October 30, 2026
Intelligent document processing (IDP) uses AI to read, extract, validate, and organize information from documents into structured data that businesses can use in digital workflows.
IDP can process a wide range of structured, semi-structured, and unstructured content across formats such as printed pages, Word documents, spreadsheets, emails, PDFs, and images. It can capture and aggregate information that might otherwise remain scattered across documents, helping businesses make faster, better-informed decisions. By automating this work, IDP reduces the need for manual document handling and provides more accurate, timely data for business processes, analytics, generative AI, and other AI-driven workflows.
Learn more about ABBYY's low-code IDP solutions
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What are the benefits of intelligent document processing?
How does intelligent document processing work?
What technologies are used in IDP?
Intelligent document processing use cases
How has intelligent document processing evolved?
How to choose the right intelligent document processing software
Business benefits of IDP include:
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IDP workflows vary by system and use case, but a typical process may include:
IDP combines a number of AI and document processing technologies:
Businesses often use IDP alongside other automation and AI technologies. Three important examples are:
Because IDP can automate document-heavy workflows across languages and formats, this technology is used in many different industries that handle large volumes of documents. These include:
IDP can automate document-heavy insurance processes such as claims handling and customer correspondence to speed up workflows while reducing the amount of manual work required from employees.
Multinational insurance broker Ecclesia Group, for example, used ABBYY's IDP solution to digitize, streamline, and improve customer correspondence management. The system automatically extracts information such as case numbers and license plates from claims documents, matches it to the correct customer, and routes the documents to the appropriate claims manager for further processing.
Financial services organizations often use IDP for processes including customer onboarding, Know Your Customer (KYC) compliance, trade finance, and loan processing. These workflows can involve hundreds of pages of supporting documents, including IDs, proof of address, bank statements, utility bills, and pay slips. IDP can process information across those documents to help banks speed up underwriting and approvals while giving customers a smoother digital application experience.
Financial services firm FinTrU provides some of the world's top investment banks with technology-enabled lifecycle management solutions. To support global banks with their regulatory processes, the company built TrU Label: a platform for automating complex document workflows, powered by ABBYY AI, and saw a 40% improvement in document processing efficiency, with a 99% first-time pass rate in compliance review.
A growing number of healthcare organizations are turning to IDP to reduce manual processing and access information buried in records and other documents.
Chris O'Brien Lifehouse, one of Australia's leading cancer treatment and research facilities, implemented ABBYY IDP and eliminated 80% of its paper records. This provided faster access to patients' complete medical history, accelerating patient care and improving access to critical patient information.
Mistakes in paperwork can delay shipments and disrupt operations in transportation and logistics. IDP can improve accuracy by automatically processing large volumes of shipping documents such as customs declaration forms, delivery receipts, bills of lading, and driver logs.
CustomsTrack, a provider of automation solutions for customs clearance at international borders, drastically cut processing time at the EU/UK border to under five minutes with ABBYY IDP. Their solution eliminated the need for customs brokerage services, delivering 100% accuracy in data extracted from customs documents and 80% reduction in processing effort.
Early document automation focused mainly on digitizing text. Basic OCR systems could convert printed content into machine-readable text, but couldn't understand how information was organized on a page or what that information meant in context. People still had to identify document types, locate the relevant data, verify it, and route it to the next step.
| Capability | Traditional OCR & Capture | Modern IDP |
| Text Recognition | Converts text to digital format | Digitizes text with an understanding of document type, layout, and context |
| Processing Approach | Relies on templates and rules | Uses a combination of AI models to handle a wide range of document formats |
| Data Extraction | Extracts predefined fields | Extracts and validates relevant information based on context |
| Workflow Handling | Follows predefined workflows | Makes decisions about how documents and data should be processed |
| Output | Feeds data into predefined business workflows | Produces structured data for analytics, LLMs, and AI agents |
As technology advanced, OCR was combined with early AI to create the first IDP solutions. Instead of simply converting text into machine-readable form, these systems could start to interpret layout and context to extract relevant information. Advances in machine learning, computer vision, and NLP continued to make IDP more adaptable and accurate.
Today, IDP is taking on a broader role as businesses adopt generative and agentic AI.
Choosing the right IDP solution requires considering not only how accurately and efficiently it processes documents, but also how well it fits into your existing operations and how easily it can meet and adapt to changing business and regulatory needs. Criteria to weigh include:
As part of the ABBYY Document AI portfolio, ABBYY's IDP solution provides end-to-end processing for complex, real-world business documents in more than 200 languages.
Built for enterprise and regulated environments, ABBYY IDP can extract accurate, consistent, structured data that follows established security and compliance requirements. Human review, validation, and governance controls can also be incorporated where greater oversight is required.
