The Artificial Intelligence for IT Operations (AIOps) market is entering a more mature phase of adoption. Projected to reach $32.4 billion by 2028, AIOps is moving beyond early-stage adoption to become a strategic capability for enterprises that depend on digital infrastructure for business continuity, customer experience, and regulatory compliance. But the market is not simply growing. It is also consolidating and evolving, making it increasingly important to distinguish proven operational capability from marketing claims, a shift Indian enterprises should evaluate carefully before making long-term technology investments.
The consolidation trend is increasingly visible. Cisco’s acquisition of Splunk brought a major security and observability platform into Cisco’s technology portfolio. Dell Technologies acquired Moogsoft, an AI-driven intelligent monitoring provider with established AIOps capabilities, while HPE acquired OpsRamp to strengthen its AIOps and hybrid IT operations management capabilities. Francisco Partners and TPG also acquired New Relic, taking the observability company private. Together, these transactions illustrate how ownership and competitive dynamics across AIOps and observability are changing, with several established capabilities increasingly becoming part of broader technology portfolios.
For CIOs and IT leaders at Indian enterprises, particularly across BFSI, healthcare, and government digital services, this consolidation creates both opportunities and strategic considerations. The opportunity lies in access to broader, more integrated technology platforms. The challenge lies in potential vendor lock-in, greater dependence on individual platform ecosystems, and the need to distinguish marketed capabilities from proven operational outcomes. Understanding this landscape is essential for making technology investments that deliver measurable operational and business value without adding unnecessary complexity to modern IT environments.
What Consolidation Actually Means for Enterprise Buyers
When a large platform vendor acquires an AIOps company, the acquisition does not automatically translate into a stronger integrated offering. Product, platform, data, and operational integration can take time, and the pace varies significantly across acquisitions. During this transition, customers may need to evaluate how product roadmaps, integrations, support models, and existing capabilities will evolve within the broader platform ecosystem.
More fundamentally, acquired capabilities may evolve as they are integrated into the acquirer’s broader platform strategy. This can introduce changes in product roadmaps, integrations, architecture, and platform priorities for existing customers. Enterprises should therefore evaluate whether an acquired AIOps or observability platform continues to provide the domain depth, interoperability, cross-domain correlation, and deployment flexibility required for their operating environment.
For Indian enterprises making long-term technology investments, where procurement and deployment cycles can extend over significant periods, selecting a recently acquired product requires careful evaluation of roadmap and integration risk. The product evaluated during procurement may evolve before deployment as integration priorities and roadmaps change, while its architecture, integrations, or operating model may continue to evolve over the investment lifecycle.
This is where iStreet Network takes a different approach. Rather than relying on acquisition-led integration, iStreet’s Resilient Operations capabilities are powered by HEAL Software’s purpose-built AIOps solution, which correlates telemetry across existing observability, ITSM, and infrastructure environments to support anomaly detection, root cause analysis, predictive intelligence, and remediation. This provides enterprises with a connected intelligence layer across observability, operations, and resolution while preserving interoperability with their existing technology ecosystem.
The Five-Point Test: Separating Genuine AIOps Capability
The term ‘AIOps’ is applied so broadly in the market that it has become nearly meaningless as a differentiator. Monitoring tools with basic threshold alerting label themselves as AIOps. Dashboards with rudimentary anomaly detection claim AIOps capability. ITSM platforms that add a machine learning module to their ticketing workflow market themselves as AIOps-powered.
For enterprise buyers, this creates a filtering problem. How do you distinguish a platform that genuinely transforms IT operations from one that simply applies an AI label to conventional monitoring? The answer lies in a five-point capability test that maps directly to what AIOps must deliver to justify its investment.
Cross-domain data ingestion and analytics. A genuine AIOps platform must ingest and analyse diverse IT data, metrics, logs, traces, events, from across the entire technology landscape. If a platform only works with data from its own monitoring agents, or only analyses infrastructure metrics without application telemetry, it fails this test.
