New research shows SRE and platform engineering teams are increasingly responsible for making AI trustworthy, scalable, and reliable

Dynatrace (NYSE:DT), the leading AI-powered observability platform, today released findings from The State of SRE and Platform Engineering 2026, a study examining how enterprises are orchestrating observability, automation, and AI to scale site reliability engineering (SRE) and platform engineering in large enterprises. The global survey of 919 IT leaders concludes that rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control.

The findings demonstrate how SRE and platform engineering teams are at the forefront of integrating new benchmarks, tooling, and capabilities for AI workloads into their reliability and development environments. Gartner^® projects that by 2028, 80% of enterprises will adopt SRE practices across their organizations, up from just 30% in 2024. Backed by executive support and shared ownership, these teams now carry growing accountability for the success or failure of AI initiatives, as organizations depend on them to evolve platforms, tooling, and standards.

Closing the Gap Between AI Development and AI Operations

These findings point to why Dynatrace recently announced its intent to acquire Arize. With 67% of SREs now naming AI model monitoring their top use case, and monitoring for model performance and accuracy already the most common AI-powered capability among SREs (58%), the demand for AI evaluation is outpacing the tools built to handle it. Yet AI is falling short on cost reduction and MTTR, and more than a third of platform engineers cite tool integration as their biggest barrier.

The Arize acquisition will help address this need directly: bringing AI-native evaluation into the observability platform itself, so teams building AI models and teams operating them in production are working from the same data instead of stitching together separate systems.

Why scale is the next big challenge for enterprises

The study demonstrates that SRE and platform engineering are now firmly established across large enterprises:

For SREs: 92% of organizations report executive leadership support for SRE initiatives
For platform engineers: 89% of organizations practicing platform engineering have implemented an internal developer platform (IDP), with 60% reporting broad adoption across departments
For both roles: 73% of SRE and platform engineering teams now collaborate and share responsibilities across reliability and platform domains

Together, these findings demonstrate how enterprises have invested deeply in reliability, automation, and developer productivity, and SRE and platform engineering teams are now expected to apply that foundation to the next phase of digital transformation. AI workloads are increasingly part of production infrastructure, but rising complexity, new telemetry, and novel ways of failing are placing increased demands on observability.

AI raises the bar for reliability and oversight

According to the study, agentic AI is driving new priorities and challenges:

For SREs: 89% use service-level objectives (SLOs) across at least some teams or systems. 67% say monitoring AI models are now their top use case.
For platform engineers: 55% prioritize enabling developers with AI-powered tools such as coding copilots and chatbots.

While AI technologies are generally meeting expectations for improving reliability and developer productivity, they are delivering less impact than expected in lowering costs and reducing mean time to resolution (MTTR). This gap highlights the need for greater system-level intelligence and workflow orchestration than simply adding AI tools to existing environments. Nearly half of SRE respondents stated that too many data sources and metrics hinder their ability to define and manage effective SLOs.

Notably, teams are intentionally prioritizing visibility and human oversight before expanding automation, and monitoring AI systems for model performance and accuracy is SREs’ most common AI-powered capability (58%).

Observability is emerging as the control plane for AI-driven operations

As enterprises push toward greater automation and AI-assisted operations, observability is becoming foundational to AI governance, reliability, and optimization across SRE and platform engineering. However, integration, complexity and fragmented data are emerging as major barriers to progress:

More than a third (37%) of platform engineers report that integrating with existing tools and systems is their top challenge.
Only 40% of platform engineers report embedding observability across all deployment stages.

Half of SREs now use AI‑powered capabilities for automated incident response, signaling a shift toward agentic operations where observability must act as the control plane that governs when and how autonomous actions are taken.

"SRE and platform engineering laid the groundwork for modern digital reliability, but AI is rewriting the rules. Enterprises need to now move from managing systems to orchestrating them, connecting observability, automation, and agentic AI to operate at the speed these initiatives demand, turning insight into action at scale," said Steve Tack, Chief Product Officer at Dynatrace. "This research also reflects why we recently announced our intent to acquire Arize. AI engineering teams have been evaluating in one set of tools while operations teams monitor in another, and that gap is no longer sustainable as AI moves deeper into enterprise production."

Download The State of SRE and Platform Engineering 2026: How enterprises are orchestrating observability, automation and AI to scale reliability report here.

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FAQ

What does The State of SRE and Platform Engineering 2026 research cover, and who did you survey?

