As enterprises accelerate the deployment of AI into production environments, Site Reliability Engineering (SRE) and Platform Engineering teams are taking on a broader role in ensuring AI systems remain reliable, scalable, secure, and trustworthy, according to a new global study released by Dynatrace.
The State of SRE and Platform Engineering 2026 report, based on a survey of 919 senior IT leaders, managers, and decision-makers, highlights how rapid AI adoption is redefining the responsibilities of teams responsible for digital operations and infrastructure. The study found that enterprises are increasingly relying on SRE and platform engineering teams to support AI workloads, manage automation, and establish the controls necessary for large-scale AI deployment.
According to the report, AI is moving beyond experimentation and becoming a core component of production infrastructure. As a result, organizations are investing heavily in reliability and developer productivity initiatives. The research found that 92% of organizations report executive support for SRE initiatives, while 89% of enterprises practicing platform engineering have implemented internal developer platforms. Additionally, 73% of SRE and platform engineering teams now collaborate and share responsibilities, reflecting the growing convergence of operational and platform functions.
AI is also introducing new operational priorities. The study revealed that 67% of SRE professionals now consider AI model monitoring their top use case, while 55% of platform engineers prioritize enabling developers through AI-powered tools such as coding copilots and chatbots. Monitoring AI model performance and accuracy has become one of the most important capabilities for ensuring reliable AI operations.
However, the benefits of AI are being accompanied by increasing complexity. Nearly half of SRE respondents said that managing excessive data sources and metrics makes it difficult to define and maintain effective service-level objectives (SLOs). For platform engineering teams, tool integration remains the biggest challenge, with 37% citing integration with existing systems as a significant obstacle.
The findings also highlight the growing importance of observability in AI-driven environments. As organizations move toward autonomous and agentic operations, observability platforms are emerging as critical control layers that connect reliability, automation, governance, and security. The report found that half of SRE teams already use AI-powered capabilities for automated incident response, signaling a shift toward more autonomous operational models.
Commenting on the findings, Steve Tack, Chief Product Officer at Dynatrace, said AI is fundamentally changing how enterprises manage digital operations. He noted that organizations must evolve from simply managing systems to orchestrating increasingly complex environments through a combination of observability, automation, and AI.
The report concludes that while AI is improving productivity and operational efficiency, enterprises still face challenges around governance, integration, visibility, and control. As AI becomes more deeply embedded in business operations, organizations will need stronger observability and operational frameworks to ensure reliability, compliance, and scalability at enterprise scale.
