IT 운영용 인공지능 시장 : 컴포넌트, 도입 형태, 기업 규모, 최종사용자별 - 세계 예측(2025-2032년)

The Artificial Intelligence for IT Operations Market is projected to grow by USD 35.06 billion at a CAGR of 19.21% by 2032.

Positioning enterprises to harness AI-driven operations for measurable resilience, real-time observability, and strategic IT value across complex modern infrastructures

Enterprises today face an inflection point where operational complexity, digital scale, and the velocity of incidents demand new approaches to IT operations. Artificial intelligence for IT operations has matured from concept to core operational capability, offering automated detection, correlation, and remediation tools that reduce mean time to resolution and streamline cross-functional workflows. As infrastructure footprints diversify across cloud, hybrid, and on-premise environments, organizations must reconcile fragmented telemetry with unified observability strategies to sustain performance and reliability.

Moreover, recent advances in machine learning architectures and model efficiency have narrowed the gap between experimental pilots and production-grade solutions. This transition occurs alongside evolving organizational expectations: leaders now seek demonstrable business outcomes such as improved service availability, cost control, and platform stability rather than purely technical proofs of concept. Consequently, AIOps initiatives increasingly integrate with site reliability engineering, cloud operations, and business continuity teams to deliver measurable operational resilience.

Finally, the introduction of governance frameworks and heightened regulatory scrutiny requires that technology and compliance functions collaborate early in the adoption lifecycle. Ethical model considerations, explainability of automated actions, and robust audit trails are no longer optional; they are prerequisites for enterprise-scale deployments. Therefore, a pragmatic approach that balances technical feasibility with governance, skills, and vendor ecosystem readiness will determine which organizations realize the full potential of AI-driven operations.

Recognizing the transformative shifts reshaping operational intelligence as AI models, observability stacks, and automation redefine incident detection and remediation

The operational landscape has shifted dramatically as observability stacks, AI model capabilities, and automation frameworks converge to reshape incident management and service assurance. Where teams once relied on manual triage and siloed dashboards, new toolchains are enabling continuous correlation of telemetry, proactive anomaly detection, and automated remediation workflows. This transformation reduces noise, focuses human expertise on higher-value work, and shortens time horizons for detecting systemic degradation.

Concurrently, the proliferation of hybrid and multi-cloud architectures has elevated the importance of data portability and federated monitoring. Organizations increasingly prioritize vendor-agnostic observability layers that aggregate metrics, logs, traces, and events across distributed environments. At the same time, the integration of predictive analytics into operations teams has shifted the emphasis from reactive troubleshooting to anticipatory maintenance and capacity optimization.

In parallel, changes in procurement and vendor engagement models are accelerating outcomes. Strategic partnerships, outcome-based contracting, and managed service offerings enable enterprises to access specialized capabilities while mitigating internal skills gaps. As a result, leaders must reassess organizational operating models, upskill cross-functional teams, and adopt governance controls that uphold reliability and compliance. Taken together, these shifts demand a holistic strategy that aligns architecture, process, and people to generate sustainable operational improvements.

Assessing the cumulative operational and supply chain implications of evolving United States tariff measures announced through 2025 on AIOps platforms and deployments

Evolving trade policy and tariff measures announced through 2025 have created a new layer of commercial and operational risk that technology leaders must factor into AIOps planning. Hardware-dependent components, particularly specialized accelerators and networking gear, face variable cost dynamics that can influence procurement timing and vendor selection. In turn, organizations may adapt by prioritizing software-centric solutions, leveraging cloud consumption models to defer capital expenditure, or restructuring supplier relationships to increase regional diversification and resilience.

Beyond direct cost implications, tariffs affect lead times, inventory buffers, and vendor logistics. Therefore, IT and supply chain teams must collaborate to assess component sourcing, stock levels for critical hardware, and service level agreements for maintenance and spare parts. In many cases, companies will seek greater contractual assurances from vendors regarding lead-time commitments, price protection clauses, and contingency support to maintain continuous operations.

Moreover, tariffs can accelerate architectural decisions that reduce exposure to hardware-specific risks. For example, organizations may accelerate migration to cloud-native observability or adopt managed services that abstract hardware procurement. These strategic shifts are complemented by renegotiated commercial terms and a renewed emphasis on vendor ecosystems that can support regional deployments and localized support. Ultimately, the cumulative effect of tariff changes is to increase the importance of flexible procurement strategies and architectural designs that prioritize portability and operational continuity.

