Enterprise AI is rapidly evolving from isolated text-generation tools to autonomous agents capable of executing complex operational decisions across business-critical workflows. These agents are beginning to support functions such as real-time inventory placement, system orchestration, and corporate rewards management. Yet most enterprise data architectures were designed for analytics, reporting, and retrospective insight—not for autonomous decision-making where actions are executed directly against systems of record.

This session examines why Master Data Management must evolve from a legacy, batch-oriented data foundation into an action-grade operational layer for agentic enterprise AI. In autonomous environments, stale records, near-match identity errors, duplicate entities, and data drift can create serious downstream consequences when AI-driven decisions trigger live enterprise workflows. Reliable agentic systems therefore require a new MDM architecture built around immutability, versioned data contracts, real-time identity resolution, and event-driven execution.

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

The session will also address structural methods for reducing data drift and maintaining trusted entity resolution as autonomous agents interact with critical enterprise systems. Designed for enterprise architects, data leaders, AI platform teams, and technology decision-makers, this presentation offers a practical roadmap for re-architecting MDM for action-oriented AI environments where data quality directly determines operational outcomes.