AIOps Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, 2021-2031

The Global AIOps Market is projected to expand significantly, growing from a valuation of USD 2.27 Billion in 2025 to USD 6.36 Billion by 2031, reflecting a Compound Annual Growth Rate (CAGR) of 18.73%. AIOps, or Artificial Intelligence for IT Operations, utilizes artificial intelligence and machine learning algorithms to automate and upgrade IT operational workflows. By analyzing massive quantities of data produced by hardware and software components, these solutions detect anomalies, forecast potential outages, and perform root cause analysis with minimal human intervention. This market growth is primarily fueled by the increasing intricacy of modern hybrid cloud environments and the unmanageable volume of alerts, which necessitate intelligent automation to ensure system reliability and operational efficiency.

Despite these clear benefits, the market encounters obstacles regarding the successful deployment of such complex systems. A major hurdle slowing market expansion is the difficulty organizations face in integrating fragmented data silos to generate the high-quality datasets needed for accurate algorithmic processing. According to CompTIA, in 2024, 62 percent of technology companies planned to increase their adoption of artificial intelligence to handle routine tasks and accelerate automation. While this indicates robust demand, actualized growth depends on enterprises resolving internal data governance issues and addressing the technical skills gap required to manage these advanced platforms.

Market Drivers

The escalating complexity of hybrid and multi-cloud IT architectures serves as a primary catalyst for the Global AIOps Market. As enterprises distribute their infrastructure across on-premises data centers and various cloud platforms, the resulting operational noise creates a manageability crisis that traditional monitoring tools are unable to address.

This architectural sprawl demands intelligent solutions capable of ingesting and correlating immense volumes of telemetry data to maintain system stability. According to the 'The State of Observability 2024' report by Dynatrace in March 2024, 88 percent of organizations experienced an increase in the complexity of their technology stack over the previous year. Consequently, businesses are aggressively deploying AIOps to interpret these intricate environments, ensuring critical infrastructure remains visible and manageable despite the rapid expansion of digital touchpoints.

Concurrently, the market is driven by the imperative for proactive incident management and accelerated resolution times to minimize costly service disruptions. Organizations are prioritizing AIOps to transition from reactive troubleshooting to predictive remediation, drastically reducing Mean Time to Resolution (MTTR) and enhancing service availability. This operational shift delivers tangible reliability gains; according to New Relic's '2024 Observability Forecast' released in September 2024, organizations that achieved full-stack observability experienced 79 percent less downtime annually compared to those without such capabilities. The widespread recognition of these efficiency improvements has solidified AI integration as a standard for modern operations, as evidenced by Splunk's 2024 finding that 97 percent of surveyed respondents utilized artificial intelligence and machine learning to enhance their observability operations.

Market Challenges

The inability to effectively integrate fragmented data silos represents a substantial barrier to the progress of the Global AIOps Market. AIOps platforms depend heavily on the ingestion of comprehensive, high-quality datasets to train machine learning algorithms and execute accurate root cause analyses. When organizational data is trapped within distinct legacy systems or departmental pockets, these platforms lack the holistic context necessary to identify anomalies or predict outages with precision. This fragmentation directly compromises the reliability of algorithmic outputs, rendering automation less effective and reducing the overall value proposition for enterprises.

This issue of data quality and unification significantly restricts market scalability. According to the Association for Intelligent Information Management, in 2024, 52 percent of organizations reported encountering challenges related to data quality and categorization during the implementation of artificial intelligence systems. Such obstacles force companies to dedicate excessive resources to manual data cleansing rather than operational innovation. Consequently, the complexity of bridging these technical gaps discourages widespread adoption and delays the realization of return on investment for AIOps deployments.

Market Trends

The integration of Generative AI and Large Language Models is fundamentally reshaping the Global AIOps Market by transitioning platforms from passive monitoring tools into active, conversational assistants. Unlike traditional predictive models that rely solely on numerical metrics, these generative systems can synthesize unstructured data to generate automated remediation scripts, summarize complex incident logs, and draft post-mortem reports in natural language. This capability significantly lowers the barrier to entry for non-technical staff and accelerates the development of automation playbooks. The momentum behind this trend is evident in enterprise strategies, as according to IBM's 'Global AI Adoption Index 2023' released in January 2024, 33 percent of surveyed enterprises identified the automation of IT processes as a key driver for their artificial intelligence adoption.

Simultaneously, the convergence of AIOps with observability and security frameworks is driving the market toward a unified DevSecOps approach. As cyber threats become more complex, organizations are abandoning isolated security tools in favor of integrated platforms that correlate performance anomalies with potential security breaches in real-time. This holistic visibility ensures that vulnerability management is embedded directly into the continuous delivery pipeline, preventing risks before they impact end-users. This strategic alignment is becoming a top priority for leadership, and according to the '2024 CISO Report' by Dynatrace in May 2024, 71 percent of Chief Information Security Officers stated that DevSecOps automation is critical to minimizing application security risk and ensuring robust defense measures.

Key Players Profiled in the AIOps Market

• AppDynamics
• BMC Software, Inc.
• HCL Technologies Limited
• International Business Machines Corporation
• Micro Focus
• Moogsoft Inc.
• ProphetStor Data Services, Inc.
• Resolve Systems, Splunk Inc.
• VMware, Inc.

Report Scope

In this report, the Global AIOps Market has been segmented into the following categories:

AIOps Market, by Offering:

• Platform
• Service

AIOps Market, by Application:

• Infrastructure Management
• Application Performance Analysis
• Software Asset Management
• Network & Security Management
• Others

AIOps Market, by Deployment:

• On-Premise
• Cloud

AIOps Market, by Enterprise Size:

• Large Enterprises
• Small
• Medium-Sized Enterprises (SMEs)

AIOps Market, by vertical:

• BFSI (Banking
• Financial Services
• Insurance)
• Healthcare & Life Sciences
• Retail & E-Commerce
• IT & Telecom
• Energy & Utilities
• Government & Public Sector
• Media & Entertainment
• Others

AIOps Market, by Region:

• North America
• Europe
• Asia-Pacific
• South America
• Middle East & Africa

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global AIOps Market.

Available Customization

The analyst offers customization according to your specific needs. The following customization options are available for the report:

• Detailed analysis and profiling of additional market players (up to five).

This product will be delivered within 1-3 business days.