Unpacking component, deployment, enterprise size, and end-user segment dynamics to reveal targeted adoption patterns, integration needs, and buying behaviors in AIOps

A nuanced segmentation lens reveals how adoption patterns vary across component, deployment mode, enterprise size, and end-user needs, shaping differentiated product requirements and buying behaviors. At the component level, stakeholders evaluate Services and Solutions through distinct lenses: Services encompass Managed Services and Professional Services, where Managed Services include Managed Support and Remote Monitoring and Professional Services span Consulting, Integration, and Support; Solutions focus on technical capabilities such as Anomaly Detection, Event Correlation, Performance Monitoring, Predictive Analytics, and Root Cause Analysis. This dual-track view clarifies why some organizations prefer vendor-led managed offerings while others prioritize in-house solution control.

Deployment mode further differentiates buyer priorities. Cloud-first organizations and those adopting hybrid cloud or private cloud models emphasize scalability, telemetry ingestion rates, and cross-account visibility, whereas on-premise deployments prioritize data sovereignty, low-latency processing, and tighter integration with legacy tooling. Enterprise size also frames requirements: large enterprises demand deep integration, multi-tenancy, and enterprise-grade security controls, while small and medium enterprises often favor turnkey solutions with simplified onboarding and managed support to offset limited internal resources.

End-user verticals introduce another dimension of differentiation. Government and defense customers emphasize compliance, auditability, and secure deployment pathways; healthcare and life sciences prioritize patient-safety aligned observability and validated analytics; IT and telecom firms require high-throughput event correlation and carrier-grade availability; manufacturing buyers focus on predictive maintenance and OT-IT convergence; and retail stakeholders emphasize customer experience monitoring and transaction-level performance. Together, these segmentation vectors inform product roadmaps, pricing strategies, and go-to-market approaches that vendors and buyers must align to realize value.

Examining regional demand drivers, infrastructure readiness, and policy influences across the Americas, Europe Middle East and Africa, and Asia-Pacific technology ecosystems

Regional considerations materially shape adoption speed, deployment patterns, and vendor engagement approaches across the global technology landscape. In the Americas, demand is driven by advanced cloud adoption, a mature managed services ecosystem, and a strong appetite for automation to support distributed digital services. Organizations here often prioritize rapid time-to-value and flexibility, seeking vendors who can deliver integrated cloud-native observability and managed incident response capabilities while aligning to stringent security controls.

In Europe, the Middle East and Africa, regulatory frameworks and data residency expectations influence architecture and procurement choices. Enterprises in EMEA emphasize compliance, explainability, and robust audit trails, and they frequently opt for hybrid deployment models that balance cloud innovation with local control. Meanwhile, public sector entities and mission-critical industries in the region demand high levels of customization and long-term vendor partnerships that include local support and certification.

Asia-Pacific presents a heterogeneous landscape where rapid digitalization and manufacturing scale drive interest in predictive analytics and OT integration. Many organizations in APAC prioritize solutions that support large-scale telemetry ingestion, edge processing for latency-sensitive use cases, and localized managed services for regional continuity. Across all regions, vendor strategies that respect local regulatory nuances, provide strong partner networks, and offer flexible commercial models will be best positioned to meet enterprise needs.

Analyzing vendor strategies, partnership models, innovation trajectories, and competitive differentiators that shape the AIOps supplier landscape and customer choices

The supplier landscape for AI-driven operations is characterized by a mix of established platform providers, specialized vendors, system integrators, and managed service firms, each pursuing distinct strategic plays. Some vendors emphasize integrated observability suites that bundle telemetry ingestion, storage, and analytics to provide an end-to-end operational view, while others differentiate through best-of-breed modules that focus on high-performance anomaly detection or sophisticated root-cause analysis algorithms. This diversity allows buyers to assemble capabilities that match their architectural preferences and organizational maturity.

