AI-Powered Data Quality and Validation Systems for Industrial Operations: A Reference Architecture for Rule-Based and AI-Assisted Exception Management

Industrial organizations depend on data from production systems, distributed control systems, energy meters, laboratory information systems, maintenance platforms, enterprise applications, and manual reporting. When these sources contain missing values, duplicates, implausible measurements, timing errors, unit inconsistencies, or cross-system disagreements, defects can propagate into operational dashboards and management decisions. This technical paper proposes a reference architecture for an AI-assisted industrial data-quality and validation system that combines deterministic validation rules, exception management, cross-system reconciliation, human review, and controlled AI assistance. The proposed architecture introduces a validation layer between operational data sources and analytical consumption. It preserves source records, applies explicit quality rules, assigns exception severity, routes uncertain cases for review, and maintains an audit trail. AI is positioned as an assistive component for explanation, prioritization, retrieval, clustering, and investigation rather than as an unrestricted mechanism for changing source-of-truth data. The paper presents a validation rule framework, exception-management workflow, AI and function-calling architecture, implementation methodology, dashboard and analytics approach, evaluation framework, governance controls, and phased deployment roadmap. This work is presented as a conceptual technical framework and does not claim empirical production deployment or experimental performance results.