To help teams get started quickly, ABBYY Document AI supports more than 150 prepackaged AI models and solutions through the ABBYY Marketplace. Its low-code platform lets business teams customize or train their own models without extensive development work, while prebuilt connectors and REST APIs make it easier to send structured data directly into business applications, AI models, and agentic workflows.
ABBYY has more than 35 years of experience in document processing. Request a demo to see firsthand how ABBYY Document AI extracts reliable, structured data at scale.
Organizations that handle large volumes of documents can benefit from IDP. It reduces repetitive manual work, improves accuracy, and frees employees to focus on higher-value tasks. IDP can also capture and aggregate information that might otherwise remain scattered across documents, helping businesses make faster, better-informed decisions.
OCR digitizes text from documents to make them accessible electronically. IDP often includes OCR as one step in a broader process that also understands that content in context, extracts relevant data, and routes that data for downstream use.
Automatic document processing (ADP) generally relies on predefined rules to handle routine document tasks and works best with consistent, structured formats. IDP can typically process a wider range of document types, including complex unstructured and semi-structured formats, because it can understand context and adapt to different layouts and content.
Both IDP and RPA aim to automate business processes, but while IDP interprets and extracts data from a wide variety of documents, RPA automates routine, repetitive tasks such as entering data and updating records across various systems.
Businesses can automate more end-to-end processes by combining IDP’s ability to understand and extract data from documents with RPA’s ability to use that data to carry out repetitive tasks.
Intelligent document processing and large language models solve different problems. IDP is designed to understand documents, extract the right data from them, validate that data, and convert it into structured information that business systems can use. LLMs are designed to understand and generate language, which makes them useful for tasks such as summarization, reasoning, and natural-language interaction.
In practice, IDP is the better fit for document-specific tasks such as classification, extraction, and validation, especially when accuracy, consistency, and auditability matter. LLMs add value when organizations want to interpret, summarize, or interact with the data that has already been extracted.
Yes. In fact, they often work best together. IDP can first read documents, classify them, extract the relevant information, and validate it. That structured, trustworthy data can then be passed to an LLM for higher-level tasks such as summarization, reasoning, question answering, or generating communications.
This hybrid approach helps organizations get the strengths of both technologies. IDP provides a reliable data foundation, while LLMs add flexibility and language intelligence. Used together, they can improve accuracy, reduce hallucination risk, and make document-driven workflows more useful and scalable.
IDP focuses on turning document content into accurate, structured data. Agentic automation focuses on using AI agents to reason, make decisions, and take action across workflows. In other words, IDP is about understanding and preparing document data, while agentic automation is about using data to drive dynamic actions.
The two are complementary. AI agents are only as effective as the information they receive. If the underlying data is incomplete, inconsistent, or trapped in documents, agents can make poor decisions or trigger the wrong actions. IDP helps solve that problem by extracting and validating the document data agents need to work effectively.
Intelligent document processing helps convert unstructured content like PDFs, scans, and images into structured data that RAG systems can retrieve and use more effectively. This helps RAG systems provide more relevant information, improve user confidence through source-backed outputs, and offer a more cost-effective alternative to retraining foundation models.
Most RAG failures trace back to the document parsing layer, not the model. Bad parsing produces bad chunks; bad chunks produce weak retrieval; and weak retrieval produces hallucinations that no amount of model tuning can fix.
FineParser ensures the success of RAG pipelines. It produces DocLang, JSON, ALTO, and XML formats that carry semantic labels for every content element: headings, paragraphs, table cells with their row and column positions, footnotes, captions, and form fields. See how structured, AI-ready data improves RAG workflows.
ABBYY is continuously investing and innovating to safeguard what matters most to you, delivering new standards of safety, reliability, and privacy. Our record of maintaining high ethical standards in AI is one of the key factors that sets us apart.
Our technologies meet the stringent demands of multiple regulations and global regulatory frameworks, including the European Union’s Artificial Intelligence Act (AIA). ABBYY is committed to operating as a security-first cloud provider whose governance, operational rigor, and transparency also align with the core principles of BSI C5. By embedding governance, risk discipline, and auditable transparency into service delivery, ABBYY enables customers to accelerate cloud adoption without expanding their risk exposure.
Maxime Vermeir
VP of AI Strategy at ABBYY
Maxime Vermeir is Vice President of AI Strategy at intelligent automation company ABBYY. He focuses on helping enterprises move artificial intelligence from experimentation to execution, translating advances in large language models (LLMs) and other AI technologies into systems that deliver measurable business outcomes.
Working at the intersection of strategy, process, and technology, Maxime advises organizations on how to operationalize AI at scale across real-world workflows. He also hosts ABBYY’s AI Pulse podcast, where he explores emerging AI trends and their practical impact on enterprises.
Follow Max on LinkedIn.
