Generative AI in IT Operations Market Size, Share, Growth, Report 2025 to 2034

Generative AI in IT Operations Market Size and Growth 2025 to 2034

The global generative AI in IT operations market size was valued at USD 1.76 billion in 2024 and is expected to be worth around USD 29.91 billion by 2034, exhibiting a compound annual growth rate (CAGR) of 32.74% over the forecast period 2025 to 2034.

The generative AI in IT operations market is primarily driven by the growing need for automation and intelligent decision-making across complex IT infrastructures. With the increasing scale of digital changes, organizations face huge amounts of IT events, alerts, and data logs, which are difficult and error-prone to monitor manually. The generative AI Real-Time Route Cost Analysis enables automated resolution by detecting and generating scripts, summaries, and recommendations, which reduces downtime and operating costs. Additionally, the integration of large language models (LLM) in IT operating equipment enhances broad adoption, enabling natural language questions and automated documents.

A major growth factor is the increasing demand for cost-efficient and scalable solutions in hybrid and multi-cloud environments. As enterprises expand their cloud footprints, managing distributed systems requires intelligent devices that can dynamically adapt. Generative AI fills this gap by offering reference-awareness and automation. A recent example of this is the launch of ServiceNow's "New Assist" in 2024, which uses generative AI to provide a ticket summary and resolution suggestion. This innovation has helped reduce the time of IT service resolution by 40% in pilot enterprise settings, demonstrating the tangible effects of generative AI on operational efficiency.

What is a Generative AI in IT Operations?

Generative AI in IT Operations refers to the application of generative artificial intelligence technologies, such as large language models (LLMs), natural language processing (NLP), and generative neural networks, to automate, optimize, and enhance IT operational processes. These systems go beyond traditional surveillance by generating materials such as resolution scripts, event summaries, future state models, and intelligent alerts. Major applications include automatic event reaction, root cause analysis, smart alerting, predictive maintenance, log analysis, and documentation generation. This empowers IT teams to reduce manual workloads, respond rapidly to issues, and maintain high system availability.

Adoption Trends of Generative AI in IT Operations:

Generative AI in IT Operations Market Report Highlights

• North America has held dominant position, accounting for revenue share of 47% in 2024.
• Asia-Pacific has reported revenue share of 25% in 2024.
• By component, the software segment has captured revenue share of 60% in 2024.
• By deployment mode, the cloud-based segment dominates the market in 2024.
• By enterprise size, the small and medium enterprises segment is expected to grow at the fastest CAGR during the period.

Generative AI in IT Operations Market Recent Trends

Integration of GenAI with ITSM Platforms

Generative AI is becoming a core feature in modern IT Service Management (ITSM) tools, where it's transforming how incidents are logged, processed, and resolved. Platforms like ServiceNow have integrated GenAI into their ecosystem through products like Now Assist, which automatically generates incident summaries, suggests solutions, and even drafts responses. This has enabled IT teams to reduce manual workload and improve operational efficiency significantly. In 2024, several enterprises reported that Now Assist helped cut incident resolution time by up to 40%, showcasing its impact in real-world IT environments.

Rise of Natural Language Interfaces for ITOps

One of the most user-centric advancements in Generative AI for IT operations is the widespread adoption of natural language interfaces. These interfaces allow IT personnel to interact with systems using plain English commands or questions, making tools more accessible and reducing the learning curve for new users. IBM Watsonx Assistant, for example, allows administrators to ask questions like “Why did server X crash last night?” and receive contextual responses generated by AI. This trend is democratizing IT operations and enabling faster onboarding and decision-making, especially in large-scale support environments.

Predictive Automation Using LLMs

Beyond reactive support, Generative AI is enabling predictive automation in IT operations. By leveraging large language models trained on historical incident data and logs, platforms can now forecast potential failures and suggest preventive measures. Dynatrace’s Davis AI exemplifies this trend by using predictive analytics and GenAI to alert teams before incidents occur and generate remediation scripts on the fly. This shift from reactive to proactive operations is reducing downtime and ensuring higher availability for critical services in sectors like finance, healthcare, and telecommunications.

