The Data is in: Where Teams are Getting Stuck Going AI-Native

AI integration in engineering teams is facing significant challenges due to a mismatch between traditional software systems and the dynamic nature of AI workloads, as highlighted by Platform Engineering's annual survey. The primary obstacles include human factors such as skills gaps and siloed teams, with 57% of organizations citing a lack of expertise as a major barrier. Additionally, legacy pipelines struggle to accommodate AI's demands, with 51% of respondents finding it difficult to integrate AI into existing systems, and 41% failing to adapt their CI/CD pipelines for continuous learning models. To overcome these hurdles, the report suggests adopting a composable and modular infrastructure using principles like Infrastructure-as-Code (IaC), enabling flexible GPU access, and moving inference to the edge to enhance system adaptability and efficiency. Emphasizing the need for standard DevOps principles to be applied to AI models, the text argues for a shift from experimental approaches to treating AI as a core business capability, thereby building infrastructure that evolves alongside AI models.

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