AIOps Platform Market size is estimated to be valued at USD 11.78 Bn in 2025 and is expected to reach USD 54.62 Bn in 2032, exhibiting a compound annual growth rate (CAGR) of24.5% from 2025 to 2032.
The AIOps (Artificial Intelligence for IT Operations) platforms market is rapidly growing as organizations seek smarter ways to manage complex IT environments. These platforms combine AI and ML with IT operations to automate processes, detect anomalies, and enable real-time decision-making.
The rising AIOps platform market demand is driven by the need for operational efficiency, real-time monitoring, and reduced downtime across cloud, hybrid, and on-premise systems. Industries such as banking, telecom, and healthcare are adopting AIOps to enhance service reliability and reduce manual workloads. AIOps is becoming essential to modern IT strategies as digital transformation accelerates.
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Despite growing enterprise interest and steady vendor innovation, the adoption of AIOps platforms continues to face friction due to several recurring pain points reported by end users. Feedback collected from IT operations teams, DevOps practitioners, and SREs highlights a series of unmet needs that present both challenges and opportunities for market players.
One of the most frequently cited concerns among users is the complexity involved in deploying AIOps solutions. Implementations often require extensive customization, prolonged onboarding, and significant integration effort with legacy infrastructure and disparate monitoring tools. Many users report that “out-of-the-box” functionality remains limited, and achieving full operational value can take several months. This implementation barrier significantly slows time-to-value, particularly for mid-sized organizations with limited IT resources.
AIOps platforms often require a blend of AI/ML proficiency and domain-specific IT operations expertise—skill sets that are rarely found in a single team. As a result, organizations are forced to either invest heavily in training or rely on managed services or system integrators. This skills gap leads to underutilization of platform features, reduced effectiveness, and extended deployment timelines.
While AIOps is positioned to reduce alert fatigue by correlating and suppressing redundant events, end users frequently report ongoing issues with excessive alerts and low-quality signal extraction. In many cases, platforms fail to provide reliable event correlation without intensive manual rule creation or tagging strategies. As a result, operations teams continue to experience high levels of alert noise, diminishing trust in the platform and forcing teams to revert to manual monitoring in critical cases.
In terms of organization size, the small and midsize companies’ segment is expected to dominate the global AIOps platforms market with a highest share in 2025, primarily due to strong demand for AIOps platform’s ability to operate with limited IT Staff and budgets. SMEs cannot always afford large IT teams or multiple monitoring tools. AIOps platforms consolidate functions such as performance monitoring, anomaly detection, and alerting into a single, automated system, reducing operational costs and manual intervention. Even small businesses are using cloud services, SaaS platforms, and hybrid infrastructures. AIOps helps them gain unified visibility and manage these setups proactively without building complex custom solutions.
In February 2025, Futran Solutions released its latest “AIOps 2025” vision, spotlighting transformative AI-driven automation in IT operations. The framework emphasizes predictive analytics, real-time anomaly detection, and self-healing systems to preempt downtime, optimize cloud resource allocation, and streamline incident response workflows.
In terms of component, the platform segment is expected to contribute with the highest share in the market in 2025 due to its integrated AI-centric capabilities which aligns with enterprises needs for scalable and proactive operations. AIOps platforms integrate essential functionalities such as event correlation, anomaly detection, predictive analytics, and automated incident response into a single, unified system. This holistic approach offers comprehensive IT oversight and workflow efficiency, making platforms more valuable than standalone services. Organizations especially in finance, healthcare, and IT demanding for real-time analytics powered by AI/ML to proactively address issues before they escalate. AIOps platforms are inherently designed for this, offering greater value than reactive, manual services.
In May 2025, Digitate, a leader in autonomous enterprise software, introduced flagship ignio™ AIOps platform on Amazon Web Services (AWS) Marketplace. This strategic move enables global customers to access, deploy, and scale ignio directly through AWS, streamlining procurement and integration. The AI-driven platform leverages machine learning to detect, diagnose, and resolve IT incidents autonomously, helping enterprises enhance service reliability and reduce operational costs.
