What Is AIOps? A Complete Guide

First coined by Gartner in 2017, artificial intelligence for IT operations (AIOps) refers to the application of machine learning to big data analytics for the automation and management of IT operations. Take any area of IT operations—log analytics, application monitoring, service desk, incident management, etc.—augment it with AI, and you’ve got AIOps. There are many ways AI can be integrated into existing IT operations to help your team be more efficient, proactive, accurate, and productive. This comprehensive guide explores what AIOps is, how it works, and its benefits, challenges, use cases, and tools.

AIOps is the leveraging of AI and machine learning to enhance and automate IT operations. The acronym AIOps encapsulates the fusion of artificial intelligence and IT operations, reflecting its aim to intelligently manage and optimize IT systems.

AIOps goes beyond traditional IT management tools by using advanced algorithms to analyze and interpret vast amounts of data generated by IT infrastructure. This data-driven approach enables AIOps to identify patterns, anomalies, and trends that might go unnoticed by human operators. In doing so, AIOps empowers organizations to proactively address issues, predict potential failures, and optimize performance.

The heart of AIOps’ value is the ability to make sense of the overwhelming volume of data generated by various IT components. Machine learning algorithms play a pivotal role in this process, as they continuously learn from historical data, adapting and evolving to better understand the intricacies of an organization's IT environment.

Data analytics is another critical aspect of AIOps, providing the capability to extract valuable insights from diverse data sources. This includes logs, metrics, and event data, which AIOps platforms analyze in real time to detect patterns indicative of potential issues. By automating the correlation of these data points, AIOps not only identifies problems but also helps in predicting and preventing future disruptions.

DevOps vs. AIOps

The synergy between DevOps and AIOps has become increasingly important for organizations needing to enhance efficiency and streamline operations. DevOps, or development and operations, is a set of practices that aims to automate and improve the collaboration between software development and IT operations.

The relationship between DevOps and AIOps is symbiotic. While DevOps focuses on the collaboration and communication between development and IT teams, AIOps brings a layer of intelligence to the operational side. This collaboration ensures a more agile and responsive IT environment by seamlessly aligning development and operations.

What Is Deterministic AIOps?

Deterministic AIOps is a paradigm within AIOps that emphasizes predictability and reliability in its operations. In contrast to traditional AIOps approaches that may rely heavily on probabilistic models, deterministic AIOps aims to provide more certainty in decision-making processes. By incorporating deterministic algorithms and logic, this approach seeks to reduce uncertainties and make IT operations more transparent and controllable.

In deterministic AIOps, models are built on explicit rules and logic, enabling organizations to have a clearer understanding of the decision-making process. This level of transparency is crucial for IT operations requiring precision and reliability. Deterministic AIOps becomes especially beneficial in scenarios where the consequence of errors or inaccuracies can have significant impacts on business operations.

What Is Domain-agnostic AIOps?

Domain-agnostic AIOps takes a broader approach by transcending specific domains or industries. Unlike domain-specific AIOps solutions tailored to the unique characteristics of a particular sector, domain-agnostic models are designed to be versatile and adaptable across various industries.

The flexibility of domain-agnostic AIOps lies in its ability to handle diverse data sets and operational scenarios without requiring extensive customization for each domain. This makes it a valuable asset for organizations operating in multifaceted environments, allowing them to deploy AIOps solutions without the constraints of domain-specific limitations.

Why Modern AIOps Is Deterministic and Domain-agnostic

Modern AIOps solutions are increasingly adopting a dual approach, combining the deterministic principles of reliability with the versatility of domain-agnosticism. This combination addresses the evolving needs of organizations dealing with complex IT landscapes and diverse operational domains.

The deterministic aspect ensures precision and reliability in decision-making, reducing the risk of errors and providing a more transparent and understandable AIOps process. Simultaneously, domain-agnosticism allows organizations to deploy AIOps solutions seamlessly across various industries, promoting scalability and adaptability.

AIOps helps IT operations respond to disasters faster, minimizing recovery time objectives (RTOs) and recovery point objectives (RPOs).

The best way to understand AIOps is to consider what a typical IT operations professional must do to respond to a disruption of services and how AI can automate the process.

In its Market Guide for AIOps Platforms,¹ Gartner breaks the cycle of responding to and resolving disruptions into three steps:

1. Observe: The initial incident that triggered the disruption must be noticed for someone to respond to it. An AIOps platform automatically ingests logs, metrics, alerts, events, and other data needed to understand what is going on behind the scenes of an app when the incident occurred. Instead of a human manually pulling that information from disparate data sources and trying to make sense of it all, the platform will consolidate all of this data and identify patterns.

¹ https://www.gartner.com/en/documents/4015085

2. Engage: This step involves analyzing the monitored information and diagnosing the root cause of the disruption. Relevant information for resolving the issue is contextualized and passed on to the responsible operations personnel best equipped to resolve it. The AIOps tool can perform risk analyses, automate communication of responsibilities, and package relevant data for IT operations staff.

