AI-Powered Intelligent Document Processing in Digital Enterprises

Abstract

Intelligent Document Processing (IDP) using Artificial Intelligence (AI) enables organizations to efficiently process and analyze large volumes of unstructured and semi-structured data. Traditional rule-based and manual methods struggle with scalability and complexity, whereas AI-driven IDP leverages machine learning, natural language processing, computer vision, and deep learning to enhance accuracy and decision-making. This paper presents a comprehensive study of AI-based IDP systems before 2019, focusing on their architecture, methodologies, and applications in digital enterprises. It explains how technologies like OCR convert scanned documents into machine-readable formats and how supervised and unsupervised learning improve classification and data extraction. A modular architecture including ingestion, preprocessing, classification, extraction, validation, and storage is discussed. The study highlights improved performance in terms of accuracy, precision, recall, and processing time compared to traditional systems. Applications across banking, healthcare, insurance, and logistics are examined. Finally, key challenges such as data privacy, model interpretability, and system integration are identified, along with future research directions, emphasizing the role of AI-driven automation in digital transformation.