What is Document AI?

Document AI handles structured data such as spreadsheets, unstructured data such as emails and contracts and semistructured documents such as forms, invoices and financial reports. Such documents contain valuable information, but their formats often require advanced machine learning techniques to extract insights efficiently.

When humans extract information manually from large volumes of documents, it's a time-consuming process that invariably causes inaccuracies. In contrast, document AI systems "read" documents in a way similar to humans and have a contextual understanding of the material. So, they can interpret meaning and relationships in the same way a human would—albeit at a faster pace and larger scale and with results devoid of human error.

Document AI simulates human reading by using a combination of technologies to ingest, process and interpret many types of documents with a high level of comprehension.

At the core of doc AI, optical character recognition (OCR) converts scanned or handwritten text into machine-readable text. This process allows document AI to "read" various formats, including PDFs, custom documents, images and forms, regardless of whether the text is typed or written. Once digitized, the text becomes searchable and editable, making the document more accessible for further analysis or use in various business processes.

OCR only handles character recognition—it doesn't interpret the meaning behind the text. Here is where natural language processing (NLP) plays a key role. NLP enables document AI to interpret the meaning and context within the text, much like a human reader. By applying linguistic models, document AI can identify relationships between different parts of a document to recognize names, dates and addresses, even without explicit labels.

Machine learning models, particularly deep learning, enhance document AI's accuracy. These models are trained on vast datasets, using data science techniques that allow them to recognize complex patterns within documents. Similar to the way the human brain processes information, neural networks in document AI analyze document layouts, fonts and languages, continuously adapting to various formats. This flexibility allows document AI to handle multiple real-world scenarios, from simple invoices to complex legal contracts and improve its capabilities through ongoing learning.

Metadata also plays a major role by providing additional, often hidden, information about a document. Metadata includes details such as the document's creation date, author, file format and keywords that further describe its content. By using metadata, document AI works to better organize, manage and retrieve documents, improving workflow efficiency.

Application programming interfaces (APIs) are essential in connecting document AI models to other systems. Document AI APIs facilitate the seamless integration of document AI with enterprise platforms, automating document-related workflows and aiding real-time data extraction and analysis. These APIs help document AI to scale, making it adaptable to a wide range of business tasks while integrating with broader IT infrastructures.

Document AI platforms also use processors as intermediaries between document files and machine learning models. These processors are responsible for specific actions such as classifying, splitting, parsing and analyzing documents, helping ensure that the system properly processes and understands each document.

The parser analyzes and interprets data structure. It breaks down documents into their fundamental components, understands the relationships between these elements and converts unstructured or semistructured data into formats that the AI system can process.

In addition to comprehending text, document AI can analyze the structure and layout of documents. It recognizes elements such as headings, paragraphs, tables and lists, helping the AI understand the hierarchy and context of the document. This structured analysis is useful for identifying key-value pairs, such as within invoices where document AI extracts amounts due and payment dates to reduce the need for manual input.

Most standard document AI models come pretrained on numerous document types, but enterprises often use specialized documents with unique formats, terminology or layouts specific to their domain. Fine-tuning doc AI models allows them to be tailored for specific needs. For instance, a legal firm might fine-tune a model to better understand legal jargon, contract clauses and formatting peculiarities, making the AI more accurate.

Advanced document AI systems go beyond simple data extraction to provide summaries of lengthy documents. By highlighting key points within the document, these systems allow users to quickly grasp essential information without reading through the entire document.

Document AI is often integrated with cloud storage and enterprise systems to streamline document management and analysis across the organization, giving appropriate users access to the documents and information they need, when they need it.

Traditional document AI solutions rely heavily on OCR, rule-based systems and machine learning models for extraction, classification and data processing. Many doc AI platforms do not inherently use generative AI (gen AI) or large language models (LLMs), especially when tasks are focused on straightforward data extraction and classification from documents.

However, generative AI has proven effective at enhancing document AI. When integrated with generative AI, a document AI system can be directed to draft new documents based on extracted data templates. For example, in insurance claims processing, after data is extracted from claim forms, a generative AI model embedded within the document AI platform can help an agent draft a follow-up, a report on the claim or recommendations based on the input data.

