Guide to Everything You Need to Know About AIOps
Get everything you need to know about certain autonomous operations terms.
IT professionals are often faced with the challenge of managing a large number of system alerts and coordinating with different teams to identify and address many IT issues on time. Given the size and complexity of IT infrastructure, managing vast amounts of data and workflows and being prepared to deal with potential issues can be stressful and often lead to undesired outcomes. Application performance monitoring and incident management are two major responsibilities of IT operations teams, and without an effective system in place, responding quickly to tickets can be difficult.
Thanks to AI advances, ITOps and DevOps teams can now tackle and even prevent costly downtime using historical data and real-time data like performance metrics. AIOps solutions offer a data-driven approach to identifying and resolving many common IT issues by using a centralized platform to aggregate crucial data from different sources, analyze it, and provide accurate resolutions and actionable insights to effectively enhance user experience. This post will comprehensively cover what AIOps is, offering numerous examples and use cases to understand how AIOps can streamline and simplify technical and operational business processes.
In this guide, we discuss:
- What is AIOps?
- Why Do We Need AIOps?
- Role of AI in AIOps
- What are the building blocks of AIOps?
- How does AIOps Work?
- Benefits of AIOps?
- Why is AIOps Trending Now?
- The Top 5 AIOps use cases
- What are Examples of AIOps in Different Industries?
- How AIOps is Transforming IT Operations
- How to Define Your AIOps Strategy
Read why Omdia named Digitate AIOps Leader
What is AIOps?
AIOps stands for ‘Artificial Intelligence for IT Operations, which is also known as IT Operations Analytics (ITOA). It refers to the practice of integrating AI and its capabilities, such as Natural Language Processing (NLP) and Machine Learning (ML), to automate and enhance IT operations, including event correlation, anomaly detection, and causality determination. It helps streamline common IT issue identification and resolution using big data analytics.
A key element of any successful AIOps strategy is the early alignment of business goals and technical requirements to ensure that AI-driven insights drive measurable outcomes.
AIOps addresses the challenges that vast amounts of IT data can pose due to its complexity and distributed architectures like multiple cloud setups. By collecting data from different systems and analyzing it, AIOps helps identify issues and their root causes to provide an accurate diagnosis and appropriate solutions. This not only provides IT teams with actionable insights but also simplifies dealing with these issues with automated responses to minimize manual intervention.
With the integration of separate IT operations tools into a single, automated IT system, AIOps empowers IT teams to be proactive in responding to application slowdowns and outages with more readiness to offer resolutions with end-to-end visibility and context. AIOps fills a lacuna in an ever-evolving and diverse IT landscape, breaking down silos on the one hand and fulfilling user expectations for little to no interruption in application performance on the other. AIOps is being seen as the future of IT operations management as the demand is burgeoning, and businesses are increasingly becoming digital transformation-focused.
Evolution of AIOps
About 7-8 years ago, the IT industry started seeing a lot of complexity being built into the enterprise IT landscape – with many applications, huge growth in compute, and technology innovations fueling an ever-expanding interdependent ecosystem. Also, it led to an explosion of digital data as new technologies got adopted. At the same time, there was a lot of diversity as many legacy technologies, platforms, and applications were still retained.
Traditional IT Operations – which usually consist of a siloed organization having various levels of support teams, command centers, and teams that are separated according to the technology towers became sluggish and reactive. IT Operations teams simply could not keep up with the rate of change in the environment and started incurring high costs of running the entire IT Operations.
There was a huge dependency on a few good people in the organization who were always involved in firefighting efforts during outages. Over the years they would have gathered a lot of tacit knowledge about the infrastructure and when these people left the organization, they created a big void that was difficult for other members of the operations teams to fill. Hence, it became crucial to digitize a lot of this tacit knowledge.
Hence there was an immediate need to reimagine the entire idea of IT Operations and scale up the IT Operations functions to support not just IT Infrastructure but also applications to solve business problems.
All of this created the need of having the construct of AIOps which essentially applies machine learning and AI to vast amounts of ITOps data to breakdown the complexity, remove the silos across technology layers, provide intelligent analytics by correlating observational data as well as engagement data, and use automation to eliminate all the repeatable lower order tasks.
Why Do We Need AIOps?
IT teams play an integral role in enhancing business outcomes – by advancing critical digital transformation projects, delivering optimized user and customer experiences, and ensuring availability.
However, IT Ops today needs to deal with:
- Rising complexity. The modern IT landscape is a mixture of legacy systems, including on-premises mainframes and distributed systems, as well as new technologies, such as containers, cloud, virtual, and software-defined components making it difficult to analyze information across layers.
