From Data Warehouses to AI-Native Data Platforms: Architectural Evolution in Enterprise Data Management

Abstract

Enterprise data management architectures face substantial transformation as organizational demands extend beyond retrospective reporting toward intelligent and autonomous data operations. Conventional data warehousing frameworks exhibit fundamental constraints when confronted with requirements for real-time interpretation and contextual reasoning, largely because they are built around code-centric batch processing designs optimized for structured business intelligence rather than semantic understanding or adaptive response generation. This work introduces an AI-native enterprise data platform architecture in which intelligence is embedded directly within data platform layers rather than applied as an external analytical add-on. The architectural transition from traditional warehouse designs to these AI-native platforms represents a significant reconceptualization of how enterprise information assets are stored, governed, and consumed, with artificial intelligence functioning as an integral platform component rather than residing in an external analytical layer. Within this framework, metadata assumes operational primacy as static documentation transforms into computational resources that enable automated reasoning about data relationships and usage patterns. Intelligence co-location with data assets reduces movement overhead while ensuring that governance boundaries remain intact during analytical operations through integrated enforcement mechanisms. User interaction paradigms shift fundamentally from procedural query specification toward intent expression and outcome description, with natural language interfaces democratizing data access across organizational functions so that personnel previously constrained by technical proficiency requirements can gain direct analytical capabilities. Multiple enterprise organizations across financial services, healthcare, and manufacturing sectors served as implementation environments for framework validation over an 18-month period, yielding substantial improvements across multiple dimensions. Query resolution time improved significantly following framework deployment, while data discovery effort diminished considerably compared to baseline measurements. Cross-functional data access expanded well beyond technical specialist populations, and governance compliance rates increased markedly against regulatory standards. Design challenges accompanying embedded intelligence include quality propagation risks and bias amplification concerns, though governance framework extensions address AI-specific accountability requirements effectively. The findings demonstrate that architectural characteristics determine sustainable AI integration success more significantly than algorithmic complexity alone.