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State of DevOps Report: Platform Engineering Edition 2026
- Letter from the Authors
- About the Research: Methodology, Sample, and Objectives
- Introduction: How AI Is Changing the Stakes for Platform Engineering
- Chapter 1: AI Is an Amplifier: Why Platform Maturity Determines Success
- Chapter 2: From Experimentation to Autonomy: Where AI Actually Works
- Chapter 3: Governance Becomes Software: The Rise of Embedded Control
- Chapter 4: Trust Is an Outcome: How Platform Engineering Enables AI Confidence
- Chapter 5: Avoiding Fragmentation: The New Role of Platform Teams
- Conclusion: The True State of Platform Engineering
Report > State of DevOps Report: Platform Engineering Edition 2026
About the Research: Methodology, Sample, and Objectives
Research Methodology and Respondent Profile
This report is part of the 2026 State of DevOps Report. This decade-spanning report has drawn insights from over 40,000 technology practitioners and leaders since its inception, making it an unparalleled resource for understanding the evolution and impact of DevOps.
Back to topMethodology
This study was conducted through a 20-minute online survey with 820 global IT decision-makers (itdms), purchase influencers, and DevOps practitioners.
Back to topRespondent Breakdown by Region
Participants were sourced globally, with representation across key regions:
| Label | Value |
|---|---|
| North America | 31.1 |
| Europe | 27.4 |
| Asia Pacific | 21.1 |
| Latin America | 14.4 |
| Middle East/Africa | 6 |
Research Objectives
The study aimed to explore how platform engineering teams are navigating the impact of AI adoption while maintaining governance, resilience, and trust. Key objectives included:
1. Understanding the Impact of AI on Platform Engineering
- How AI-driven automation is reshaping infrastructure provisioning, configuration management, and operational decision-making.
- How platform teams balance increasing speed with the need for control, stability, and oversight.
2. Assessing Risk, Resilience, and Platform Maturity
- Evaluating whether AI adoption amplifies system risk or strengthens resilience.
- Identifying how platform maturity influences the ability to maintain auditability, security posture, and operational trust.
3. Adoption of Governance and Control Mechanisms
- Examining how policy-as-code, guardrails, and standardized workflows evolve in AI-driven environments.
- Understanding how platform teams operationalize governance through shared platforms and internal developer platforms.
4. Role of Platform Teams in AI Governance and Trust
- Investigating how platform teams act as stewards of governance across DevOps, SRE, and broader engineering functions.
- Understanding how platform teams contribute to establishing and maintaining AI trust within organizations.
5. Benchmarking Platform Engineering Outcomes
- Measuring the relationship between platform engineering maturity and governance, compliance, and resilience outcomes.
- Identifying signals that distinguish organizations that successfully adopt AI while maintaining control and traceability.
Limitations
Results reflect self-reported practices and perceptions.