Topology assembly. The platform must automatically discover and map dynamic relationships between IT assets, services, applications, databases, infrastructure components, network paths, to create a contextual model of the environment. Without topology, correlation is impossible and root cause analysis is guesswork.
Correlation and pattern recognition. The platform must use machine learning to group related alerts, suppress noise, and identify meaningful patterns that indicate an actual incident, rather than flooding operators with hundreds of individual alerts that are symptoms of a single root cause.
Causality determination. Beyond correlation, the platform must determine the most likely root cause of an issue, tracing from observed symptoms through the dependency graph to the originating failure. This is the distinction between knowing that things are broken and understanding why.
Remediation association or augmentation. The platform must either suggest specific corrective actions based on historical resolution data or execute remediation autonomously where policies allow. Detection without direction is incomplete.
iStreet Network’s HEAL AIOps solution, aligns with these five criteria. Its capabilities include cross-domain ingestion across infrastructure, applications, networks, and digital experience; topology-aware dependency mapping; ML-driven event correlation that groups related signals and reduces alert noise; automated root cause analysis that traces causal relationships across service dependencies; and contextual solution recommendations with governed remediation where authorised.
The Competitive Landscape: How AIOps Providers Differ
The current AIOps market includes distinct categories of providers that differ in depth of capabilities, breadth of platforms, and market presence.
Market leaders offer comprehensive platforms combining deep observability with integrated AI engines. This tier includes platforms that provide full-stack visibility, causal AI, and automated remediation as integrated capabilities rather than bolted-on features. These platforms demonstrate the ability to ingest data from any source, maintain dynamic topology models, correlate events across domains, determine root cause, and recommend or execute remediation.
Strong performers typically have deep capabilities in specific domains, ITSM-led platforms with strong workflow automation, observability platforms with emerging AI features, or security-focused platforms adding operational intelligence. They deliver value in their core domain but often lack the cross-domain integration depth that genuine AIOps requires.
Niche specialists focus on specific functional capabilities, event correlation, network monitoring, capacity planning, or autonomous remediation. They excel in their specialty but typically require integration with other platforms to deliver a complete AIOps workflow.
iStreet Network’s positioning is distinctive within this landscape. Through HEAL Software’s unified platform, iStreet delivers leader-tier capabilities, full-stack observability, AIOps intelligence, GenAI-powered incident copilot, and Resiliency Operations Centre and governance, while maintaining the architectural coherence and deployment flexibility that Indian enterprises in regulated sectors require. This combination of breadth and integration depth, specifically tuned for the compliance and governance requirements of India’s BFSI, healthcare, and government sectors, creates a differentiated value proposition that assembled platforms cannot easily replicate.
Market Growth Drivers: Why AIOps Investment Is Accelerating
The business case for AIOps is no longer theoretical. Organisations that successfully adopt AIOps platforms report measurable returns: an average reduction in IT downtime of 30 to 50 percent and savings of 15 to 25 percent in overall IT operational costs. These outcomes are driven by the fundamental economics of intelligent automation, replacing hours of manual correlation, diagnosis, and remediation with AI-driven processes that operate at machine speed and machine consistency.
Several structural factors are accelerating AIOps adoption. The expansion of cloud-native architectures, including microservices, Kubernetes, and serverless computing, has increased the scale and complexity of enterprise IT operations, making manual monitoring and correlation increasingly difficult. Regulatory and governance requirements, including regulatory bodies, and CERT-In (Indian Computer Emergency Response Team), along with frameworks like the DPDP Act, are also increasing the need for stronger visibility, traceability, and operational control. At the same time, persistent skills constraints in IT operations are increasing the value of AI-driven automation, correlation, and decision support.
North America currently accounts for approximately 48 percent of the global AIOps market, reflecting the concentration of early adopters and large technology companies. However, APAC adoption is accelerating rapidly, driven by India’s unique combination of massive digital transaction volumes, sophisticated regulatory requirements, and a growing recognition among enterprise leaders that operational intelligence is not optional.
