The work aligns with market pressures for responsible AI, regulatory readiness, and real-time trusted data, offering a practical path to AI-enabled governance that is explainable, auditable, and scalable.

With over two decades as a Lead MDM Engineer, the author brings deep TIBCO EBX expertise and is driving AI-enabled MDM initiatives that automate data quality checks, detect anomalies, and enable predictive stewardship.

The architecture centers on provenance-aware metadata ingestion, LLM-based contextual reasoning with human-readable lineage narratives, policy-driven validation engines, and continual learning loops for ongoing improvement.

Customer-level benefits include faster onboarding through automated entity resolution, enhanced personalization via higher data quality, transparent dispute resolution with evidence-backed explanations, and more reliable service from consistent data across product, supplier, and location records.

Nagender Yamsani’s elevation to the Editorial Board of IJSRCSEIT and IJSRST and his induction as an Eminent Fellow of the World Research Council mark a milestone for AI-enabled data governance.

A centerpiece is the 2024 paper, Large Language Models for Intelligent Data Stewardship in Enterprise Architectures, Provenance, and Evidence-Mapped Governance, which outlines a production-ready blueprint for embedding LLMs with explainability, auditable processes, and scalability.

The framework tackles compliance, transparency, and control by tying LLM reasoning to verifiable provenance and policy gates, reducing fragmentation and opaque AI outputs.

Overall, the work emphasizes practical impact and urgency for enterprises undergoing data modernization and AI adoption.

Enterprise outcomes include shorter audit prep times, lower operating costs through automation, faster insights from golden records, stronger regulatory posture via policy-aware automation, and scalable governance without proportional staffing increases.

The work is innovative because it unifies AI reasoning, governance automation, and lineage transparency into a deployable model that meets regulatory demands for explainability, supports multi-domain MDM at scale, offers a reproducible evaluation framework, and turns stewardship into a proactive capability.