Multi-Cloud & Hybrid Environment Support

As organizations increasingly operate across multiple cloud platforms and on-premise environments, Generative AI is being adapted to handle these complex infrastructures. Tools like Google Cloud’s Duet AI for DevOps are helping DevOps teams by automatically generating deployment scripts, optimizing resources, and identifying potential misconfigurations across cloud providers. With the ability to understand and manage hybrid workloads, GenAI ensures consistent performance and governance, empowering organizations to scale their operations without increasing risk or manual oversight.

Report Scope

Generative AI in IT Operations Market Dynamics

Market Drivers

• Increasing Complexity of IT Infrastructures: As organizations shift to multi-cloud, edge computing, and contained environments, manually managing IT infrastructure becomes increasingly unstable. The dynamic and distributed nature of the modern environment introduces countless variables that must be monitored in real time. To automate pattern recognition in large datasets, generative AI steps in to generate insights from logs and synthesize reactions, providing a scalable solution for reliability and uptime assurances. Companies like Cisco and IBM are integrating GenAI into their observation and orchestration platforms to support this requirement.
• Demand for Intelligent Automation and Cost Efficiency: The drive for cost optimization is pushing CIOs to adopt AI-first strategies in operations. GenAI L1 and L2 automatically support IT, reduce ticket volume, and handle repeated requests such as password resets, performance diagnosis, and configuration checks. This rapid resolution alleviates the burden on human operators, providing timely and consistent service levels. ServiceNow's assistance and BMC's HelixGPT are already demonstrating significant ROI by combining general reactions with traditional AIOps.

Market Restraints

• Data Privacy and Security Concerns: GenAI tools rely heavily on access to logs, user data, and operational telemetry, raising privacy red flags. If handled inappropriately, these systems can inadvertently leak sensitive data, violate compliance regulations, or become targets for cyberattacks. Especially in industries such as banking or healthcare, IT leaders hesitate to implement GenAI without clear guidelines and transparency. Vendors also express concerns about how to store, train, and manage these systems when integrated into the enterprise.
• High Implementation and Integration Costs: Many IT departments work with legacy systems that lack standardized APIs, data formats, or cloud readiness, which makes integration a complex, time-consuming, and expensive effort. In addition to the software, the infrastructure required to support LLMs (including GPUs, data pipelines, and security layers) adds to capital expenditures. For organizations without a mature digital transformation roadmap, this presents a significant obstacle to adoption.

Market Opportunities

• Expansion into SMB and Mid-Market Segments: Until recently, GenAI solutions in ITOps were geared toward large enterprises with robust budgets and infrastructure. However, the trend toward cloud-based, plug-and-play solutions is opening doors in the mid-market sector. The available platforms now offer sequential, modular, subscription-based GenAI tools for IT admins and managed service providers (MSPs), reducing barriers to entry and expanding the total addressable market.
• Development of Industry-Specific Solutions: Different industries have unique IT operations needs—manufacturing environments may require predictive maintenance, while healthcare systems prioritize compliance and uptime. Vendors are now creating finely-tuned GenAI models that understand domain-specific language, alerts, and system behavior. For example, NVIDIA Clara is being adapted for healthcare ITOps, while GE is exploring GenAI for digital industrial control systems. Solutions tailored to vertical-specific pain points can facilitate market entry and strengthen customer loyalty.

Market Challenges

• Lack of Skilled Talent and Training: Despite growing interest, many organizations lack personnel trained to deploy, fine-tune, and oversee GenAI systems. IT teams may struggle to interpret model outputs, evaluate reliability, or integrate the output into DevOps pipelines. The absence of a structured AI governance framework also complicates adoption. There is an urgent need to bridge the skills gap and develop AI literacy programs while ensuring human oversight for critical decisions.
• Model Reliability, Hallucination, and Compliance Risks: Generative AI models, especially when applied without contextual grounding, can “hallucinate” answers or generate incorrect outputs. In IT operations, this might mean deploying a defective patch or incorrectly executing an event, resulting in outages or data loss. Ensuring that GenAI outputs are clear, understandable, and verified before execution remains a significant challenge. Leading vendors are incorporating human feedback (RLHF) and reinforcement learning with trust layers to address this issue, but universal reliability is still evolving.

Generative AI in IT Operations Market Segmental Analysis

The generative AI in IT operations market is segmented into components, application, deployment mode, enterprise size, and regions. Based on component, the market is classified into software, and services. Based on the application, the market is categorised into 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, and security & compliance automation. Based on deployment mode, the market is categorised into on-premises, cloud-based, and hybrid. Based on enterprise size, the market is classified into small and medium enterprises (SMEs), and large enterprises.