- Increase alerts. An increased number of monitoring tools for different technologies lead to a huge number of inaccurate and redundant alerts. This complicates operations and increases the time to identify the root cause of issues across systems and domains.
- Dynamic systems. In recent years, the use of containerized applications and microservices has significantly increased complexity due to the dynamic nature of operations.
- Data deluges. The volume, variety, and velocity of data that needs to be managed, correlated, and analyzed continues to grow dramatically.
To deal with such complexities, it is no longer enough to react when issues arise. Teams must gain the visibility needed to identify potential issues—and address them before they affect service levels. To contend with the explosive growth in data, complexity, and user demands, IT teams need to adopt an AIOps platform.
As part of your AIOps strategy, teams should define the scope, select appropriate platforms, and ensure data governance to support proactive issue detection and resolution.
As IT infrastructures evolve, old rules-based systems fall short because they rely on a pre-determined, static representation of a mostly homogeneous, self-contained IT environment.
AIOps uses AI and Intelligent Automation to provide a single-source-of truth for all ITOps processes and detailed root-cause analysis for any IT event, help predict probable incidents, provide intelligent recommendations for fixes, and enable proactive automation to improve the performance of digital services.
Role of AI in AIOps
AIOps rely on the maturity of specific AI models to provide intelligence and visibility to IT operations teams, as well as to provide intelligent resolutions to common IT issues.
Instead of having to rely on IT engineers to identify a problem with an application and fix it manually, AIOps can use algorithms to identify and resolve the problem automatically. Likewise, rather than requiring IT staff to determine how best to manage application performance or how many resources to allocate to it, a platform can provision environments automatically by parsing data to determine the optimal mix of resources.
Algorithms can pick out significant alerts from a noisy event stream, identify correlations between alerts from diverse sources, assemble the correct team of IT specialists to diagnose and resolve a situation, propose probable root causes and practical solutions based on past experiences, and learn from feedback to improve continuously over time.
Clustering and correlation are the most complex and crucial steps, requiring multiple different approaches. A combination of historical pattern-matching and real-time identification helps identify both recurring and net-new issues.
The most advanced AIOps platforms leverage a combination of various types of reasoning to ensure optimal outcomes:
Rule-based Reasoning
This is also known as the ‘traditional’ approach. It refers to explicit user–defined rules which are required to make a decision.
Case-based Reasoning
Case–based reasoning is completely dependent on data and is more adaptive to the changing scenario. The system learns frequent, dominant, and recent cases from historical data and derives patterns. The analysis, predictions, and recommendations based on historical occurrences, frequencies, and relationships are an outcome of “Case-based Reasoning”.
Model-based Reasoning
Model-based reasoning is primarily driven by two things – the situational data (CMDB (configuration management database), influencers based on relationship data, inventory list, and so on) and the factual data comprising of technology model or Meta mode. AIOps leverage the structural context and behavioral patterns of the systems and apply reasoning logic for deciding the course of action
Type of Reasoning | Business Significance | AI/ML used | Cognitive |
Model based | Game changer | Yes | Real |
Case based | Smart | Yes | Limited |
Rule based | Traditional | No | No |
Anchoring your AIOps strategy to these reasoning models ensures that your platform can adapt from simple rule-based alerts to sophisticated, context-driven automation.
What are the building blocks of AIOps?
The 6 Elements of AIOps
- Extensive and Diverse IT Data – AIOps brings together diverse data from both IT operations management and IT service management. This is often referred to as breaking down data silos, bringing data together from disparate tools so they can speak to each other and accelerate root cause identification, and eventually enable automation.
- Aggregated Big Data Platform – At the heart of the platform is big data. As the data is liberated from siloed tools, it needs to be brought together to support next-level analytics. This needs to occur not just offline, as a forensic investigation using historical data, but also in real-time as data is ingested.
- Machine Learning – Big data enables the application of machine learning to analyze vast quantities of diverse data. This is not possible prior to bringing the data together or by manual human effort. Machine learning automates existing manual analytics and enables new analytics of new data all at a scale and speed unavailable without AIOps.
- Observe – This is the evolution of the traditional ITOM domain that integrates development and other non-ITOM data to enable new models of correlation and contextualization. In combination with real-time processing, probable cause identification becomes simultaneous with issue generation.
- Engage – The evolution of the traditional ITSM (Information Technology Service Management) domain includes bidirectional communication with ITOM data to support the above analysis. Artificial intelligence or machine learning expresses itself here in cognitive classification plus routing and intelligence at the user touchpoint. An example of this is a chatbot.
- Act – This is the final mile of the AIOps value chain. Automating analysis, workflow, and documentation is all part of AIOps. Act encompasses the qualification of human domain knowledge into the automation and orchestration of remediation and response.
Tip: Your AIOps strategy should map each of these building blocks to specific use cases and KPIs, ensuring end-to-end visibility and accountability.
Read why Omdia named Digitate AIOps Leader
How does AIOps Work?
To grasp how AIOps works, understanding the role of each AIOps component technology, i.e., big data, machine learning, and NLP, is crucial. AIOps utilizes a big data platform to collect distributed IT operations data, teams, and tools into one location. This data typically includes historical performance and event data, real-time operations events, system logs and metrics, network data, incident-related data and ticketing, application demand data, and infrastructure data. Once this is done, AIOps will use advanced analytics, machine learning, and NLP capabilities to ‘observe,’ ‘engage,’ and ‘act.’ This is how this step-by-step process works:
Data Collection and Performance Analysis (Observe)
The AIOps system will collect, process, and analyze real-time data from different sources, such as traditional IT monitoring, log events, and network traffic. The collected data can be structured or unstructured. Next, the system will pinpoint and categorize abnormalities with anomaly detection, pattern detection, and predictive analytics. This step helps separate real issues from noise to reduce alert fatigue and false alarms, apprising IT teams of problems that need resolution.
Inference and Root Cause Detection (Engage)
AIOps does root cause analysis to understand why current issues were caused. Since anomalies will be categorized, IT teams can attempt to resolve the problems and prevent them from recurring in the future. Regardless of their locations, the relevant teams will be notified about the problems and possible resolutions so that they can work together to minimize common performance issues and bottlenecks.
Response Automation and Collaboration (Act)
When issues and possible resolutions are regularly routed to relevant IT teams, AIOps enhances collaboration among teams while speeding up response time with automation. These responses can be resource scaling, rebooting a service, or executing predefined scripts to address problems. This adaptive learning from IT teams’ actions enables AIOps to remediate issues even before end users and businesses become aware of them.
Implementing an AIOps strategy involves defining clear data pipelines, choosing appropriate ML models, and integrating AI-driven workflows into existing ITSM and ITOM processes.
What are the benefits of AIOps?
- End-to-end visibility into company applications and infrastructure
- Improved performance monitoring
- Faster Mean Time to Resolution (MTTR)
- Noise reduction
- Increased company-wide collaboration
- Helps IT leaders optimize their spending on cloud usage and software to provide what the business needs when it needs it
- Breakdown of data silos
- Simplified root cause analysis
- Seamless customer experience
- Reduction of IT service ticket volumes
- Predictive and proactive IT self-healing
Why is AIOps trending now?
Machine learning and AI algorithms are becoming increasingly prominent in business operations as they have demonstrated the ability to streamline and complete manual tasks more efficiently and cost-effectively at scale. IT operations teams particularly face challenges while collecting and processing large amounts of big data and finding the root cause of issues. AIOps proves instrumental in overcoming these challenges as it can handle the speed, scale, and complexity of digital transformation, and due to this, AIOps has gained popularity in the last five years.
AIOps tools can analyze more performance data, which is generated through IoT devices, APIs, mobile applications, and digital or machine users. According to Splunk, an AIOps vendor, 73% of this data remains unused by ITOps teams. AIOps can address this issue by continually and automatically processing the data. By using and analyzing this unused data, AIOps can help IT teams gain a better understanding of the impact of incidents. For example, if an ERP system is down, AIOps prioritizes the issue by using machine learning algorithms. This correlates to a shorter response time, which end users expect when faced with an issue. Companies can thus detect and respond to issues more promptly and cut down on their mean time to resolution (MTTR).
ITOps teams are in charge of the overall health of the IT ecosystem and ensuring the interaction between different applications and services is smooth. AIOps can make this a seamless process by understanding situations in IT systems. They run root-cause analyses and analyze the collected data to offer actionable insights to IT teams.
AIOps provides a better experience handling IT operations than traditional ITOps technologies as it requires minimal human intervention to make updates to infrastructures amidst dynamic environments. New technologies are no longer difficult to integrate with ITOps tools, as these integrations are automatically completed by AIOps. As we are aware, IT operations tools deal with thousands of events (monitoring noise from across the IT estate), both on-premise and in the cloud, AIOps can reduce this noise by 99%, helping businesses filter and zero in on the main issues.
The Top 5 AIOps use cases?
AIOps helps manage IT operations effectively and reduce the overall IT budget by leveraging AI technologies. Here are the top 5 AIOps use cases. The main purpose of AIOps is to optimize IT operations. By providing visibility and automation, AIOps can drive important business and IT innovations. The following article shares the top 5 AIOps use cases.