The State of SRE and Platform Engineering 2026 is a global Dynatrace research report based on a survey of 919 senior IT leaders, decision makers, managers, and supervisors involved in site reliability engineering, platform engineering, or IT operations at enterprises with annual revenues of $500 million or more. The report examines how organizations are orchestrating observability, automation, and AI to scale SRE and platform engineering practices as AI workloads move into production.

How is AI changing what SRE and platform engineering teams are responsible for?

AI is expanding the scope of both disciplines beyond their original mandates. For SREs, monitoring AI models for performance and drift is now the top use case, surpassing incident response and SLO management. For platform engineers, AI tools require provisioning and governance frameworks that existing platforms weren't built to support. In both cases, accountability for AI outcomes is outpacing the operational frameworks teams have to manage it.

Why are SRE and platform engineering teams becoming more important as AI scales?

As AI workloads move further into production environments, SRE and platform engineering teams are increasingly responsible for the reliability, scalability, and trustworthiness of those systems. The report found that rapid AI adoption is redefining what these teams must deliver, forcing organizations to rethink how they manage scale, automation, and control.

What are the key findings from the report?

The research shows that SRE and platform engineering practices are now firmly established across large enterprises, with 92% of organizations reporting executive leadership support for SRE initiatives, 89% of organizations practicing platform engineering having implemented an internal developer platform, and 73% of SRE and platform engineering teams collaborating and sharing responsibilities across reliability and platform domains.

Why does this report cover SRE and platform engineering together rather than separately?

Both disciplines are being reshaped by the same AI forces simultaneously, but in distinct ways. SREs own production reliability; platform engineers build the platforms developers depend on. Studying them together reveals how AI is stress-testing the entire operational stack, from platform to production, in ways a single-discipline study would miss. Seventy-three percent of these teams already collaborate and share responsibilities.

What new failure modes are AI introducing, and what capabilities do SRE and platform engineering teams need to address them?

AI systems fail differently than traditional software: model drift, inconsistent outputs, rising inference costs, and data security risks don't surface through conventional infrastructure metrics, requiring new instrumentation, governance, and observability capabilities. This helps explain why 67% of SREs and 63% of platform engineers prioritize AI capabilities as the most important observability capability.

Methodology

This report is based on a global survey of 919 senior leaders, decision makers, managers, and supervisors, directly involved in or responsible for site reliability engineering, platform engineering, or IT operations in enterprises with annual revenues of $500 million or more. It was conducted and analyzed by Qualtrics partner Y2 on behalf of Dynatrace between October 2025 and January 2026. Respondents represented organizations across the Americas, EMEA, and Asia-Pacific.

About Dynatrace

Dynatrace is advancing observability for today’s digital businesses, helping to transform the complexity of modern digital ecosystems into powerful business assets. By leveraging AI-powered insights, Dynatrace enables organizations to analyze, automate, and innovate faster to drive their business forward. To learn more about how Dynatrace can help your business, visit www.dynatrace.com, visit our blog and follow us on LinkedIn and X @dynatrace.

Curious to see how you can simplify your cloud and maximize the impact of your digital teams? Let us show you. Sign up for a 15-day Dynatrace trial.

Cautionary Language Concerning Forward-Looking Statements

This press release includes certain "forward-looking statements" within the meaning of the Private Securities Litigation Reform Act of 1995, including statements regarding the expected benefits of the proposed Arize acquisition, capabilities expected to be available to organizations from using Dynatrace and Arize following the closing of the proposed acquisition, and the expected future benefits to organizations from using Dynatrace and Arize following the closing of the proposed acquisition. These forward-looking statements include all statements that are not historical facts and statements identified by words such as "will," "expects," "anticipates," "intends," "plans," "believes," "seeks," "estimates," and words of similar meaning. These forward-looking statements reflect our current views about our plans, intentions, expectations, strategies, and prospects, which are based on the information currently available to us and on assumptions we have made. Although we believe that our plans, intentions, expectations, strategies, and prospects as reflected in or suggested by those forward-looking statements are reasonable, we can give no assurance that the plans, intentions, expectations, or strategies will be attained or achieved. Actual results may differ materially from those described in the forward-looking statements and will be affected by a variety of risks and factors that are beyond our control, including our ability to successfully complete the Arize acquisition and integrate the newly acquired business and offerings, the risks set forth under the caption "Risk Factors" in our most recent Annual Report on Form 10-K, subsequent Quarterly Reports on Form 10-Q, and our other SEC filings. We assume no obligation to update any forward-looking statements contained in this document because of new information, future events, or otherwise.

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