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This article presents a novel approach to automating invoice data extraction using artificial intelligence, addressing the persistent challenges of manual processing in enterprise environments. Traditional rule-based methods for extracting data from PDF invoices have proven inadequate for handling diverse document formats and scaling to meet growing business demands. The article proposes an AI-driven pipeline that integrates optical character recognition, natural language processing, and machine learning to create a robust, scalable solution for automated invoice processing. The system employs an event-driven architecture with real-time processing capabilities, incorporating advanced validation mechanisms and seamless integration with existing enterprise resource planning systems. Through a comprehensive case study at a large retail organization, the article demonstrates significant improvements in processing speed, accuracy, and operational efficiency compared to traditional methods. The findings indicate that AI-driven approaches can substantially reduce manual intervention while improving data quality and compliance. This article contributes to the growing body of knowledge on intelligent document processing and provides practical insights for organizations seeking to modernize their financial operations through automation.
Santoshkumar Anchoori - (Wed,) studied this question.