For Indian CIOs, the question is no longer whether to invest in AIOps but how to invest wisely, choosing platforms that deliver measurable ROI rather than adding another layer of complexity to an already complex technology landscape.
The AIOps Copilot: Bringing GenAI into IT Operations
The most significant recent development in AIOps is the integration of generative AI, not as a marketing feature, but as a practical tool that accelerates incident resolution and knowledge management.
Traditional AIOps platforms surface insights: ranked probable causes, correlated incidents, anomaly detections. But interpreting these insights still requires domain expertise. An engineer needs to understand the service topology, recall previous incidents, assess the remediation options, and make a decision. This interpretation step is where resolution speed bottlenecks in practice.
GenAI-powered copilots address this bottleneck by providing conversational access to operational intelligence. Instead of navigating dashboards and reading correlation reports, an engineer can ask natural language questions, ‘What changed in the last deployment?’, ‘What is the blast radius of this incident?’, ‘What fixed this issue last time?’, and receive contextual, data-driven answers that draw on the full breadth of the AIOps platform’s intelligence.
iStreet Network’s HEAL GenAI ‘Talk to Incidents’ copilot, anchor this conversational intelligence to specific outcomes: RCA acceleration, fix guidance, and impact assessment. The copilot is not a generic chatbot. It is an operational interface that understands your environment’s topology, your incident history, your remediation patterns, and your service dependencies, and uses that understanding to provide answers that are immediately actionable.
This represents a meaningful evolution in how operations teams interact with their intelligence platforms. Rather than requiring operators to extract insights from data, the copilot delivers insights proactively and contextually, reducing the cognitive load on engineers and enabling faster, more confident decision-making during high-pressure incidents.
What Indian Enterprise Leaders Should Prioritise
For enterprise technology leaders evaluating AIOps investments, the consolidating market demands a disciplined approach. Five considerations should guide the evaluation process.
First, apply the five-point capability test rigorously. Verify that the platform delivers genuine cross-domain ingestion, topology assembly, correlation, causality determination, and remediation, not just AI labels on conventional monitoring.
Second, assess architectural coherence. Platforms assembled through acquisition carry integration risk and architectural inconsistency. Purpose-built platforms deliver more predictable deployment timelines and more consistent operational behaviour.
Third, evaluate sovereign deployment and governance readiness. For Indian enterprises operating under requirements from regulatory bodies, and the DPDP Act, and other sector-specific frameworks, control over data, infrastructure, access, and auditability is increasingly important. A Sovereign AI Enterprise Platform should enable enterprises to maintain greater control over where data and AI workloads operate, how sensitive information is governed, and how operational evidence is maintained.
Fourth, demand measurable ROI evidence. The platform should demonstrate specific, quantifiable outcomes, MTTR reduction percentages, alert noise reduction ratios, capacity optimisation savings, downtime prevention metrics, from comparable enterprise deployments.
Fifth, consider the vendor’s strategic trajectory. In a consolidating market, the vendor’s independence, innovation roadmap, and commitment to the platform’s continued development are as important as the current feature set.
About iStreet Network
iStreet Network’s Sovereign AI Enterprise Platform, built on the Sanjeevani of AI™ framework, provides a sovereign enterprise architecture designed to support data residency, enterprise-controlled deployment, governance, and regulatory alignment. This enables Indian enterprises to maintain greater control over their data, AI workloads, and operational intelligence while strengthening security, compliance readiness, and digital resilience.
iStreet Network’s Resilient Operations portfolio — spanning AIOps and GenAIOps, Full-Stack Observability, Digital Experience Monitoring, and the Resiliency Operations Centre — is designed to meet all five criteria for India’s most demanding enterprise environments.
Talk to our advisors to evaluate how iStreet Network can deliver measurable AIOps outcomes within your enterprise environment.
Originally inspired by insights from HEAL Software, an iStreet Network AIOps product.
