Design and Implementation of a Responsible, Explainable, and Compliance-Driven AI Architecture for Enterprise-Scale Content Management Systems Integrating Generative Models, Retrieval Pipelines, and Real-Time Governance Controls

Abstract

This paper introduces a data analytics-driven methodology for supplier onboarding and ERP-based compliance management that expedites qualification, enhances assurance, and reduces lifecycle risk. A standardized digital intake records identity, regulatory, ESG, cybersecurity, and tax credentials; deterministic rules authenticate necessary evidence, while a comprehensible gradient-boosted model assesses residual risk utilizing factors such as sector, jurisdiction, beneficial ownership depth, sanctions proximity, and historical incident rates. All processes are recorded in the ERP vendor master and procurement modules using regulated APIs. The approach establishes a golden-record strategy, reference taxonomies, and data-quality regulations to avert duplicate or incomplete vendor profiles. Continuous monitoring employs event streams and dashboards to identify status alterations, expired certificates, negative media, delayed attestations, and control deviations. Exceptions initiate structured remedial operations, whereas feedback loops recalibrate the model and adjust thresholds for idea drift. We assess the approach using a quasi-experimental design that compares matched business units prior to and following implementation. Results demonstrate a 32–45% reduction in onboarding lead time, a 28% decline in first-year compliance exceptions, and a 19% enhancement in audit-readiness ratings, all while upholding competition and diversity standards. Ablation analysis indicate that the most significant effects stem from master-data quality controls and the automatic ERP gates of the policy engine. A reference architecture, governance RACI, and value tracking framework are provided to facilitate expansion across multi-ERP environments. The contribution is threefold: firstly, a cohesive, analytics-driven methodology that integrates onboarding and compliance into a singular, data-oriented process; secondly, a transparent risk assessment framework linked with verifiable controls; and thirdly, actionable change-management strategies that facilitate value realization. Future endeavors will enhance causal inference, incorporate document intelligence, and investigate privacy-preserving data sharing.