In terms of verticals, the IT & telecom segment is expected to contribute the greatest share of the market in 2025, driven the need to reduce downtime and automate complex operations, along with improving customer experience and support the rollout of next-gen technologies such as %G, Edge computing, and AI-native cloud infrastructure. AIOps helps in increasing complex infrastructure, services, and customer demands. Telecom operators manage vast, geographically distributed network infrastructures (5G, fiber, edge). AIOps continuously monitors metrics like latency, packet loss, bandwidth, and device performance, using machine learning to detect anomalies in real-time. AIOps predicts potential system failures by analyzing historical log data and behavioral patterns.
In March 2025, At Mobile World Congress 2025 in Barcelona, Jio Platforms teamed up with Nokia, AMD, and Cisco to introduce an Open Telecom AI Platform, offering AI-driven orchestration across RAN, routing, AI data centers, and security layers. Designed to optimize efficiency, security, and revenue, this modular platform also targets global service providers with Jio as its initial pilot.
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North America held the dominant position in the market in 2025 with 33.10% share and is projected to retain its dominance throughout the forecast period. The U.S. and Canada are major economies driving the growth of the AIOps platform market in this region, owing to the region being an early adopter of advanced technologies such as artificial intelligence, machine learning, big data, and analytics in the region. Moreover, the presence of IT companies such as Google, Microsoft Corporation, IBM, Oracle, and others plays a major role in the growth of the market in the region.
Experts forecast that by 2025, AIOps will shift from reactive to proactive/resilience-focused frameworks, particularly within North American organizations serious about digital transformation.
Europe AIOps platform market is experiencing significant growth due to European enterprises rapidly moving to clou-native, hybrid and multi-cloud environment. Europe also has strict data protection and compliance requirements under the General Data Protection Regulation (GDPR) and the proposed EU AI Act are pushing companies to adopt transparent, auditable AI systems. AIOps platforms with built-in compliance reporting and explainable AI are well-suited for regulated sectors such as finance, telecom, and healthcare. A 2025 ISG report notes that Swiss enterprises increasingly demand AIOps and FinOps to manage multicloud complexity. Providers leveraging AIOps have cut operational workloads by 30–50%, optimizing both performance and costs. This is further accelerating the AIOps market share.
The U.S. AIOps platform market is characterized by advanced IT infrastructure, large-scale enterprises and heavy reliance on cloud ad hybrid environment. U.S. enterprises run vast multi-cloud, hybrid, and microservices-based environments that generate massive telemetry data (logs, metrics, traces). U.S. is rapid shifting towards cloud adoption. For instance, in 2024, JPMorgan Chase integrated AIOps with their hybrid cloud strategy to reduce MTTR (mean time to resolution) by 35%.
The U.K. AIOps platform market is market by high demand in banking, insurance, and government sectors for service reliability and compliance. The UK's banking sector alone faces over £1 billion in fraud annually, with more than 3.3 million incidents recorded in 2024, a 12% increase from 2023. This sector is working towards maintaining high uptime and secure platforms to meet service-level agreements and regulatory standards, making AIOps essential.
| Report Coverage | Details | ||
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| Base Year: | 2024 | Market Size in 2025: | USD 11.78 Bn |
| Historical Data for: | 2020 To 2024 | Forecast Period: | 2025 To 2032 |
| Forecast Period 2025 to 2032 CAGR: | 24.5% | 2032 Value Projection: | USD 54.62 Bn |
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International Business Machines Corporation, Splunk Inc., CA Technologies, VMware, Inc., Micro Focus International plc., HCL Technologies Limited, AppDynamics, BMC Software, Inc., Moogsoft, and FixStream Network Inc. |
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The activities and transactions involved in banking operations are frequently done by clients, employees, and outside organizations. Due to the complexity of these processes, monitoring is crucial. Over the projection period, AIOps platform market forecast anticipates strong growth, driven by the ability of these platforms to provide real-time insights and automate issue resolution. For example, C.A. Technologies' AIOps platform, Digital Experience Insights, enables financial institutions to address complex IT challenges such as performance bottlenecks, capacity planning, and configuration management. In April 2025, a UK-based retail bank deployed a similar AIOps solution to cut resolution times by 40%, improving both uptime and fraud detection capabilities.
