in mind, New Relic makes Applied Intelligence an integrated feature for every full platform user, on every New Relic observability plan—AIOps for all.

A natural evolution in observability

AIOps represents the next step in the evolution of IT operations and observability. Consider how AI has seamlessly integrated into daily life, often in ways we may not immediately recognize—for instance, interacting with a smart home device. Over time, AI applications have advanced to perform complex pattern recognition, like identifying faces in images or accurately detecting anomalies in medical imaging and manufacturing processes.

Today, machine learning algorithms, trained on diverse datasets, excel at identifying patterns and automating solutions far faster than human capabilities allow.

Why is AIOps Important?

As the complexity of operating production systems increases, software teams require faster, more effective ways to resolve incidents. AIOps provides the automation and intelligence needed to augment existing incident management workflows, assisting teams with finding and fixing problems faster. Modern AIOps solutions prioritize ease of onboarding, learning, and usage, making them an accessible and valuable tool for teams facing growing operational demands.

Key Benefits of AIOps

“Do more with less” has long been a mantra of IT – making AIOps an important inclusion in an observability platform. AIOps delivers a range of important benefits, enabling systems to run better with higher uptime, reducing costs, and allowing engineers to focus their time on innovative initiatives instead of tracking down problems. 

Improve performance: With trained models for predictive analytics, AIOps can find and solve performance issues faster, allowing systems to run more efficiently.

Reduce downtime: Predictive analytics can identify issues before they happen and help drive automated solutions that keep systems running smoothly.

Accelerate root cause analysis: Applied intelligence examines your telemetry and other siloed data to find root causes in real-time.

Accurately predict outcomes: Machine learning models trained on your data – along with wider and more general IT metadata and information – can quickly analyze and more accurately predict outcomes.

Improve collaboration: Expanding training and analytical data beyond telemetry brings in critical insights from other departments (such as customer service, analytics, and sales), helping IT ops work more effectively and make faster, data-backed decisions. 

Reduce IT spend: AIOps accelerates automated problem-solving and solution implementation, helping cut costs on specialized appliances (such as network monitoring and security hardware and legacy IT infrastructure tools), software, and the time IT professionals spend on manual tasks. 

Accelerate innovation: With greater intelligent automation in IT operations, engineers can focus on more important innovation and initiatives that position them to stay ahead of threats or create efficiencies. 

What problems do AIOps solve?

As software teams modernize and adopt cloud-native technologies, IT environments are becoming increasingly more complex. Teams must monitor a growing number of microservices with more software changes occurring faster, more operational data emitted across fragmented tools, more dashboards, and more alerts. This puts added pressure on IT professionals to find and fix incidents quickly, as well as prevent them from occurring in the first place. This breakneck pace and scattered array of systems and services can contribute to greater fatigue with IT teams.

As the volume of data increases, so does time required to diagnose and resolve issues. Many IT ops teams find themselves mired in a constant cycle of reactive problem-solving, fighting fires instead of implementing proactive strategies to prevent outages or performance issues.

Response fatigue is real. Between noisy alerts and countless “unknown unknowns,” distinguishing critical signals from noise continues to pose a major challenge. Quickly pinpointing the root cause of an incident – and responding proactively – tacks on an additional layer of complexity. Every minute that DevOps, SRE, and NOC teams spend analyzing data, detecting anomalies, or manually diagnosing issues impacts service level objectives (SLOs), company reputation, and overall profitability.

AIOps helps solve these challenges by using AI-driven methodologies trained on your data to proactively detect issues, identify root causes, and recommend or automate solutions. As a result, IT teams can focus more intently on innovation instead of firefighting on multiple fronts across an organization. 

How does AIOps Work?

AIOps follows a four-stage, disciplined approach that integrates AI into technologies to drive greater efficiencies. These stages, followed sequentially, help ensure an effective AIOps deployment that is tuned to your infrastructure, apps, and SLOs.

The Four Key Stages of AIOps

The four stages of AIOps include data collection and curation, training models on your data, building automated solutions that respond to the predictions of the models, and deployment for anomaly detection.

