The increasing complexity of administrative tasks in organizations necessitates innovative solutions to enhance efficiency and reduce operational costs. This study explores the application of convolutional neural networks (CNNs) in automating administrative workflows through advanced document analysis. By leveraging CNNs, we aim to streamline the processing of diverse document types, including forms, invoices, and reports, enabling faster data extraction and classification. Our approach combines pre-trained CNN models with fine-tuning techniques to improve accuracy in recognizing and interpreting document layouts and textual content. The outcomes show notable gains in accuracy and processing time over conventional rule-based systems. Furthermore, we discuss the implications of AI-driven automation on workforce productivity, data integrity, and decision-making processes within administrative environments. This research not only highlights the transformative potential of CNNs in document analysis but also provides a framework for organizations seeking to implement AI solutions in their administrative workflows.