Abstract
<jats:p>Banking institutions face mounting pressure to digitise core operational workflows, document processing, compliance validation, financial reconciliation, and board-level reporting, which have historically relied on manual effort and specialist analyst teams. This paper argues that AI-powered digital transformation of these workflows is not merely an efficiency gain but a structural reimagining of how banks create, validate, and act on operational intelligence. Drawing on the design, implementation, and empirical evaluation of a proof-of-concept (POC) AI Document Intelligence System developed for a regional bank, this study demonstrates that AI augmentation of core document operations reduces processing time by up to 98%, eliminates analyst dependency for routine intelligence tasks, and achieves a hallucination-guarded accuracy rate of 87–94% across six representative operational question types. The system integrates natural language querying, automated financial contradiction detection, grammar and compliance validation, structured summarization, and multi-format export within a single interface. The paper introduces the Intelligent Operations Framework as a design guide for banks seeking to operationalize AI across document-intensive workflows without sacrificing audit integrity or regulatory compliance. Findings are contextualized within the broader literature on BI democratization, AI trust, and digital transformation in financial services.</jats:p>