As there is a numerous high-profile data breaches over the past few years, banks and other financial institutions are particularly concerned with protecting the security of the data they produce.
AIops is being used by many banks and other financial organizations to improve efficiency. For instance, major Indian banks wanted to improve the efficiency of the on boarding of their digital merchants as well as streamline and make transactions easier for their new customers. QualityKiosk's AIops-based analytics solution AnaBot improved merchant onboarding success rates and increased revenue by 7% by better supporting ON-US & OFF-US transactions.
Approximately three-fourths (74%) of financial institutions in the U.S. and the U.K. reported an increase in cybercrime, according to British Aerospace (BAE) Systems Applied Intelligence. Cybercrime can have serious financial repercussions for businesses in addition to known financial losses, including collapsing stock prices, reputational harm, and legal action. AIops can assist in the prosecution of this crime. To protect vulnerable systems, AIops solutions provide round-the-clock monitoring, suspicious activity detection, and defense operation activation.
The AIOps platform market is no longer a peripheral innovation, it has become a central enabler of operational resilience and strategic IT transformation. From a technical and operational standpoint, AIOps is rapidly reshaping how enterprises manage hybrid and distributed environments, particularly where cloud-native architectures and continuous integration pipelines introduce volatility and complexity.
AIOps is not merely about automation; it is about intent-driven observability. Traditional IT operations teams are constrained by the inability to detect low-signal anomalies buried within voluminous telemetry. AIOps addresses this limitation by introducing correlation engines that analyze logs, metrics, and traces in real time to identify root causes, often before incidents impact service-level objectives (SLOs).
An excellent example of this is PayPal’s deployment of AIOps to reduce incident triage time by 60%, where machine learning was used to map incident clusters across Kubernetes pods and microservices in real time. Similarly, LinkedIn’s implementation of auto-remediation workflows through AI-led diagnostics led to a 70% decrease in mean time to resolution (MTTR) across its internal infrastructure layers. These are not proofs of concept, they are live operational cases that illustrate the transformative capacity of mature AIOps frameworks.
Moreover, AIOps is disrupting traditional ITSM platforms by rendering static runbooks obsolete. Through event deduplication and causality detection, platforms like Dynatrace and Moogsoft are achieving alert compression rates upwards of 95%, effectively eliminating alert fatigue and enabling operators to focus on decision-making rather than firefighting.
A trend that should not be underestimated is the convergence of AIOps with DevOps pipelines. By integrating anomaly detection into CI/CD workflows, AIOps is evolving into a pre-production assurance layer. Atlassian’s Jira Service Management, for example, leverages anomaly scoring models to block deployment artifacts likely to cause regressions, effectively embedding predictive operations into the software lifecycle.
Another critical shift is the move from centralized to federated intelligence. AIOps platforms are increasingly being embedded at the edge in 5G towers, smart factories, and satellite control centers, where telemetry volumes are impractical to backhaul. IBM’s Watson AIOps is now being applied at the edge layer for autonomous manufacturing diagnostics, providing context-aware recommendations to plant engineers.
*Definition: Artificial Intelligence for IT Operations (AIOps) is a multilayered technology platform that enhances IT operations by using machine learning and analytics to analyze the big data collected for various IT operations tools and devices to resolve issues in real-time.
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About Author
Monica Shevgan has 9+ years of experience in market research and business consulting driving client-centric product delivery of the Information and Communication Technology (ICT) team, enhancing client experiences, and shaping business strategy for optimal outcomes. Passionate about client success.
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