[Latest] Global Generative AI in IT Operations Market Size/Share Worth USD 29.13 Billion by 2034 at a 32.82% CAGR: Custom Market Insights (Analysis, Outlook, Leaders, Report, Trend, Forecast, Segmentation, Growth Rate, Value, SWOT Analysis)

Austin, TX, USA, Aug. 13, 2025 (GLOBE NEWSWIRE) — Custom Market Insights has published a new research report titled “Generative AI in IT Operations Market Size, Trends and Insights By Component (Software, Services), By Deployment Mode (Cloud-Based, On-Premises, Hybrid), By Enterprise Size (Small and Medium Enterprises (SMEs), Large Enterprises), By Application (Anomaly Detection & Incident Management, Root Cause Analysis (RCA), Capacity Planning, Change Risk Analysis, Intelligent Alerting, Predictive Analytics & Forecasting, Automation of IT Tasks, Log Analysis & Monitoring, Security & Compliance Automation), and By Region – Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025–2034“ in its research database.

“According to the latest research study, the demand of global Generative AI in IT Operations Market size & share was valued at approximately USD 1.71 Billion in 2024 and is expected to reach USD 2.26 Billion in 2025 and is expected to reach a value of around USD 29.13 Billion by 2034, at a compound annual growth rate (CAGR) of about 32.82% during the forecast period 2025 to 2034.”

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Overview

According to experts at CMI, service providers using new strategies and technologies offer great chances for businesses in the Generative AI in IT Operations Market in the coming years. Furthermore, we expect the growing significance of organized retailing to drive the market’s future growth.

Key Trends & Drivers

• Shift Toward Autonomous IT Operations: Increasingly, organizations are moving into autonomous IT operations where systems self-monitor, heal, and optimize with the minimum amount of human intervention. Generative AI, in this particular scenario, plays a substantial role in producing incident-response suggestions, predicting issues, and performing all sorts of routine maintenance-type work. The trend is supported by the intention to free up manual workloads, facilitate faster response times, and ensure that availability is at an all-time high. In such an IT operation environment, autonomy certainly comes into force, especially in very large-scale, distributed environments where manual management is next to impossible. While enterprises are gearing up for operational resiliency and cost efficiency, this trend toward AI-led autonomy strengthens the demand for generative AI applications in IT operations.

• Integration with DevOps and CI/CD Pipelines: Generative AI is being channeled into DevOps and Continuous Integration/Continuous Deployment (CI/CD) pipelines to enhance software delivery speed and reliability. By running tests, validating deployment, and monitoring performance, generative AI acts to ensure smooth rollouts and quick resolutions to arising issues. The AI can simulate the outcome of a deployment and even forecast the associated risks before any change goes to production. This, therefore, complements the trend surrounding the rise of agile methodologies, where rapid iterations and little-to-no downtime are essential. Hence, the compatibility of development with IT operations continues to rank high as a strategy in the adoption of AI-powered DevOps integration, which feeds into market growth in the generative AI space.

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• Rise of Explainable and Transparent AI Models: The increasing demand for explainable and transparent AI is expected to propel the demand for the growth of the market. Among key decision-making processes in IT operations, a stakeholder needs to know what AI has done and the reason for it. Vendors are responding with models that do not only resolve problems but also increase explainability by providing an audit trail and pertinent context. It does build trust and encourages collaboration between human operators and AI systems. In regulated industries, such collaboration is vital, where accountability or compliance is absolutely necessary. Explainable generative AI enhances the confidence of users and promotes wider use in sensitive and complex IT environments.

• Increased Use by Small and Medium Enterprises (SMEs): Whereas large enterprises led the initial adoption, SMEs seem to be now stepping in. Greater availability of cheaper AI tools and easier-to-use interfaces means smaller businesses are harnessing the tools now to perform IT operations, lessen the incidence of failure, and reduce the cost of operations. Cloud-hosted solutions, pay-as-you-go price models, and low-code/no-code builders make generative AI very feasible for smaller businesses. The democratization of AI is creating opportunities that were previously exclusive to large corporations. The adoption of IT in operations-related tools by SMEs is expected to increase dramatically over the next few years as they continue their digital transformation journey.

• Expansion of AI-Driven Security Operations (SecOps): Increasingly, generative AI finds application in Security Operations (SecOps) for the identification of threats and predictions of vulnerabilities, as well as to help frame automated responses. IT teams are now under pressure to successfully secure environments due to the increasing sophistication and frequency of cyber threats. AI models can also analyze security logs, identify anomalies, and simulate attack scenarios, thereby recommending proactive actions. Generative AI within SecOps enables rapid threat detection and reduces response time. Overall, as companies focus more on cybersecurity and following rules, the combination of IT operations and security through AI-driven platforms is rapidly becoming a major factor for growth in today’s market.

• Real-Time Data Processing and Edge Intelligence: With the surge of IoT and edge computing technologies, now IT environments are more decentralized and in need of real-time monitoring and decision-making at the edge. Generative AI is being leveraged to work in these situations to perform local analysis, incident detection, and remediation, therefore reducing latency and enabling business continuity for critical applications. The real time data processing further supports the systems that are capable of adapting dynamically to performance conditions. With the increased demand for responsiveness and low-latency operations from sectors such as manufacturing, logistics, and telecom, generative AI working on the edge is gaining momentum toward becoming a massive trend for innovation and market expansion.

Report Scope