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, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace based on a survey of 919 IT leaders examining how enterprises are orchestrating observability, automation, and AI to scale site reliability engineering (SRE) and platform engineering in large enterprises.
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."
