The News
Dynatrace has released its State of Observability 2025 report, revealing that 100% of surveyed organizations now use AI, yet most remain fragmented in execution. The study calls for a unified strategy to link AI initiatives with measurable business outcomes, positioning AI-powered observability as a central control plane for managing risk, efficiency, and sustainability. Dynatrace pairs this analysis with a 90-day action plan to help leaders operationalize observability data for ROI and resilience. Read the full report here.
Analysis
Observability Becomes the Control Plane for Enterprise AI
The State of Observability 2025 findings reflect a major inflection point: observability is evolving from a technical practice to a strategic business enabler. The report confirms that 75% of organizations are increasing observability budgets, and AI capabilities are now the #1 buying criterion. This aligns with ECI and theCUBE Research’s Day 2 study, where 61.3% of enterprises plan to expand observability investments and 54% already use full-stack observability tools.
AI has transformed observability into a decision fabric: a unified intelligence layer that connects telemetry with business KPIs, enabling real-time insight into cost, carbon impact, customer experience, and resilience. This shift mirrors the broader enterprise movement toward agentic operations, where AI systems act autonomously on observability data to optimize performance and prevent incidents before they occur.
Trust, Governance, and the Human Loop
A key insight from Dynatrace’s research is the “AI trust gap.” While every organization now uses AI, 69% of AI-powered decisions are still verified by humans. This signals that governance and accountability remain essential. This finding echoes theCUBE Research’s DevSecOps study, where 42.4% of enterprises are significantly increasing security budgets and 44.5% plan to strengthen identity and access management.
For developers and platform teams, this reinforces a growing consensus: AI systems need observable transparency and human-in-the-loop validation. Dynatrace’s framing of “automate with guardrails” captures this balance well by advocating for generative and predictive AI to automate remediation while maintaining policy checks and rollback controls. In an era of autonomous agents and self-healing systems, trust isn’t automatic; it’s engineered.
Business Observability and Measurable Impact
One of the report’s most notable findings is that only 28% of organizations currently align observability data with business KPIs, despite recognizing its potential. Yet Dynatrace’s framework for connecting technical metrics to business outcomes (MTTR, SLOs, cost per request, revenue at risk, and CX scores) offers a practical blueprint for leaders who need to justify AI investments in financial terms.
This approach reflects trends from ECI’s AppDev Done Right research, where 93.3% of organizations track SLOs but nearly half spend excessive time identifying root causes. The implication is clear: the next competitive advantage will come from correlating operational performance with business performance, enabling developers and executives to speak the same language.
AI-Driven Observability as a Sustainability Lever
Dynatrace also positions observability as a vector for sustainability and cost optimization, which is a growing theme in modern FinOps and platform engineering. By correlating telemetry with energy consumption (kWh) and CO₂ emissions, organizations can uncover inefficiencies that cut both financial and environmental costs. In ECI’s Day 2 findings, 59.4% of leaders already prioritize automation and AIOps to accelerate these outcomes. Observability is becoming not only the nervous system of IT, but also a conscious system of accountability, linking digital health with operational sustainability.
From Insights to Action: A 90-Day Framework
Dynatrace’s 90-day roadmap transforms abstract observability goals into concrete execution:
- Days 1–30: Instrument key customer journeys and close telemetry gaps.
- Days 30–60: Map observability data to KPIs; pilot AI-assisted security and cost optimization use cases.
- Days 60–90: Automate common remediation workflows using generative AI with human approvals.
This structure mirrors ECI’s enterprise AI maturity research, which emphasizes incremental automation and continuous feedback loops. By focusing on quick wins and traceable business metrics, Dynatrace provides a pragmatic blueprint for organizations struggling to connect AI investments to measurable ROI.
Looking Ahead
Dynatrace’s 2025 report marks a pivotal shift in how enterprises define observability. It’s no longer a downstream practice of monitoring systems; it’s an upstream discipline of running AI responsibly. As AI-native observability becomes embedded into DevOps, security, and sustainability strategies, the line between observing and acting will blur.
For developers, this means greater reliance on self-healing architectures and AI-driven diagnostics, guided by clear governance and compliance frameworks. For business leaders, it’s a mandate to translate observability into value that ties uptime, performance, and automation directly to financial and environmental outcomes.
As agentic AI expands across industries, observability will evolve into the control plane of the intelligent enterprise, measuring not only what’s happening in systems, but how effectively AI is helping humans make better, faster, and more accountable decisions.