While traditional document AI systems can seamlessly extract data in most cases, it can fall short when faced with interpreting ambiguous language, conducting multistep reasoning or recognizing characters in low-quality, noisy images. Generative models help fill these gaps by correcting errors, providing deeper contextual interpretation and enhancing the system's ability to handle legal, medical or technical documents that demand nuanced understanding.

IBM Automation Document Processing is a low-code solution that uses AI and deep learning to classify and extract information from structured and unstructured documents. Its low-code interface allows users to automate document-related workflows with minimal programming effort, enhancing productivity and efficiency.

Google Cloud Document AI is an enterprise platform that offers a comprehensive suite of tools to automate document processing. It uses generative AI to extract data and classify documents without requiring any prior model training, making it accessible for quick implementation and deployment. Users can manage and monitor their document AI models through the Google Cloud Console, which provides an easy-to-use interface.

Google Cloud's BigQuery is a fully managed, serverless and scalable data warehouse. It supports quick analysis of large datasets using structured query language (SQL). BigQuery is well-suited for handling big data, where traditional databases struggle to process large-scale datasets efficiently.

Vertex AI is a unified platform designed to streamline the entire machine learning lifecycle, from data preparation to model deployment and monitoring. By offering tools for AutoML and custom model development, Vertex AI accommodates users with varying levels of expertise, from beginners to experienced data scientists, making it a versatile solution for building and deploying machine learning models.

Document AI offers a wide range of benefits across multiple industry use cases by automating data entry and enhancing business processes. Doc AI's ability to extract data from various documents is useful in mailrooms, shipping yards, mortgage processing and procurement, where large volumes of paperwork require efficient handling.

In the insurance sector, document AI helps process claims and policy applications by extracting important data, reducing processing times and improving operational efficiency.

In publishing, document AI can digitize physical publications, converting them into formats compatible with e-readers, which makes content more accessible, searchable and easier to manage.

In healthcare, document AI streamlines the processing of medical intake forms in doctor's offices, reducing administrative workload and helping ensure accurate patient data capture. In clinical trials, document AI improves oversight by accurately extracting data from trial documents, assuring regulatory compliance and speeding up the reporting process.

In finance and accounting, document AI efficiently parses receipts and invoices, allowing efficient expense report validation, saving time and improving accuracy. Additionally, it can analyze ID cards and other official documents to assist in identity authentication, confirming secure verification. Document AI can also extract income details from tax forms, simplifying loan approval processes and financial assessments. In accounting, document AI automates invoice processing, improving accuracy and speeding up workflows for more efficient financial management.

The technology can also analyze financial documents to detect counterfeit currency and fraudulent checks, enhancing security measures within financial institutions. Document AI improves operational efficiency by extracting essential data from customer emails and SMS, speeding up response times. It enhances fraud detection by automating document analysis, allowing organizations to identify suspicious activities quickly.

Regarding legal and business documents, document AI helps companies analyze contracts, identify key terms and clauses, speed up the review process and confirm agreement compliance. It can also detect irregularities in invoices, flagging potential errors or fraud. Document AI also automates the review of legal documents, reducing the time and effort needed to assess contracts and agreements while improving accuracy and scalability.

In the compliance and regulatory sectors, document AI assists in automating the assessment of regulatory changes and their impact on contracts, simplifying compliance management.

In the mortgage industry, document AI accelerates workflows by quickly extracting and processing essential information from loan applications. It also automates the monitoring of loan portfolios, helping with more efficient credit risk management and the timely identification of potential issues. In real estate, it standardizes document classification and automates the extraction of critical information from contracts, leases and other related documents.

Another key benefit is its ability to extract valuable data from document silos, unlocking previously inaccessible information that supports better-informed business decisions. For organizations operating globally, document AI simplifies the processing of receipts across different countries, reducing the complexities associated with international transactions. The technology also transforms static PDF documents into actionable workflows by automating tasks such as setting due dates, managing approvals and assigning responsibilities.