Platform engineers are increasingly at the forefront of AI adoption in enterprises, but new research shows that collaboration and infrastructure gaps could limit the technology’s full impact. Vultr, the world’s largest privately-held cloud infrastructure company, released findings from its annual State of AI in Platform Engineering survey, highlighting both the progress and the challenges in AI deployment.
According to the report, 75% of platform engineering teams are already hosting or planning AI workloads, and 89% use AI daily for tasks such as code generation and documentation. Yet, the study points to an “AI implementation plateau,” where early adoption momentum is not translating into enterprise-wide value.
Key trends and challenges
The survey of over 120 professionals building AI-native systems reveals several gaps:
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Fragmented ownership: Nearly 40% of organizations assign AI platform responsibilities to platform engineering teams, 25% report shared ownership, and 13% have no clear ownership.
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Infrastructure maturity gaps: Over 40% use Kubernetes extended for GPUs and AI workloads, while 35% do not orchestrate AI workloads at all.
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Pipeline limitations: While 58% embed AI into cloud-native applications, 41% have yet to adapt CI/CD or DevSecOps pipelines for AI.
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Deployment flexibility: 16% of organizations take a hybrid approach, and 9% still run GPU workloads on-premises.
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Standardization needs: More than half of respondents consider AI infrastructure templates and blueprints critical to safe, scalable adoption.
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Collaboration gaps: Nearly a third report limited collaboration with data science teams, while 16% report no collaboration at all.
Turning momentum into enterprise impact
“We haven’t seen adoption rates like this for a new technology since the 1990s; it’s incredible,” said Luca Galante, core contributor to the Platform Engineering Community. “But most enterprise AI today is still more experimental than strategic. Platform engineers are leading the way, but turning momentum into measurable impact will require stronger foundations.”
Kevin Cochrane, CMO of Vultr, added: “Platform engineers are quickly becoming the linchpin of enterprise AI adoption. Momentum alone isn’t enough. Teams need AI-first infrastructure that makes workloads safe, repeatable, and scalable. Vultr provides that foundation, helping platform teams move past experimentation toward enterprise-scale results.”
With GPU-ready instances, global orchestration, and composable architectures built for advanced MLOps, Vultr empowers platform engineers to break through the AI implementation plateau and deliver tangible enterprise value. The report underscores the growing role of platform engineering as a bridge between AI innovation and measurable business outcomes.