Component Analysis

Software: The software segment is the dominant component in the Generative AI in IT Operations market, driven by its essential role in delivering capabilities such as discrepancy detection, forecasting analytics, and automation. These tools are crucial for modernizing IT operations and are widely adopted by enterprises to enhance operational efficiency and accuracy.

Services: The services segment is the fastest-growing component of this market as enterprises increasingly seek support for integrating and customizing GenAI solutions. With the complexity of implementation and the need for specialized knowledge, the demand for consulting, training, and managed services has risen sharply.

Generative AI in IT Operations Market Revenue Share, By Component, 2024 (%)

Application Analysis

Anomaly Detection & Incident Management: Anomaly detection and incident management is the dominant application segment, as GenAI has the ability to detect and react to issues before they escalate, establishing a foundation for flexible IT operations. This helps reduce downtime, improve reliability, and enhance customer experience.

Automation of IT Tasks: Automation of IT tasks is the fastest-growing application, fueled by the need to reduce manual workloads, eliminate repetitive operations, and cut costs. GenAI automates tasks such as ticket resolution, patch management, and system monitoring, transforming IT efficiency across sectors.

Root Cause Analysis (RCA): Root Cause Analysis benefits from GenAI’s pattern recognition and data correlation capabilities, which accelerate troubleshooting and minimize service disruptions. By quickly identifying the source of issues, it enhances accountability and stability.

Capacity Planning: GenAI improves capacity planning by analyzing historical trends to predict future infrastructure demands. This prevents overprovisioning and resource deficiencies, ensuring cost-effective scalability and resource adaptation.

Change Risk Analysis: In change risk analysis, GenAI evaluates the potential effects of IT changes by referencing historical events and system behavior, helping teams avoid unplanned outages and improve deployment strategies.

Deployment Mode Analysis

Cloud-Based: Cloud-based deployment is both the dominant and fastest-growing subsegment, due to its scalability, low upfront costs, and accessibility. Organizations prefer cloud solutions for rapid deployment and seamless integration with existing equipment.

On-Premises: On-premises deployment remains relevant for organizations prioritizing data sovereignty and control. During slow growth, it provides high adaptability and protection, especially in regulated industries.

Hybrid: Hybrid deployment balances the benefits of both cloud and on-premises models, offering flexibility for organizations with mixed infrastructure needs or compliance requirements.

Enterprise Size Analysis

Large Enterprises: Large enterprises are the dominant users of GenAI in IT operations due to their resources for investing in complex IT infrastructure and advanced AI capabilities. These organizations benefit most from full-scale automation, analysis, and custom integration. 

Small and Medium Enterprises (SMEs): SMEs represent the fastest-growing enterprise segment, driven by accessible, inexpensive, and easy-to-deploy cloud-based GenAI tools. GenAI helps automate SME functions and scale operations without the need for comprehensive IT teams.

Generative AI in IT Operations Market Regional Analysis

The generative AI in IT operations market is segmented into various regions, including North America, Europe, Asia-Pacific, and LAMEA. 

North America: Technological Leadership Driving Early Adoption

The North America generative AI in IT operations market size was valued at USD 0.77 billion in 2024 and is expected to reach around USD 13.16 billion by 2034. North America remains the dominant market, primarily due to its mature digital ecosystem, advanced cloud infrastructure, and high R&D investment. The region is home to leading GenAI vendors like IBM, Google, Microsoft, and ServiceNow, which continuously innovate to deliver robust IT operations tools. Adoption is particularly strong in industries such as BFSI, healthcare, and telecom, where real-time system availability and predictive maintenance are critical. Enterprises in North America also benefit from strong collaboration between academia and industry, producing a skilled talent pool and fostering innovation. However, challenges related to ethical AI deployment and data privacy continue to be closely monitored by regulators and enterprises alike.

Asia-Pacific (APAC): Fastest Growing Region Fueled by Digital Transformation

The Asia-Pacific generative AI in IT operations market size was accounted for USD 0.44 billion in 2024 and is expected to hit around USD 7.48 billion by 2034. APAC is experiencing the fastest growth, thanks to widespread digital transformation initiatives, especially in countries like China, India, Japan, and Australia. Organizations across banking, manufacturing, retail, and government sectors are increasingly deploying GenAI for incident management, automation, and system forecasting. The rise of AI and 5G-backed smart cities, cloud-native startups, and tech-forward government policies (e.g., India’s Digital India mission and China’s Next Generation AI Plan) are major accelerators. However, the region faces challenges like uneven AI readiness across countries, infrastructure limitations in rural areas, and the need for improved AI governance frameworks. Despite this, the abundance of data, tech-savvy populations, and cost-effective AI talent are powerful growth enablers.

Europe: Emphasis on Compliance and Sustainable IT Operations

The Europe generative AI in IT operations market size was estimated at USD 0.42 billion in 2024 and is projected to surpass around USD 7.18 billion by 2034. Europe exhibits steady and resilient growth, driven by a strong regulatory environment, focus on data ethics, and demand for sustainable IT practices. Enterprises here are leveraging GenAI in IT operations to comply with stringent data protection laws like GDPR while improving system efficiency and uptime. Countries like Germany, France, and the UK are leading adopters, with a focus on integrating GenAI into smart infrastructure, Industry 4.0, and digital healthcare initiatives. Furthermore, the EU’s investments in AI research through programs like Horizon Europe are encouraging startups and enterprises to experiment with innovative IT solutions. Nonetheless, concerns over data localization and the cautious approach to third-party AI integration slightly moderate growth compared to North America and APAC.

LAMEA: Emerging Opportunities Amid Digital Modernization

The LAMEA generative AI in IT operations market size was valued at USD 0.07 billion in 2024 and is anticipated to reach around USD 1.20 billion by 2034. LAMEA (Latin America, Middle East, and Africa) presents high-growth potential, although it currently lags behind in adoption compared to other regions. Countries like the UAE, Saudi Arabia, and Brazil are investing in large-scale AI and smart city projects (e.g., Saudi Arabia’s NEOM and UAE’s AI Strategy 2031) that encourage GenAI use in IT and infrastructure management. Telecommunications and energy sectors are leading the charge, using GenAI for automated system monitoring, fault prediction, and capacity planning. However, the region faces hurdles such as digital divide, limited AI literacy, and infrastructure gaps in underdeveloped areas. Global tech players partnering with local enterprises and increasing cloud penetration are key trends helping bridge these gaps and fuel adoption.

Generative AI in IT Operations Market Top Companies

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

The competitive landscape of the generative AI in IT operations market is characterized by the presence of many global technology giants and emerging startups, all trying to increase their AIOP offerings through continuous innovation, strategic partnerships, and acquisitions. Companies such as IBM, Microsoft, Google, ServiceNow, and Dynatrace lead the market with broad platforms that integrate AI for tasks such as discrepancy detection, future maintenance, and IT automation. Meanwhile, startups and mid-tier firms are gaining traction by offering niche, agile, and scalable solutions to suit specific industries or enterprise sizes. The competition is fast as vendors race to integrate large language models (LLM), improve accuracy, and develop AI rules. The speed of innovation and ecosystem compatibility in this developed market are reshaping competitive mobility to open-source contributions and cloud-based applications.

Recent Developments

• In May 2025, Microsoft unveiled significant progress in its product ecosystem regarding AI integration. Major announcements include changes to a broad AI coding agent of GitHub Copilot, which enhances development workflows. Additionally, Microsoft introduced the Windows AI Foundry for advanced AI capabilities in the local development environment and expanded the Azure AI Foundry with more than 1,900 models, including integration with Elon Musk's Grok 3AI.  
• In May 2025, IBM accelerated its venture into the generic AI initiative by enhancing hybrid capabilities, enabling the rapid and safe integration of AI devices into daily operations. Furthermore, IBM expanded its partnership with Oracle, providing advanced AI applications in a Hybrid Cloud environment, utilizing the power of the Oracle Cloud Infrastructure (OCI) to enhance the Watsonx, IBM's chief AI portfolio.  
• In April 2025, Google Cloud announced several AI advancements, including updates to its general AI product. The purpose of these developments is to quickly and efficiently bring generic AIs to Google's advanced technology and models, such as Gemini, for real-world experiences.

Market Segmentation

By Component

• Software
• Services

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

By Deployment Mode

• Cloud-Based
• On-Premises
• Hybrid

By Enterprise Size

• Small and Medium Enterprises (SMEs)
• Large Enterprises

By Region

• North America
• APAC
• Europe
• LAMEA