Perforce Software’s 2026 State of DevOps report examined how AI tools impact engineering roles, governance, and cloud costs.
70 percent of the organisations report their DevOps maturity materially affects their success with AI. Rather than replacing established delivery practices, proper foundational workflows serve as the prerequisite for scaling these capabilities.
“The market often asks whether AI will replace DevOps. Our research shows the opposite: AI amplifies DevOps,” explains Anjali Arora, CTO of Perforce.
The standardisation imperative
High-maturity organisations see 72 percent of their leaders embed AI practices across the software delivery lifecycle. Mid-maturity teams report 43 percent embedment, and lower-maturity counterparts trail at 18 percent. Scaling bottlenecks rarely relate to tooling alone; they stem from cross-team coordination issues and skills gaps, each cited by 25 percent of respondents.
To counter workflow variance, leading teams build Internal Developer Platforms to supply unified pipelines and consistent environments. Standardisation yields economic productivity and limits unpredictable costs.
High-maturity organisations are almost twice as likely to run hybrid DevOps-platform engineering delivery models, sitting at 79 percent compared to 45 percent for lower-maturity groups.
“Organisations with disciplined engineering practices, automation, strong collaboration, and focus on control, auditability, and governance are the ones scaling AI successfully and turning innovation into measurable business outcomes,” says Arora.
Hybrid infrastructures and evolving roles
Testing workloads reflect this standardisation push. A consensus has formed around hybrid infrastructures, with 63 percent of organisations operating across cloud and on-premises environments. Functional testing remains a core component for 71 percent of teams.
As test execution becomes highly automated, AI is actively shifting roles across DevOps teams, particularly within testing workloads. 87 percent of respondents believe these tools will enable engineers to focus less on scripting and more on system design and directing outcomes.
55 percent of QA teams now concentrate on quality analytics rather than execution. At the same time, 53 percent of developers author tests directly. 41 percent report QA teams are evolving into Quality Engineering teams, with 39 percent citing a focus on orchestration across pipelines, environments, and data.
Shared ownership ensures testing strategies validate business logic, especially as 38 percent of organisations report business analysts participate in test creation.
Jake Hookom, EVP of Product at Perforce, said: “The research confirms what we are already seeing: AI is helping teams shift up from execution to oversight and strategy, effectively elevating individual roles.”
The AI confidence gap and DevOps governance risks
A disconnect exists between perceived capabilities and actual governance. While 77 percent of respondents express confidence in outputs (with 57% citing operational efficiency and 49% noting productivity returns) only 38 percent have deeply embedded these practices across multiple delivery stages.
Auditability remains a vulnerability. Compliance oversight is split between multiple functions, and only 39 percent of organisations maintain fully automated audit trails. Manual compliance checks create bottlenecks and increase regulatory risk under high-velocity delivery.
Security practices also demand attention as DevOps teams increasingly adopt AI. While 52 percent report secure coding practices embedded in CI/CD pipelines, 44 percent cite limited skills or training as a barrier. As Hookom points out, governance and auditability “need to be a focus for organisations and the collaboration between teams.”
Overall, 74 percent of respondents say AI meets or exceeds expectations. Organisations evaluate these tools based on efficiency and revenue outcomes. 50 percent measure value through improved customer retention or acquisition, while 48 percent track faster delivery of new features.
Scaling these capabilities naturally multiplies compute consumption and infrastructure demands. 74 percent report that cloud and compute costs, alongside energy usage, influence their decisions about adoption. 37 percent cite these specifically as limiting factors. Teams must implement cost attribution across infrastructure and usage-based charges to prevent these bills from eroding efficiency gains.
The era of adopting tools without accountability has ended. DevOps is not fading; rather, it is becoming the economic and operational foundation for AI at scale. Engineering leads should integrate compliance solutions that streamline adherence while carefully monitoring infrastructure costs.
See also: Using AI to speed up XR development and WebXR prototyping
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