groundcover, the world’s leading bring-your-own-cloud (BYOC) observability platform, today released its new industry report, The Observability Imperative: From Monitoring Layer to AI Decision Infrastructure in 2026, revealing that observability has moved beyond monitoring to a data foundation that powers modern AI, and most organizations aren’t ready for what it means. Administered by Atomik Research and based on a survey of 500 U.S. technology professionals, including observability, SRE, platform and engineering leaders, the findings show organizations are moving beyond reactive monitoring toward AI-powered decision infrastructure, even as cost volatility and visibility gaps slow the transition.
Among the key findings: 49% of respondents say half of their observability costs are due to AI workloads. Nearly nine in 10 respondents (89%) report using observability data for forward-looking decisions, with 47% saying it’s continuously embedded into AI, product or operational workflows. This signals a fundamental shift in how engineering organizations deploy observability.
“We know that observability has moved well beyond detecting outages, and this research makes clear that engineering organizations are already treating it as core infrastructure for understanding, operating and ultimately automating complex AI systems,” said Shazar Azulay, CEO and co-founder of groundcover. “The organizations that work to close the visibility gap now, especially around AI workloads, will move from reacting to incidents to actively shaping their outcomes. On the flip side, those that don’t maintain the high fidelity data required of AI systems risk operating with a fundamentally incomplete picture of their own systems.”
Observability Is Becoming Decision Infrastructure
Modern engineering teams generate more telemetry today than at any point in history, with Kubernetes, microservices and agentic AI systems transforming observability into one of the most data-intensive layers of the modern tech stack.
The 2026 report examines how organizations are navigating this transition across five dimensions: adoption, cost, AI visibility, trust and the emerging metrics era. The research finds strong momentum toward a more intelligent, proactive model:
- 87% of respondents say AI or automation is already integrated into observability workflows, including 34% where it is fully operational and trusted.
- 39% expect to adopt AI-driven recommendations and automation over the next 12–18 months.
- 31% expect observability to become fully embedded in strategic, AI-enabled decision-making.
The data indicates that adoption has outpaced trust. While 87% report AI integration, only 34% describe it as fully operational and trusted, a 53-point gap that’s among the most consequential findings in the survey. The report attributes this not to model quality but to a data-fidelity problem: when platforms sample the most relevant spans away to control costs, AI features are forced to reason using an incomplete picture.
- 39% of organizations spend between $1 million and $5 million annually on observability.
- 53% experienced budget overages of 10% or more in the last fiscal year, including 42% with moderate overages of 10–30%.
- 49% say 26–50% of their current observability costs are now attributable to AI workloads.
Key cost drivers include high-cardinality metrics and excessive data ingestion (39%), higher-fidelity telemetry requirements for AI systems (37%), and overlapping or fragmented tooling (31%).
Teams are responding, with 79% saying they’ve implemented adaptive sampling, changed retention policies, renegotiated contracts, or begun exploring open-source alternatives. But the report cautions that aggressive sampling cuts deepest where visibility matters most, creating a false trade-off in which the costs saved now become the visibility gaps that drive the next cost spike.
The AI Visibility Gap: New Systems Create New Blind Spots
Confidence in traditional observability is relatively strong, with 55% rating their coverage as “good.” But AI systems introduce distinct challenges that existing tools weren’t made to address:
- Model behavior versus infrastructure ambiguity: 38% say it’s difficult to determine whether failures originate in the model or the underlying infrastructure.
- Lack of visibility into external APIs and LLM providers: 34% can’t adequately observe the behavior of the external AI services their systems depend on.
These gaps are particularly severe because AI workloads generate fundamentally different signals, including prompt execution paths, token-level outputs and multi-agent coordination patterns, that traditional observability was not built to capture.
Fragmented tooling compounds the problem. When the trace that captured a failed agent request lives in one tool, the metric that captured the cardinality spike in another, and the relevant log line in a third, engineers are reconstructing incidents across multiple platforms, with each only telling a fragment of the story. More than a third (34%) of respondents cite data silos or fragmented tooling as a barrier to proactive decision-making, and 35% say their organizations are using cost pressure as a trigger to consolidate vendors.
What Teams Are Prioritizing for Agentic AI
As agentic AI systems mature, 67% of respondents say AI system performance is already systematically tracked. Top observability priorities for agentic AI environments include:
- Reasoning accuracy, explainability and visibility (48%)
- Tool and API reliability (44%)
- Energy or cost efficiency (38%)
The findings indicate that teams are shifting their observability focus from infrastructure health to AI behavior itself. The fact that two-thirds are already systematically tracking AI system performance suggests this isn’t a future concern; it’s an active priority today.
For more information, download the full report.
Schedule a meeting with the team while at Microsoft Build 2026 on June 2-3 (San Francisco, CA) or stop by booth G-222.
Research Methodology
The survey included 500 technology professionals in the United States whose roles focus on observability, including SRE, platform engineering, and engineering leadership. Fieldwork was conducted April 3–14, 2026, by Atomik Research, an independent third-party creative market research agency headquartered in Bentonville, Arkansas. The margin of error is ±2 percentage points at a 95% confidence level.
About groundcover
groundcover is a cloud native observability platform powered by eBPF. It runs inside the customer’s cloud and provides complete visibility into applications, infrastructure, networks and AI systems without operational overhead. The platform offers unlimited data coverage at a fraction of the cost of legacy observability tools. Learn more at https://www.groundcover.com.
View source version on businesswire.com: https://www.businesswire.com/news/home/20260526456726/en/
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