Report Content
Chapter 1 Methodology & Scope
1.1 Research design
1.1.1 Research approach
1.1.2 Data collection methods
1.2 Base estimates & calculations
1.2.1 Base year calculation
1.2.2 Key trends for market estimation
1.3 Forecast model
1.4 Primary research and validation
1.4.1 Primary sources
1.4.2 Data mining sources
1.5 Market scope & definition
Chapter 2 Executive Summary
2.1 Industry 3600 synopsis, 2021 - 2034
Chapter 3 Industry Insights
3.1 Industry ecosystem analysis
3.1.1 Supplier landscape
3.1.1.1 Technology providers
3.1.1.2 OEM Manufacturers
3.1.1.3 Distributors
3.1.1.4 End use
3.1.2 Profit margin analysis
3.2 Impact of Trump administration tariffs
3.2.1 Trade impact
3.2.1.1 Trade volume disruptions
3.2.1.2 Retaliatory measures
3.2.2 Impact on industry
3.2.2.1 Supply-side impact (raw materials)
3.2.2.1.1 Price volatility in key materials
3.2.2.1.2 Supply chain restructuring
3.2.2.1.3 Production cost implications
3.2.2.2 Demand-side impact (Cost to customers)
3.2.2.2.1 Price transmission to end markets
3.2.2.2.2 Market share dynamics
3.2.2.2.3 Consumer response patterns
3.2.3 Key companies impacted
3.2.4 Strategic industry responses
3.2.4.1 Supply chain reconfiguration
3.2.4.2 Pricing and product strategies
3.2.4.3 Policy engagement
3.2.5 Outlook & future considerations
3.3 Technology & innovation landscape
3.4 Patent analysis
3.5 Key news & initiatives
3.6 Regulatory landscape
3.7 Use cases
3.8 Impact forces
3.8.1 Growth drivers
3.8.1.1 Proliferation of cloud infrastructure
3.8.1.2 Growing demand for AI-based services in IT operations
3.8.1.3 Increasing volume of data generated by modern IT infrastructures
3.8.1.4 Government initiatives for AI adoption in various countries
3.8.2 Industry pitfalls & challenges
3.8.2.1 Data security and privacy concerns
3.8.2.2 Increasing number of changes in IT operations
3.9 Growth potential analysis
3.10 Porter’s analysis
3.11 PESTEL analysis
Chapter 4 Competitive Landscape, 2024
4.1 Introduction
4.2 Company market share analysis
4.3 Competitive positioning matrix
4.4 Strategic outlook matrix
Chapter 5 Market Estimates & Forecast, By Component, 2021 - 2034 ($Bn)
5.1 Key trends
5.2 Solution
5.3 Service
Chapter 6 Market Estimates & Forecast, By Deployment Model, 2021 - 2034 ($Bn)
6.1 Key trends
6.2 On-premises
6.3 Cloud
Chapter 7 Market Estimates & Forecast, By Enterprise Size, 2021 - 2034 ($Bn)
7.1 Key trends
7.2 Large enterprises
7.3 SME
Chapter 8 Market Estimates & Forecast, By Application, 2021 - 2034 ($Bn)
8.1 Key trends
8.2 Infrastructure management
8.3 Real-time analytics
8.4 Network & security management
8.5 Application performance management
8.6 Others
Chapter 9 Market Estimates & Forecast, By End Use, 2021 - 2034 ($Bn)
9.1 Key trends
9.2 BFSI
9.3 IT & telecom
9.4 Healthcare
9.5 Retail
9.6 Government
9.7 Manufacturing
9.8 Media & entertainment
9.9 Others
Chapter 10 Market Estimates & Forecast, By Region, 2021 - 2034 ($Bn)