1. Data collection: The complexity of modern IT systems, combined with an organization’s SLOs make it critical to identify and collect useful data to inform a successful AIOps deployment. Too little data – and the wrong data – create ineffective and inaccurate models. With the aid of data scientists and cross-functional teams, curating the right data helps build a more effective AIOps solution. AIOps integrates siloed data across an infrastructure. This data can include historical systems data and events, logs, network data and real-time operations.
2. Model training: What functionality do you want in your AIOps intelligence? The goals of your AIOps solution and quality of your data will determine how models are selected and trained. Key areas to focus on include proactive scalability, security, performance, and storage optimization. Because IT environments are constantly evolving, models should also be designed to retrain themselves over time to stay accurate and effective.
3. Automation: Well-trained AIOps models work best when paired with automated tools and applications that can respond to insights in real time. These tools allow AIOps to respond instantly to predictive analytics and model outcomes, reducing tedious manual effort. These tools can be created from existing observability tool sets or developed as custom applications tailored to specific needs. 
4. Anomaly detection: Once models are deployed, real-time analytics speed up anomaly detection and response. Data from previous outcomes can also be incorporated into feedback loops to continuously help retrain models to improve accuracy and effectiveness over time. 

Use cases for AIOps

There are four main ways that DevOps, SRE, and on-call teams are putting AIOps to use:

1. Detecting Issues Before They Happen

The first step in detecting issues is identifying potential problems in your software, before it impacts the customer experience. AIOps tools automatically detect anomalies in your environment and trigger notifications to your monitoring solution as well as other tools where your teams collaborate and get work done, like Slack.

2. Reducing Noise and Connecting the Dots

AIOps tools help teams prioritize and focus on critical issues by correlating related alerts, events, and incidents, and enriching them with context from historical data or other tools in your stack. The most advanced tools utilize both machine-generated (i.e., time-based clustering, similarity algorithms, and other ML models) as well as human-generated decisions to suppress noisy or low-priority alerts and identify meaningful patterns.

AIOps tools also provide valuable context by classifying incidents based on the four SRE golden signals—latency, traffic, errors, and saturation—so you can more easily diagnose the root cause of an issue and determine how to resolve it.

3. Getting Alerts to the Right People Faster

AIOps tools can automatically route incident data to the individuals or teams best equipped to respond to them. Particularly for decentralized, distributed teams, this reduces    the number of noisy alerts sent to the wrong people and cutting the time it takes to route critical incident data to the right folks.

AIOps tools run ML models to evaluate data from your incident management and monitoring tools and suggest an individual or a team that can resolve a particular problem faster, because either they’ve already seen something similar in the past or are experts at the specific components that are failing.

4. Automated incident remediation

The last, and most critical, step in resolving incidents is actually fixing the problem. AIOps tools streamline this process by automating workflows and remediation tasks to resolve the incident when it occurs, and reduce mean-time-to-resolution.

As teams look to close the gap between detecting a problem, diagnosing it, and fixing it, the scope of AIOps is increasing to solve these last-mile challenges.

Selecting the right AIOps platform

AIOps extends the value of your observability platform by using advanced IT intelligence to automate and optimize operations. A strong foundation starts with a rich set of observability tools, dashboards, and automations that adapt to your organization’s unique needs. The more you can leverage AI-powered automation within existing IT ops systems, the further you’ll progress in your AIOps journey. 

Choosing the right AIOps solution to complement your initiatives can help you weave in the right data for more effective IT operations management. AIOps solutions can be domain-agnostic or domain-specific. A domain-agnostic AIOps solution gathers data from across your organization to address a wide range of IT operations. Domain-specific solutions focus on a narrower set of data and are tuned to the specific environments and issues within a particular domain.

New Relic AI is an AIOps solution designed to help busy DevOps and SRE teams identify, troubleshoot, and resolve problems more efficiently. By minimizing repetitive, time-consuming tasks and shifting teams out of reactive “firefighting” mode, New Relic AI enables your team to focus on the creative and challenging work of building and maintaining great software.

Unlike traditional incident management tools or domain-centric AIOps platforms, New Relic AI is domain-agnostic, leveraging raw monitoring data to power its machine learning models. This allows it to integrate seamlessly with diverse environments and tools, delivering a context-rich, intelligent incident response workflow.

By deeply integrating with the incident management tools you already use, New Relic AI brings intelligence to your current processes, ensuring faster detection and noise reduction without requiring a complete overhaul of your DevOps workflow.