Enterprise software is rapidly evolving from isolated text-generation tools to autonomous agents capable of executing complex operational workflows, from real-time inventory placement to corporate rewards orchestration. As AI systems move from recommendation to execution, the quality, consistency, and timeliness of enterprise data become mission-critical. Traditional analytics-grade data architectures, however, were not designed to support autonomous decision-making at scale. In agentic environments, near-matches, stale records, duplicate entities, or data drift can trigger significant downstream errors when decisions are executed directly across enterprise systems.

This session explores the architectural shift required to evolve Master Data Management from legacy, batch-processed data tables into an action-grade, immutable contract layer built for agentic enterprise AI. It explains why MDM must move beyond its traditional role as a reporting and analytics foundation and become an operational execution layer that enables reliable, real-time decisioning.

Drawing on experience building enterprise master data hubs, including a global system orchestration framework spanning 38,000 retail locations, the presentation will outline the system-strategy decisions required to create a stable data substrate for autonomous workflows. Attendees will learn practical approaches for implementing sub-second match-and-survive functions, real-time routing, idempotent workflows, versioned data contracts, event-driven fan-out patterns, and structural methods for reducing data drift using technologies such as AWS Lambda, DynamoDB, and EventBridge.

Designed for enterprise architects, data leaders, AI platform teams, and technology decision-makers, this session provides a practical framework for re-architecting MDM for action-oriented AI environments, where identity resolution, data quality, and real-time routing directly influence operational outcomes.