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AIOps Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2025-2034

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    Report

  • 170 Pages
  • May 2025
  • Region: Global
  • Global Market Insights
  • ID: 6097038
UP TO OFF until Jun 30th 2025
The Global AIOps Market was valued at USD 5.3 billion in 2024 and is estimated to grow at a CAGR of 22.4% to reach USD 44.1 billion by 2034, fueled by the rising complexity of enterprise IT ecosystems and the urgent demand for automation in managing infrastructure performance. Businesses adopt AI-driven platforms to reduce system downtime, accelerate incident resolution, and gain real-time visibility into operational bottlenecks. As digital transformation intensifies across sectors like telecommunications, healthcare, retail, and banking, AIOps platforms become central to managing scalable and agile IT frameworks.

Technology integrates artificial intelligence with machine learning and big data capabilities to continuously monitor, analyze, and improve IT operations. Enterprises deploying hybrid and multi-cloud environments generate vast telemetry and event data, which AIOps solutions can process and interpret quickly. This empowers organizations to forecast disruptions, trace dependencies, and streamline performance monitoring across distributed systems. The ability to correlate events, detect anomalies, and pinpoint root causes in real time has made AIOps indispensable for maintaining uptime and operational continuity. As IT stacks become more decentralized, the need for intelligent platforms that enable predictive insights and autonomous responses is rapidly accelerating.

The solutions segment held a 60% share in 2024 and is projected to reach USD 28 billion by 2034. Businesses prioritize scalable AIOps platforms that reduce manual intervention and support automation across application management, incident resolution, and log analysis. These AI-powered tools are increasingly deployed across hybrid, on-premises, and cloud-based environments, offering centralized observability and operational transparency. The emphasis is on deploying agile platforms that adapt to evolving infrastructure demands while minimizing complexity and human error. The dominance of solution offerings is driven by their compatibility, scalability, and ability to address high-level performance challenges in real time.

The on-premises deployment model led the market with a 54% share in 2024. Enterprises dealing with sensitive workloads, particularly in sectors like defense, healthcare, and finance, prefer in-house setups for data protection, compliance adherence, and integration with legacy systems. On-premises installations also allow lower latency and provide organizations with total control over IT infrastructure. These advantages continue to make local deployments a preferred choice for companies with strict regulatory obligations and mission-critical operations.

U.S. AIOps Market generated USD 361.3 million in 2024, due to its cutting-edge IT landscape and early adoption of AI technologies. Robust digital infrastructure, combined with high innovation levels from tech giants like Datadog, Elastic, IBM, Cisco, and Dynatrace, has created an ecosystem primed for advanced AIOps deployment. The rise of agile frameworks, DevOps adoption, and hybrid IT environments fuels demand for intelligent automation and real-time decision-making across U.S. enterprises.

Key strategies adopted by major players in the AIOps Market include constant platform innovation, strategic acquisitions, and AI model refinement to ensure a competitive edge. Companies like BigPanda, Moogsoft, Digitate, Aisera, and Broadcom are investing heavily in expanding product portfolios that integrate seamlessly with cloud-native and hybrid systems. Building strong partner ecosystems, enhancing multi-domain visibility, and embedding real-time analytics into existing IT workflows help these companies drive enterprise adoption and solidify their leadership in the evolving AIOps landscape.

Comprehensive Market Analysis and Forecast

  • Industry trends, key growth drivers, challenges, future opportunities, and regulatory landscape
  • Competitive landscape with Porter’s Five Forces and PESTEL analysis
  • Market size, segmentation, and regional forecasts
  • In-depth company profiles, business strategies, financial insights, and SWOT analysis

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Table of Contents

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 360 degree 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

Companies Mentioned

The companies featured in this AIOps market report include:
  • Aisera
  • Amelia (IPsoft)
  • Bigpanda
  • BMC Software
  • Broadcom CA
  • Cisco
  • Datadog
  • Devo
  • Digital.ai
, Digitate
, Dynatrace
, Elastic
, Espressive
, Extrahop
, harness
, IBM
, Interlink Software
, kentik
, Logz.io
, Moogsoft

Table Information