10.1 Key trends
10.2 North America
10.2.1 U.S.
10.2.2 Canada
10.3 Europe
10.3.1 UK
10.3.2 Germany
10.3.3 France
10.3.4 Italy
10.3.5 Spain
10.3.6 Russia
10.3.7 Nordics
10.4 Asia Pacific
10.4.1 China
10.4.2 India
10.4.3 Japan
10.4.4 Australia
10.4.5 South Korea
10.4.6 Southeast Asia
10.5 Latin America
10.5.1 Brazil
10.5.2 Mexico
10.5.3 Argentina
10.6 MEA
10.6.1 UAE
10.6.2 South Africa
10.6.3 Saudi Arabia
Chapter 11 Company Profiles
11.1 Aisera
11.2 Amelia (IPsoft)
11.3 Bigpanda
11.4 BMC Software
11.5 Broadcom CA
11.6 Cisco
11.7 Datadog
11.8 Devo
11.9 Digital.ai
11.10 Digitate
11.11 Dynatrace
11.12 Elastic
11.13 Espressive
11.14 Extrahop
11.15 harness
11.16 IBM
11.17 Interlink Software
11.18 kentik
11.19 Logz.io
11.20 Moogsoft
AIOps Market Size
The global AIOps (Artificial Intelligence in IT Operations) market size was valued at USD 5.3 billion in 2024 and is estimated to register a CAGR of 22.4% between 2025 and 2034.
The growing complexity of IT environments, coupled with the need for real-time analytics and automated root-cause detection, is driving the adoption of AIOps platforms across enterprises globally. Moreover, businesses are increasingly leveraging AIOps to reduce operational costs, enhance system reliability, and accelerate digital transformation especially in sectors such as banking, healthcare, telecom, and retail.
Artificial Intelligence for IT Operations (AIOps) includes big data analytics, machine learning (ML), etc. that are innovative AI technologies to improve the performance of the detection, analysis, and resolution of reoccurring IT problems. It helps organizations achieve a deep insight into the operational approach, predict various disruptions, identify behavioral patterns, and simplify troubleshooting in complex IT environment.
As enterprises embrace more distributed topologies, for example multi-cloud topologies, micro-services, containers and hybrid infrastructures, there is an exponential growth of performance logs and telemetry data. These AIOps platforms intelligently aggregate and analyze this information making it possible for teams to effectively monitor the infrastructure, discover interdependencies between its components and proactively service system health both in terms of internal networks and external service network.
For instance, in October 2024, Keep, an open-source AIOps platform, raised USD 2.7 Mn to increase its AI-powered capabilities designed for reducing alert fatigue of operations teams. Keep receiving considerable community support since its launch, gathering 3,000 stars on GitHub, and has a community of 400 members and 60 contributors. The platform calls boast a 97% reduction in signal-to-noise level, hence giving clearer lights as to how infrastructure is performing to the users.
AIOps Market Trends
Trump Administration Tariffs
AIOps Market Analysis
Based on component, the AIOps market is divided into solutions and service. The solution segment held a market share of over 60% and is expected to generate revenue of USD 28 billion by 2034.
Based on the deployment mode, the AIOps market is divided into on-premises, and cloud. The on-premises segment dominated the market accounting for over 54% market share in 2024.
Based on enterprise size, the AIOps market is categorized into large enterprises, and SMEs. The large enterprises segment held a market share above 46% in 2024.
Based on application, the AIOps market is divided into infrastructure management, real-time analytics, network & security management, application performance management, and others. The real-time analytics segment held a market share above 30% in 2024.
By end use, the AIOps market is divided into BFSI, IT & telecom, healthcare, retail, government, manufacturing, media & entertainment, and others. The IT & telecom segment held a market share above 27% in 2024.
North America dominates the global AIOps market with a share of around 48% and U.S. leads the market in the region generating revenue of USD 361.3 million in 2024.
The AIOps market in the China is expected to experience significant and promising growth from 2025 to 2034.
The AIOps market in UK is expected to experience significant and promising growth from 2025 to 2034.
AIOps Market Share
The top 5 companies leading the AI Ops industry in 2024 are IBM, Broadcom CA, Cisco, Elastic, Aisera. Together, they hold around 70% market share in the market.
AIOps Market Companies
Major players operating in the AI Ops industry include:
AI Ops Industry News
The AIOps market research report includes in-depth coverage of the industry with estimates & forecasts in terms of revenue ($Bn) from 2021 to 2034, for the following segments:
Market, By Component
Market, By Deployment Mode
Market, By Enterprise Size
Market, By Application
Market, By End Use
The above information is provided for the following regions and countries: