In this article
Executive Summary
The Infrastructure Visibility Gap is No Longer Acceptable
IT operations have outgrown the model they were built on. Enterprises now monitor tens of thousands of metrics, ingest terabytes of logs, and generate thousands of alerts daily, all while managing increasingly complex infrastructures that span on-prem data centers, multiple cloud environments, and emerging AI workloads. Yet despite all this telemetry, too many teams still learn about outages from customers before they see them in their tools.
Recent high-profile outages at CrowdStrike, Cloudflare, and others have demonstrated just how quickly a small issue can ripple across industries, interrupt daily life, and cost companies billions.
The next phase of AI-first observability requires something bigger. It must extend beyond the data center and cloud. It must encompass the Internet itself, where applications, identity, payments, APIs, and user experience actually live. This is where business resilience is won or lost. This convergence of hybrid infrastructure observability, Internet Performance Monitoring, and Digital Experience Monitoring is the real beginning of autonomous IT.
The Convergence
Five Forces Converging Toward Autonomous IT
Survey data from 100 VP+ IT decision-makers reveals five forces converging to accelerate the shift to autonomous operations. Each one reinforces the others, creating a cycle already forming inside the highest-performing organizations.
1
Observability budgets are protected infrastructure
2
Consolidation is the optimization strategy
3
Platform loyalty is giving way to agility
4
Current tools aren’t delivering actionable insights
5
AI adoption is maturing, but most have significant runway
1. Observability Budgets are Protected Infrastructure
Cost pressure is real. Organizations are being asked to do more with less. Yet observability budgets aren’t following the typical pattern. 96% of IT leaders expect observability spending to hold steady or grow over the next 12-24 months, with 62% anticipating increases.
This isn’t immunity from budget scrutiny; it’s evidence that observability has become foundational, strategic infrastructure that leaders protect. Every company now has IT at its operational core—whether retail, banking, healthcare, or manufacturing—and protecting uptime has become non-negotiable for business delivery.
While AI initiatives command the highest share of strategic focus (cited by 63% of leaders as a top priority), cost savings are coming from other areas, not the systems that keep infrastructure visible and operational.
Protected budgets don't mean static spending. Organizations are actively reallocating spending toward outcomes rather than tools.
2. Consolidation is the Optimization Strategy
84% of organizations are pursuing or considering tool consolidation. 41% are actively consolidating, while another 43% are evaluating it. Leaders now view consolidation as the most effective way to reduce cost, improve service delivery, and unlock the unified data foundation that AI requires.
The math is straightforward. Organizations running 2-3 observability platforms (66% of respondents) or 4-5 platforms (18%) pay for overlapping capabilities, duplicate data pipelines, integration maintenance, plus bear the operational overhead of context-switching during incidents.
The gap between the current state and the desired state is massive. 74% indicate openness to a single platform if it meets requirements—a remarkable willingness to consolidate in an industry historically resistant to vendor concentration.
Key Challenge: Siloed Tools Create Visibility Gaps
51% cite relying on multiple tools with siloed views and no unified visibility as their top challenge. During production incidents, engineers context-switch between platforms, manually correlate data across systems, and waste critical minutes assembling the complete picture.
The 2024 CrowdStrike outage is estimated to have cost Fortune 500 companies over $5 billion. Not a single industry was spared.
The consolidation wave creates two outcomes that enable autonomous IT: it generates budget savings that can be reinvested in AI capabilities, and it establishes the unified data foundation that AI requires to work effectively.
3. Platform Loyalty is Giving Way to Agility
67% of IT leaders say their organization is likely to switch observability platforms within 1-2 years. This represents a fundamental shift in enterprise software buying behavior. Platform decisions that once took years to revisit are now being reconsidered on 12-24 month cycles.
The likelihood of switching breaks down as follows: 17% very likely (actively exploring or planning changes), 50% somewhat likely (open to switching if a strong case emerges), 27% not very likely, and 5% not at all likely.
Top reasons include new company initiatives requiring better monitoring (27%), security and compliance mandates (22%), a need to replace outdated tools (19%), major outages highlighting monitoring gaps (13%), and regular technology refresh cycles (11%).
The barriers to switching are operational rather than strategic. With the rise of OpenTelemetry and API-based integrations, switching costs are lower, and IT leaders now prioritize openness and Internet-aware visibility over ever-increasing ingest costs.