3. Act: The directly responsible individual (DIR) resolves the issue, restoring service to the application. Scripts, runbooks, and application release automation (ARA) can be created that will automatically run the next time the AIOps tool detects this particular issue.

The advantages of implementing AIOps include:

Faster Mean Time to Resolution (MTTR)

AIOps cuts through operational noise, correlating data from multiple IT environments to identify root causes and propose solutions faster than manual efforts. This results in significantly reduced MTTR, enabling organizations to achieve ambitious goals faster.

Lower Operational Costs

Automatic identification of operational issues and reprogrammed response scripts lead to reduced operational costs, allowing for improved resource allocation. This optimization also frees up staff resources for more innovative work, enhancing the employee experience.

Enhanced Observability and Collaboration

AIOps monitoring tools facilitate cross-team collaboration, improving visibility, communication, and transparency. This enables faster decision-making and response to issues. 

Proactive and Predictive Management

With built-in predictive analytics, AIOps continuously learns to identify and prioritize urgent alerts, enabling IT teams to address potential problems before they escalate into slowdowns or outages.

Enhanced User Experience

With predictive analytics and proactive issue resolution, AIOps can contribute to a better user experience by minimizing disruptions and ensuring the availability and performance of IT services.

Adaptability to Complexity

As IT environments become increasingly complex with the adoption of cloud, microservices, and hybrid infrastructures, AIOps becomes a crucial tool for adapting to this complexity. The ability to analyze vast amounts of data and provide actionable insights is essential for managing modern IT ecosystems.

AIOps leverages big data, advanced analytics, and machine learning capabilities to address various scenarios:

Root Cause Analysis

Root cause analysis, as the name implies, aims to identify the fundamental reasons behind issues and implement appropriate solutions. By pinpointing the root causes, teams can avoid unnecessary efforts spent on treating symptoms rather than addressing the core problem. For instance, an AIOps platform can trace the origin of a network outage, resolve it promptly, and establish preventive measures to avert similar issues in the future.

Anomaly Detection

AIOps tools sift through extensive historical data to uncover unusual data points within a data set. These outliers serve as signals to identify and predict problematic events, such as data breaches. This capability enables businesses to sidestep costly consequences like negative public relations, regulatory fines, and declines in consumer confidence.

Performance Monitoring

Modern applications often involve multiple layers of abstraction, making it challenging to discern the underlying physical server, storage, and networking resources supporting specific applications. AIOps acts as a monitoring tool for cloud infrastructure, virtualization, and storage systems, providing insights into metrics such as usage, availability, and response times. Additionally, it utilizes event correlation capabilities to consolidate and aggregate information, facilitating better information consumption for end users.

Cloud Adoption/Migration

Cloud adoption for organizations is typically a gradual process, resulting in a hybrid multicloud environment with multiple interdependencies that change rapidly and frequently. AIOps offers clear visibility into these interdependencies, significantly reducing operational risks associated with cloud migration and a hybrid cloud approach.

DevOps Adoption

DevOps accelerates development by empowering development teams with greater control over infrastructure provisioning and reconfiguration. However, IT still needs to manage this infrastructure. AIOps provides the visibility and automation necessary for IT to support DevOps without requiring substantial additional management efforts.

Here’s a step-by-step guide for organizations looking to successfully implement AIOps, covering necessary tools and technologies, potential challenges, and best practices for a smooth transition.

Step 1: Assess your current IT environment

Begin by conducting a comprehensive assessment of your current IT infrastructure, processes, and challenges. Identify pain points, areas of inefficiency, and where AIOps can provide the most significant impact. This initial analysis will help in defining clear goals and objectives for the implementation.

Step 2: Set clear objectives and goals

Establish specific and measurable objectives for implementing AIOps. Whether it's improving incident response times, enhancing system reliability, or optimizing resource utilization, having well-defined goals will guide the implementation process and provide benchmarks for success.

Step 3: Select the right AIOps tools

Choose AIOps tools that align with your organization's goals and requirements. Look for platforms that offer capabilities such as root cause analysis, anomaly detection, and performance monitoring. Popular AIOps tools include Splunk, Dynatrace, and Moogsoft. Evaluate each tool's features, scalability, and integration capabilities to ensure they meet your organization's needs.

Step 4: Integrate AIOps into existing workflows

To maximize the benefits of AIOps, integrate the new tools seamlessly into your existing IT workflows. This may involve adapting current processes or creating new ones to accommodate AIOps functionalities. Ensure that teams are trained on the new tools and understand how they fit into their daily operations.

Step 5: Address data quality and availability

Successful AIOps implementation relies heavily on the quality and availability of data. Ensure that your organization's data is accurate, up to date, and accessible. Establish data governance practices to maintain data integrity, as AIOps heavily relies on data-driven insights for effective decision-making.

Step 6: Overcome cultural resistance

Implementing AIOps may face resistance from teams accustomed to traditional IT operations. Foster a culture of collaboration and emphasize the benefits of AIOps in augmenting human capabilities rather than replacing them. Encourage open communication and involve key stakeholders in the decision-making process.

