Enterprise Readiness for Generative AI: The Critical Role of Data Engineering

Abstract

Generative artificial intelligence (GenAI) is accelerating enterprise transformation by enabling natural language interfaces, automated knowledge work, and new modes of decision support. Yet many GenAI initiatives stall after pilot stages—not because models underperform, but because enterprises lack the data engineering maturity required to operationalize these systems at scale. This paper argues that enterprise readiness for GenAI is primarily a data engineering problem: GenAI outcomes depend on the ability to reliably ingest, integrate, govern, and serve high quality data across fragmented environments. Through a literature based conceptual analysis, the study synthesizes the data engineering capabilities most critical to GenAI adoption, including scalable data pipelines, modern storage and processing architectures, integrated enterprise data platforms, and robust governance mechanisms for security, privacy, lineage, and compliance. The paper proposes a conceptual framework that links enterprise readiness dimensions (digital infrastructure, leadership alignment, talent, and governance) to data engineering capability as a mediating layer that enables sustainable GenAI deployment. The findings position data engineering as a strategic prerequisite for trustworthy, cost effective, and scalable GenAI systems, and provide a practical lens for enterprises to assess and strengthen readiness before broad rollout.