Partnerships and ecosystem plays are central to competitive positioning; strategic alliances with cloud providers, middleware vendors, and systems integrators enable vendors to embed capabilities into broader enterprise stacks and accelerate customer deployments. Additionally, innovation trajectories show a strong emphasis on model explainability, low-code automation workflows, and packaged domain-specific analytics for vertical use cases. Open-source components and community-driven tooling continue to influence product roadmaps, prompting vendors to balance proprietary differentiation with interoperability.

From a commercial perspective, vendors are exploring flexible consumption models, outcome-based agreements, and managed services that reduce friction for buyers with limited internal expertise. Meanwhile, advanced customers are driving demand for deeper instrumentation, API-driven extensibility, and robust security controls. To succeed, vendors must combine technical excellence with professional services capabilities and regional support footprints that align with enterprise procurement and operational requirements.

Actionable recommendations for technology leaders to accelerate responsible AIOps adoption, optimize operations, and mitigate geopolitical and supply chain risks

Leaders seeking to accelerate responsible AIOps adoption should follow a pragmatic roadmap that balances technical ambition with operational discipline. First, establish clear outcome definitions that tie AIOps initiatives to measurable reliability, customer experience, or cost objectives; this alignment ensures executive sponsorship and clarifies success criteria. Second, prioritize high-impact pilots that address frequent incidents or costly maintenance activities and design them to be interoperable with existing CI/CD and observability infrastructures, which reduces integration friction and accelerates learning.

Third, adopt a layered approach to procurement that evaluates both managed service options and software licensing to determine the optimal division of responsibilities between internal teams and external partners. Fourth, invest in skills and governance: upskilling site reliability engineers, embedding model risk management, and formalizing change control for automated remediation actions mitigates operational risk and supports compliance. Fifth, strengthen supply chain resilience by diversifying hardware suppliers, negotiating lead-time protections, and considering cloud-based or managed alternatives to reduce exposure to tariff-driven variability.

Finally, implement a continuous improvement cadence that incorporates operational metrics, post-incident reviews, and stakeholder feedback loops. This iterative process ensures that AIOps capabilities evolve in step with changing architectures, regulatory requirements, and business priorities, transforming initial pilots into enduring, value-creating capabilities.

Transparent research methodology detailing primary and secondary sources, triangulation processes, validation procedures, and limitations to ensure credibility

This research synthesizes primary and secondary inputs through a structured methodology designed to ensure credibility, relevance, and transparency. Primary research included interviews with enterprise IT leaders, operations engineers, procurement executives, and vendor product managers to capture firsthand perspectives on use cases, deployment challenges, and buying criteria. These qualitative insights were complemented by targeted surveys of practitioners to quantify adoption patterns, integration preferences, and service model priorities.

Secondary research encompassed technical documentation, vendor whitepapers, regulatory publications, and publicly available case studies to contextualize operational practices and ecosystem developments. The research team triangulated findings by cross-referencing primary interview responses with vendor capabilities and documented deployment narratives, enabling identification of recurring themes and divergent practices. In addition, scenario analysis was applied to assess the operational implications of procurement and policy shifts, including tariff-related supply chain scenarios.

Limitations include variability in public disclosure across vendors and the inherent rapid evolution of model architectures and commercial offerings. To mitigate these limitations, the methodology emphasized contemporaneous sourcing, iterative validation with subject-matter experts, and conservative interpretation of forward-looking implications. Finally, recommended follow-up activities include periodic updates to capture emerging features, new partnerships, and regulatory developments that affect deployment and governance.

Concluding synthesis that ties strategic implications, operational priorities, and pragmatic next steps for enterprise leaders pursuing AIOps transformation

Enterprises that embrace AI-driven operations stand to transform how they detect, diagnose, and remediate incidents, but success depends on aligning technology with governance, procurement, and organizational capabilities. Effective initiatives balance the adoption of advanced analytics and automation with rigorous change control, explainability, and cross-functional collaboration. When these elements are in place, organizations can reduce operational toil, improve service availability, and redirect human expertise toward strategic engineering work.

Regional and policy dynamics, including changes in trade and tariff regimes, underscore the need for flexible procurement strategies and architectures that prioritize portability and managed consumption options. Vendors and customers alike must adapt commercial terms and supply chain arrangements to preserve continuity in the face of geopolitical variability. Meanwhile, segmentation insights indicate that one-size-fits-all approaches rarely succeed; tailored solutions that reflect component priorities, deployment modes, enterprise scale, and vertical-specific constraints deliver better outcomes.

