AI document processing uses Machine Language (ML), Natural Language Processing (NLP), and Optical Character Recognition (OCR) to automate data extraction, categorization, and validation from documents. AI tools can identify and comprehend the context and meaning of content in various formats, such as PDFs, emails, and scanned images, minimizing manual intervention, reducing errors, and improving processing time. Robotic Process Automation (RPA) also plays a critical support role in document processing by streamlining business processes, integrating AI-extracted text and data into existing systems, chaining tasks together, and routing exceptions. Google Document AI is a specific product offering in this space, designed to help organizations efficiently process and extract insights from documents at scale. The evolution of Intelligent Document Processing (IDP) has led to the development of new deep learning techniques like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which have improved the accuracy of document classification and data extraction, particularly for complex and variable document layouts. IDP can help businesses overcome common workflow challenges such as data inaccuracy, handling high volumes of data, compliance failure, difficulty in handling unstructured data, inability to work with existing systems, and limited visibility into document processing. AI-powered tools like Nanonets can automate manual tasks, leading to faster, more precise processing, reducing overhead, increasing productivity, improving transparency, and enhancing compliance.