AIOps Use Case #1
The first AIOps use case is anomaly or threat detection. AIOps tools are valuable contributions to the making of a strong security management posture. Established processes and algorithms sift through traffic data to identify any botnets, scripts, or other threats that can take down a network. This can be incredibly helpful since many threats are complex, multi-vector, and unique. AIOps leverages machine learning to expose patterns that can undermine business service availability.
AIOps Use Case #2
The next AIOps use case is event correlation. Infrastructure teams are faced with numerous alerts when only a handful really matter. AIOps identifies the important alerts, groups them together using inference models, and identifies the core root causes of the problem. This means your infrastructure teams will no longer have overloaded inboxes filled with alert emails and get the one or two notifications that really matter instead.
AIOps Use Case #3
The third AIOps use case is intelligent alerts and escalation. After issues are identified by root cause alerts, ITOps teams leverage artificial intelligence to automatically notify subject matter experts or incident response teams to quickly resolve the problem. Artificial intelligence starts the remediation process prior to anyone even getting involved. Many AIOps tools continuously monitor hardware using machine learning to predict errors based on previous and real-time data prior to their occurrence. A ticket with all the necessary details on how to resolve the issue is automatically sent to inform you of the issue.
AIOps Use Case #4
The fourth AIOps use case is incident auto-remediation. AIOps is used as an end-to-end bridge between IT service management and IT operation management tools. IT service management teams traditionally sift through infrastructure data to identify and resolve root-cause issues. AIOps understands the root cause through inference from infrastructure alerts and sends them to the IT service management team or tool through API integration pathways.
AIOps Use Case #5
The last AIOps use case is capacity optimization. This includes predictive capacity planning and references statistical analysis or AI-based analytics to optimize application availability and workloads across infrastructure. Capacity optimization continuously monitors raw utilization, bandwidth, CPU, memory, and others to increase overall application uptime.
AIOps helps manage your IT operations effectively and reduce the overall IT budget by leveraging AI technologies to bring efficiencies to ITOps. Problems are resolved automatically within complex modern IT environments.
AIOps is important because it uses machine learning and data science to provide modern ITOps teams with a real-time understanding of any type of issue. Traditional IT management solutions typically can’t keep up with the sheer volume of issues while at the same time providing real-time insights or predictive analysis. According to Gartner, 4 out of 10 organizations are expected to strategically implement an AIOps platform to enhance performance monitoring by 2022.
Read why Omdia named Digitate AIOps Leader
What are Examples of AIOps in Different Industries?
AIOps is highly versatile in that it extends well beyond different use cases to support a wide range of industry-specific and vertical applications. Below are some examples of these industry-specific applications:
Retail
In retail, AIOps links data from different monitoring tools to demonstrate how IT issues can impact sales and customer experiences, both in-store and during online transactions. For instance, if online transactions fail, it can lead to unsuccessful purchases and often a dip in customers’ interest in purchasing from you again. AIOps can gather and analyze data related to this to provide valuable insights into revenue implications and how online payment processing can be improved.
Gaming
For the gaming industry, AIOps can be helpful for companies to correlate alerts from monitoring tools to understand how systems are used and if players are able to purchase digital goods on their platforms. This gives companies valuable information about players’ experience with their platforms and how this experience can be improved.
Travel
In the travel industry, AIOps is instrumental in linking booking numbers and transactions with the health indicators of the system’s events and performance. This correlation helps travel companies learn how the system’s well-being impacts the booking activities, helping them find a more efficient and responsive approach to resolving any issues that may arise in the travel booking process.
Brokerage
In online trading and brokerage, AIOps plays a pivotal role in connecting trading volumes, customer satisfaction, and latency. This allows trading platforms to understand the relationship between the number of trades, customer experience, and the speed of transactions, helping them optimize the trading experience for customers by identifying and resolving any issues that may impact or hinder smooth online trading.
How ignio AIOps is Transforming IT Operations
ignio AIOps platform combines artificial intelligence and machine learning through automation. ignio first mines different data sources within an enterprise to learn cross-layer technology dependencies and component behaviors. ignio then leverages contextual awareness to mimic human behavior in handling situations. It isolates the root cause for an observed IT fault, prescribes the best fix, and applies it autonomously for full recovery.
ignio proactively checks the health of business-critical technology components, identifies potential hotspots, and recommends options to prevent any business disruption with the appropriate action. The outcome is highly resilient, agile, and efficient IT operations that allow enterprises to cash in on business opportunities and run their operations optimally.
By embedding a robust AIOps strategy, ignio enables organizations to shift from reactive firefighting to proactive, predictive operations—melding AI, automation, and human expertise into a cohesive framework.
Are you ready to reimagine your enterprise IT operations? Get an ignio AIOps demo and discover how a well-crafted AIOps strategy can drive your digital transformation.