In summary, the pathway to effective AIOps adoption is iterative and pragmatic. Begin with focused pilots, validate outcomes against business metrics, and scale through governance, skills development, and vendor partnerships. By doing so, organizations will not only improve day-to-day reliability but also build a foundation for continuous operational improvement and strategic advantage.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

3. Executive Summary

4. Market Overview

5. Market Insights

• 5.1. Implementation of event correlation and anomaly detection with unsupervised ML algorithms for proactive incident management
• 5.2. Adoption of generative AI models to automate root cause analysis and incident remediation workflows
• 5.3. Integration of AIOps platforms with hybrid cloud and multi-cloud infrastructures for unified observability
• 5.4. Application of real-time streaming analytics and AI-driven alert noise reduction in complex IT environments
• 5.5. Use of AIOps-driven capacity forecasting and resource optimization in serverless and containerized architectures
• 5.6. Deployment of explainable AI techniques to improve transparency and governance in automated IT operations
• 5.7. Incorporation of AI-powered chatbots and virtual agents for autonomous IT service desk support and ticket resolution

6. Cumulative Impact of United States Tariffs 2025

7. Cumulative Impact of Artificial Intelligence 2025

8. Artificial Intelligence for IT Operations Market, by Component

• 8.1. Services
  • 8.1.1. Managed Services
    • 8.1.1.1. Managed Support
    • 8.1.1.2. Remote Monitoring
  • 8.1.2. Professional Services
    • 8.1.2.1. Consulting
    • 8.1.2.2. Integration
    • 8.1.2.3. Support
• 8.2. Solutions
  • 8.2.1. Anomaly Detection
  • 8.2.2. Event Correlation
  • 8.2.3. Performance Monitoring
  • 8.2.4. Predictive Analytics
  • 8.2.5. Root Cause Analysis

9. Artificial Intelligence for IT Operations Market, by Deployment Mode

• 9.1. Cloud
  • 9.1.1. Hybrid Cloud
  • 9.1.2. Private Cloud
  • 9.1.3. Public Cloud
• 9.2. On-Premise

10. Artificial Intelligence for IT Operations Market, by Enterprise Size

• 10.1. Large Enterprises
• 10.2. Small And Medium Enterprises

11. Artificial Intelligence for IT Operations Market, by End User

• 11.1. Government And Defense
• 11.2. Healthcare And Life Sciences
• 11.3. IT And Telecom
• 11.4. Manufacturing
• 11.5. Retail

12. Artificial Intelligence for IT Operations Market, by Region

• 12.1. Americas
  • 12.1.1. North America
  • 12.1.2. Latin America
• 12.2. Europe, Middle East & Africa
  • 12.2.1. Europe
  • 12.2.2. Middle East
  • 12.2.3. Africa
• 12.3. Asia-Pacific

13. Artificial Intelligence for IT Operations Market, by Group

• 13.1. ASEAN
• 13.2. GCC
• 13.3. European Union
• 13.4. BRICS
• 13.5. G7
• 13.6. NATO

14. Artificial Intelligence for IT Operations Market, by Country

• 14.1. United States
• 14.2. Canada
• 14.3. Mexico
• 14.4. Brazil
• 14.5. United Kingdom
• 14.6. Germany
• 14.7. France
• 14.8. Russia
• 14.9. Italy
• 14.10. Spain
• 14.11. China
• 14.12. India
• 14.13. Japan
• 14.14. Australia
• 14.15. South Korea

15. Competitive Landscape

• 15.1. Market Share Analysis, 2024
• 15.2. FPNV Positioning Matrix, 2024
• 15.3. Competitive Analysis
  • 15.3.1. International Business Machines Corporation
  • 15.3.2. Broadcom Inc.
  • 15.3.3. Microsoft Corporation
  • 15.3.4. Splunk Inc.
  • 15.3.5. ServiceNow, Inc.
  • 15.3.6. Cisco Systems, Inc.
  • 15.3.7. BMC Software, Inc.
  • 15.3.8. Dynatrace LLC
  • 15.3.9. Datadog, Inc.
  • 15.3.10. HCL Technologies Limited