Step 7: Monitor and evaluate

Regularly monitor the performance and impact of AIOps on your IT operations. Collect feedback from teams and end users to identify areas for improvement. Use key performance indicators (KPIs) established in the goal-setting phase to measure the success of AIOps implementation and make adjustments as needed.

The three main challenges with AIOps are skills gaps, security, and scalability. Let’s take a brief look at each one. 

Challenge 1: Skills Gap

Most IT departments are still learning the ropes of AIOps. Invest in training and upskilling programs to bridge the skill gap within your IT teams. You may also want to consider collaborating with external training providers or hiring AIOps experts.

Challenge 2: Security

AIOps tools may introduce new security vulnerabilities to your systems. Prioritize cybersecurity measures and ensure that AIOps tools comply with industry standards and regulations. Implement robust encryption and access controls to safeguard sensitive data.

Challenge 3: Scalability

As your organization grows its AIOps-based systems, it may run into issues with scalability. Be sure to choose AIOps tools that can scale with your organization's growth. Regularly reassess your infrastructure needs and upgrade tools accordingly to ensure continued scalability.

AIOps Tools for Data Collection

Splunk: Splunk is a versatile AIOps tool that collects and indexes machine-generated data from various sources, including logs and events. It provides real-time insights into system performance, enabling organizations to troubleshoot issues proactively.

Logstash: Logstash, part of the Elastic Stack, is an open source data collection engine. It ingests and transforms data from multiple sources, facilitating centralized log management. Logstash supports various plugins for seamless integration with diverse data inputs.

AIOps Tools for Anomaly Detection

Dynatrace: Dynatrace employs AI-driven anomaly detection to analyze application performance and user experience. It automatically baselines normal behavior, identifying anomalies that could indicate issues. The tool provides actionable insights to resolve problems swiftly.

Moogsoft: Moogsoft uses machine learning algorithms for anomaly detection in IT operations. It analyzes patterns in data, identifying deviations from the norm and correlating events to understand the root cause of issues. This accelerates incident resolution.

AIOps Tools for Response and Remediation

PagerDuty: PagerDuty is a leading incident response platform. It integrates with monitoring and alerting tools, automatically creating incidents based on predefined rules. It facilitates real-time collaboration among teams for efficient incident resolution.

ServiceNow: ServiceNow combines incident response and remediation capabilities. It automates workflows, streamlining the response process. ServiceNow's platform ensures that incidents are documented, tracked, and resolved with minimal manual intervention.

AIOps Tools for AI Training

TensorFlow: TensorFlow is an open source machine learning framework developed by Google. It’s widely used for training AI models in AIOps. TensorFlow offers a comprehensive set of tools and libraries, making it suitable for various machine learning applications.

PyTorch: PyTorch is another popular open source machine learning library. Known for its dynamic computational graph, PyTorch simplifies the process of building and training complex AI models. It’s favored by researchers and developers for its flexibility and ease of use.

These are the emerging trends in AIOps:

Predictive Analytics and Proactive Issue Resolution

One of the significant trends in AIOps is the shift from reactive to proactive IT management. With predictive analytics, AIOps platforms can analyze historical data, identify patterns, and predict potential issues before they impact the system. This proactive approach enables IT teams to resolve issues before users even notice them, ensuring higher service reliability.

End-to-end Visibility and Integration

AIOps is moving toward providing comprehensive end-to-end visibility into IT environments. This involves integrating with various IT monitoring and management tools to create a unified view of the entire infrastructure. By breaking down silos and providing a holistic perspective, AIOps platforms enable better decision-making and faster issue resolution.

Automated Root Cause Analysis

Traditional troubleshooting often involves a time-consuming process of identifying the root cause of an issue. AIOps platforms are advancing with automated root cause analysis capabilities, leveraging machine learning algorithms to pinpoint the exact source of a problem. This not only accelerates issue resolution but also reduces the workload on IT professionals.

ChatOps Integration

ChatOps, the practice of bringing collaboration into the tools used for infrastructure management, is gaining traction within the AIOps landscape. Integrating AIOps with chat platforms allows for real-time communication and collaboration among IT teams, fostering a more agile and responsive operational environment.

AIOps holds great promise for transforming IT operations. As predictive analytics, automated root cause analysis, and other advanced capabilities become more prevalent, AIOps is poised to revolutionize the way IT professionals manage and optimize complex infrastructures. Embracing these advancements and staying informed about the latest trends in AIOps will be key to staying competitive and successful in the evolving landscape of IT operations.

A key way to take full advantage of AIOps is through the Pure1® AIOps platform, which helps consolidate all your data sources into a single storage management solution. Powered by AI, Pure1 Meta® automates the tracking, monitoring, and analysis of your infrastructure by leveraging predictive analytics to identify issues before they become a problem. Knowledge is power, and Pure1 can help you take back control of your data.