Enterprise AI Transformation Through Modern Data Platforms

Abstract

The shift towards enterprise AI transformation driven by modern data platforms has emerged as a has become a major research and practical challenge for organizations seeking to create value from AI not just relying on standalone algorithms, but on managed, scalable, and actionable data ecosystems. This review critically evaluates peer-reviewed journal literature published in the last decade (2015-2026) related to AI capability, big data analytics capability, data governance, machine learning operations, digital transformation, and organizational value creation. The literature surveyed shows that data platforms play a role in enterprise AI transformation, by providing integrated data access, scalable analytics capabilities, establishing data governance, managing the data model lifecycle, and connecting technical architecture and enterprise change. There is, however, some empirical evidence that is not equally consistent. While previous research clearly shows correlations between analytics capability and performance, the limited number of journal articles that focus on production AI systems, platform modularity, lineage, feature management, monitoring and cross-functional operating models as coupled transformation mechanisms suggests an opportunity for further exploration. Further longitudinal studies are needed at both architectural and organizational levels. Enterprise AI transformation using modern data platforms is more of a socio-technical capability development exercise than a mere technological migration.