Platform Engineering for Data Teams in the Age of AI: Building Autonomous, Intelligent Data Infrastructure

Abstract

The explosion of tools and disjointed workflows is posing a level of complexity to enterprise data infrastructure like never before, hindering agility in an organization. Platform engineering is a solution to such challenges, based on Internal Developer Platforms, which simplify infrastructure complexity by providing self-service and standard workflows. With Generative AI integrated into platform engineering, intelligent operating models are formed, with AI copilots generating infrastructure code, automatically enforcing governance, and anticipating failures before they strike. It is an AI-enhanced architecture that consists of five coordinated layers, including user interaction, AI intelligence, orchestration, infrastructure management, and observability layers. Context engines combine multi-dimensional data to drive specialist models to generate code, validate policies, and predict analytics. The empirical validation is showing significant gains in terms of deployment velocity, reliability, compliance with governance, cost optimization, and productivity of the developers. Organizations that adopt AI-enhanced platforms see transformational benefits in the reduction of incidents, consistency in policy enforcement, and operational efficiency, which confirms the use of the strategy as the basis of competitive advantage in data-driven markets.