Global Generative AI in IT Operations Market Size Likely to Grow at a CAGR of 32.82% By 2034

Market Size and Growth

As per the Generative AI in IT Operations Market size analysis conducted by the CMI Team, the global Generative AI in IT Operations Market is expected to record a CAGR of 32.82% from 2025 to 2034. In 2025, the market size is projected to reach a valuation of USD 2.26 Billion. By 2034, the valuation is anticipated to reach USD 29.13 Billion.

Overview

According to industry experts at CMI, the implementation of new strategies and technologies by service providers presents lucrative opportunities for players in the Generative AI in IT Operations Market during the forecast period. Furthermore, the growing significance of organized retailing is expected to drive the future growth of the market.

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 huge 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 make a forecast of the associated risks before any change goes to production. This, therefore, complements the trend surrounding the rise of agile methodologies, where fast 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.

• 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. Amongst key decision-making processes of 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, this 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. This democratization of AI is opening avenues beyond what was previously just big corporate-favored. The adoption of IT in Operations-related tools by SMEs is expected to increase dramatically over the next few years as SMEs continue on 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. Henceforth, with the mounting sophistication and frequency of cyber threats, IT teams are pressured to secure environments successfully. AI models also offer the ability to analyze security logs, spot anomalies, and simulate attack scenarios, thus recommending some proactive actions. The presence of generative AI within SecOps allows threats to be detected at speed and shortens response time. All around, as organizations give precedence to cybersecurity and compliance, this merging of IT operations and security through AI-driven platforms is quickly becoming a generational-level growth driver in the market today.

• 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

SWOT Analysis

• Strengths: Generative AI can automate a wide range of repetitive and time-consuming IT tasks, including ticket classification, alert triage, log analysis, code generation, and even automated incident resolution. By leveraging large language models (LLMs) trained on historical incident data and logs, Generative AI can analyze patterns, predict potential failures, and suggest preventive measures before problems escalate into outages. Generative AI solutions, especially cloud-based ones, are highly scalable and can dynamically adapt to manage complex, distributed systems in hybrid and multi-cloud environments.

• Weakness: Training and deploying large generative AI models require substantial computational resources (e.g., high-performance GPUs) and significant financial investment. Generative AI models are highly dependent on high-quality, diverse, and well-structured training data. Integrating Generative AI tools with existing legacy IT systems, workflows, and tools can be complex and challenging, requiring significant effort and expertise.

• Opportunities: As enterprises continue to expand their cloud footprints, the complexity of managing distributed systems will drive further demand for intelligent automation offered by Generative AI. As cloud-based, plug-and-play Generative AI solutions become more accessible and affordable, opportunities are opening up for adoption beyond large enterprises. Enhancing self-service capabilities for users and assisting IT support agents through intelligent chatbots and virtual assistants is a significant area of growth.

• Threats: The fast pace of innovation in Generative AI means that solutions can quickly become outdated, requiring continuous investment in R&D and updates. Evolving laws and regulations around AI, data privacy, and ethical use can create compliance challenges and increase the cost of development and deployment. Existing AIOps solution providers are rapidly integrating Generative AI into their offerings, creating a competitive landscape.

List of the prominent players in the Generative AI in IT Operations Market:

• Amazon Web Services (AWS)
• AppDynamics
• BMC Software
• Broadcom
• Dynatrace
• Freshworks
• Google Cloud (Alphabet)
• IBM
• Micro Focus
• Microsoft
• Moogsoft
• Oracle
• PagerDuty
• ServiceNow
• Splunk (Cisco)
• Others

The Generative AI in IT Operations Market is segmented as follows:

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

Regional Coverage:

North America

• U.S.
• Canada
• Mexico
• Rest of North America

Europe

• Germany
• France
• U.K.
• Russia
• Italy
• Spain
• Netherlands
• Rest of Europe

Asia Pacific

• China
• Japan
• India
• New Zealand
• Australia
• South Korea
• Taiwan
• Rest of Asia Pacific

The Middle East & Africa

• Saudi Arabia
• UAE
• Egypt
• Kuwait
• South Africa
• Rest of the Middle East & Africa

Latin America

• Brazil
• Argentina
• Rest of Latin America