AIOps: The Digital Revolution in IT Operations
In the ever-evolving IT landscape, the sheer volume of data generated can be overwhelming. Traditional IT workflows that rely on manual processes and human intervention often cannot keep up with the pace and complexity of modern digital environments. This is where AIOps promise a new approach to solving this problem. AIOps promise to create smarter, more efficient, and more proactive IT ecosystems by combining artificial intelligence (AI) and IT operations.
At its core, AIOps are more accurately called artificial intelligence for IT operations a way to provide IT systems with brain-like functionality. This enables AIOps to sift through data, make decisions and even predict future challenges.
You can also find a good explanation of AIOps in the following video from IBM:
So how do AIOps achieve their predictive power? To answer this question we would like to provide you with one example of how AIOps operate, found in Vijay Kanade article “What Is Artificial Intelligence for IT Operations (AIOps)? Meaning, Tools, and Use Cases”.
Accordingly, AIOps platforms, e.g., initiate their process by:
(1) Data Collection: AIOps platforms gather data from various sources within an IT environment, including logs from applications and systems, metrics from monitoring tools, and user interaction data.
(2) Data Aggregation and Correlation: The collected data is then aggregated and correlated to provide a unified view of the IT environment, combining both structured and unstructured data.
(3) Pattern Recognition and Anomaly Detection: Machine learning algorithms are applied to analyze the data, establishing baseline performance metrics and detecting anomalies.
(4) Root Cause Analysis: AIOps platforms can determine the underlying causes of incidents by correlating events and metrics.
(5) Predictive Analytics: By utilizing historical data, AIOps can predict future incidents or performance trends.
(6) Automation and Remediation: Routine tasks are automated, and incident resolution is assisted, either by triggering automated responses or providing recommendations to IT teams.
This being said the use cases for AIOps are diverse, hence the way they operate may also vary.
Typical use cases for AIOps include optimizing network performance, managing cloud resources, improving IT service desks through automation, monitoring application performance, and more. Additionally, we believe that AIOps (will) play a central role in cybersecurity by detecting threats, monitoring data center health, helping with capacity planning, streamlining DevOps processes, ensuring optimal digital user experiences, and automating routine IT infrastructure tasks. Furthermore, we see another use case in the financial and healthcare sectors, where AIOps can help secure digital services and provide early detection and warning of anomalies.
The AIOps market
The AIOps market is undergoing rapid change and growth. According to a market research report by Sheer Analytics and Insights, the global AIOps platform market was valued at $3.2 billion in 2020 and is expected to reach an impressive $23.3 billion by 2031. This growth is driven by the increasing complexity of IT environments, with the advent of cloud computing, IoT devices, and growing reliance on digital services. Traditional IT management methods are becoming obsolete, and AIOps platforms provide a comprehensive view of the IT ecosystem, ensuring timely identification and resolution of issues.
The competitive landscape in the AIOps market is therefore very dynamic. Notable players in the market include IBM, Splunk, ServiceNow, AppDynamics, BMC Software, Inc, Broadcom, HCL Technologies Limited, Micro Focus, Moogsoft, ProphetStor Data Services, Inc, Resolve Systems, VMware, Inc, CA Technologies, FixStream, and Correlsense. All of these companies are developing targeted AIOps solutions tailored to meet the diverse needs of the industry, and we expect more innovative features to emerge as the market matures.
The landscape of AIOps
The AIOps landscape is where established tech experts and new innovators come together to shape the future of technology. These platforms use advanced machine learning, not just to add another tool to the IT toolbox, but to help businesses act before problems arise, rather than just reacting to them. Beyond the tech side of things, what’s really special about these platforms is how they fit smoothly into current IT systems, giving a clear view of everything from day-to-day monitoring to advanced automation. While AIOps is a big change that has made some companies skeptical about the amount of trust one should have in these upcoming technologies, history showed us that embracing new ways of doing things is often key to long-term success. Therefore, we consider it important to take a closer look at the products on the market.
Therefore, we choose to provide some examples of current AIOps tools here:
- IBM Watson AIOps uses natural language processing to interpret unstructured data, enabling the analysis of textual information like incident descriptions. It provides automated root cause analysis by leveraging AI and machine learning, correlating data from various sources to quickly identify the underlying causes of IT issues.
- Moogsoft utilizes machine learning and advanced correlation techniques to detect potential incidents early, ensuring system uptime. By leveraging AI and ML, it identifies patterns to prevent recurring issues. The platform also automates workflows for efficient incident routing, remediation, and closure, emphasizing its expertise in service assurance, especially in cloud environments with microservice and ephemeral architectures. This approach empowers organizations to resolve issues confidently, save time, and drive innovation.
- Dynatrace: offers unified observability and security through advanced analytics and automation. It uses machine learning to proactively detect incidents, ensuring system uptime. The platform provides insights across infrastructure, applications, and user experiences, and its AI-driven capabilities help businesses swiftly identify and resolve issues, optimizing both performance and security in cloud-native environments
In our view, AIOps is a game-changer for the tech world. It has the power to make IT operations smarter and more efficient. However, because it’s a complex technology, it might take some time for many businesses to fully adopt and use it. While we’re excited about its potential, we understand that good things often take time to develop and be widely accepted.
About the author:
Guido Perscheid works as a Business Development Manager for the Center for Deep Tech Innovation and is pursuing his PhD at the Chair of Business Informatics at the University of Bamberg. Guido is an expert in innovation management with a focus on technology implementation in the fields of Blockchain, Artificial Intelligence, Internet of Things and Climate Technology.
