[2/2] Perforce’s State of DevOps Report 2026 (Category DevOps)

Continuing my discussion of Perforce’s State of DevOps 2026, here are its main findings on DevOps maturity and AI. They reflect a management perspective: respondents were selected largely from managers making infrastructure and platform decisions in their companies.

1️⃣ Some 70% say DevOps maturity significantly influenced AI success Yet only 38% of organizations have embedded AI deeply across multiple SDLC stages. Another 38% use it frequently without standardization, and 17% remain at limited pilots. The maturity gap is large: 72% of leaders in high-maturity organizations report deeply embedded AI, compared with 43% in medium-maturity and 18% in low-maturity organizations. Scaling AI therefore depends on mature engineering processes, beyond buying tools.

2️⃣ AI inherits the operating model rather than fixing it The report describes 32% of organizations as highly standardized, 35% as mostly standardized, and 34% as still using partly standardized or chaotic delivery. Roughly a third therefore operate where outcomes depend on the particular team. Perforce calls this a variability problem: while workflows, environments, and governance vary between teams, AI results will vary too. Hence the emphasis on a control plane of shared templates, standards, pipelines, and managed environments.

3️⃣ Confidence in AI exceeds its actual integration into processes Some 77% trust AI outputs, but only 38% have deeply integrated AI into delivery, and only 39% have fully automated audit data. The authors state the measurement risk directly: organizations trust AI faster than they establish verification, auditability, and consistent measurement. Higher individual productivity in an IDE does not by itself mean mature AI-native delivery.

4️⃣ Economic benefits exist, but are not automatic Some 74% think AI faces inflated expectations. Perforce also reports stronger ROI where delivery systems are mature: high-maturity organizations are 36% more likely to automate 61%+ of deployments from commit to production, and 66% more likely to respond to production incidents “very effectively.” Among low-maturity organizations, 78% lack standardized delivery and only 19% handle incidents very effectively. Put simply, without mature DevOps, AI can accelerate work while also increasing rework, variability, downtime, and costs.

To me, this supports the central argument of my article “From a Traditional PDLC to AI-Native Development”: AI-native development means redesigning the whole system for creating, verifying, and delivering changes. In the article I go further and consider the distinct roles in that process :)

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