4. Current Tools Aren’t Delivering Actionable Insights
Only 41% of IT leaders report satisfaction with their platform’s ability to derive useful insights from collected data. This means 59% are sitting on mountains of telemetry without the tools to turn data into action and prevention.
The Dissatisfaction Breakdown
The satisfaction gap reveals itself in specific pain points:
-
38% cite a lack of advanced insights as a top barrier
-
6% struggle with alert fatigue, receiving hundreds or thousands of notifications daily while missing critical issues
-
39% report integration gaps that prevent monitoring tools from working seamlessly with ITSM systems and DevOps workflows
The problem isn’t data collection—it’s correlation, context, and causality. Traditional observability tools were built for simpler architectures and smaller data volumes. They struggle with high-cardinality data from containerized environments, correlating metrics, logs, and traces across systems, and identifying root causes in distributed architectures.
This dissatisfaction creates demand for AI-powered capabilities that deliver measurable outcomes: automated correlation and root cause analysis, predictive capabilities that identify issues before they impact users, and intelligent alerting that reduces false positives.
5. AI Adoption is Maturing, But Most Organizations Have Significant Runway
Just 4% of organizations have reached full operational maturity, fully leveraging AI across IT operations. Another 12% are using AI to automate root cause analysis and remediation, while 13% rely on AIOps mainly for anomaly detection and incident response. The majority—49%—are still piloting or experimenting with AI in limited environments, and 22% haven’t adopted it yet.
AI adoption has clearly begun, but scaling it is where progress stalls. 62% of organizations have started implementing AI—piloting, testing, or using it in limited ways—but haven’t yet operationalized it across IT.
Leaders Want Automation with Guardrails
IT leaders need policy-driven actions with approval workflows, integration with existing governance processes, and explainability that shows why AI flagged an issue and which data contributed to the determination. Black-box systems that can’t explain their reasoning erode trust and limit adoption.
The 78% that haven't reached full operationalization aren't stuck because AI doesn't work. They're stuck because they've been trying to run AI on fragmented data, disconnected tools, and platforms that can't explain their reasoning.
The Convergence Effect
Why Autonomous IT is Closer Than You Think
These five behavior shifts don’t exist in isolation. They form a reinforcing cycle that accelerates the shift toward autonomous IT.
The cycle begins with cost pressure. While observability budgets are protected within IT organizations, there is a rationalization of total spend with consolidation as the optimization strategy—reducing vendor count, eliminating duplicate capabilities, and cutting integration overhead. But consolidation delivers more than cost repurposing. It creates the unified data foundation that effective AI requires.
That unified foundation enables AI capabilities that actually work: automated correlation, root cause analysis, predictive alerting, and eventually autonomous remediation. These capabilities deliver measurable outcomes, like reduced MTTR, less alert fatigue, and fewer incidents reaching production. And measurable outcomes justify continued investment, which protects observability budgets even when other spending gets cut.
Protected budgets restart the cycle. Organizations with stable funding can pursue the next round of optimization and capability building, widening their advantage over competitors still stuck in reactive operations.
IT leaders report two additional accelerators of this cycle: dissatisfaction with current tools creates urgency to move, and declining platform loyalty removes the friction that once kept organizations locked into underperforming solutions.
The Path Forward
The Mandate for IT Leaders
This convergence creates a clear mandate: autonomous IT is no longer a future-state vision. It’s the 2026 operational requirement.
Organizations face a decision point. Continue managing observability as a collection of disconnected tools and manual processes, or move decisively toward unified platforms that enable AI-powered autonomous operations. The window to act is open now—but it won’t stay open indefinitely.
The Path Forward Requires Three Sequential Moves
- Consolidate observability tooling to create a unified data foundation across infrastructure, applications, and user experience.
- Extend visibility to the Internet layer where applications, APIs, and customer experience actually live—closing the gap between internal telemetry and business outcomes.
- Deploy AI-powered automation that moves from reactive alerting to predictive prevention and autonomous remediation.
The organizations that execute this path will gain a competitive advantage through improved reliability, faster innovation cycles, and reduced operational overhead. Those who delay will find themselves managing increasingly complex infrastructure with insufficient tooling while competitors operate autonomously.
The technology exists. The budgets are available. The switching windows are open. The market is ready. The question for IT leaders isn't whether autonomous operations will become standard. It's whether you'll be among those who define that standard or those scrambling to adapt to it.
© LogicMonitor 2026 | All rights reserved. | All trademarks, trade names, service marks, and logos referenced herein belong to their respective companies.
