Invoices, contracts, onboarding forms, policies, reimbursement claims, credit applications. Every company receives and generates an enormous volume of documents every day. For years, document management systems have promised to solve the problem: digitize, index, archive. But the real bottleneck is not storage. It is extraction: taking the content of a document, understanding it, validating it, and putting it in the right format to feed a downstream process. This is the work that Intelligent Document Processing seeks to automate, and that until a few years ago almost always required a human operator.
What an IDP system actually does
Gartner defines Intelligent Document Processing as a category of specialized data integration tools that enable automatic extraction of information from documents in multiple formats and with variable layouts. The output is then made available to the applications and workflows that need it. Documents can arrive in physical form, after scanning, or directly in digital format such as PDFs and emails.
The critical point in this definition is "multiple formats and variable layouts." Traditional optical character recognition solutions work well on structured documents with fixed fields and predictable positions. Most real-world business documents are not like that: contracts with variable clauses, invoices from different vendors with incompatible layouts, handwritten forms, emails with heterogeneous attachments. IDP was built to handle this variability, using machine learning and, increasingly, language models to understand content rather than merely reading its structure.
A market with over one hundred vendors
In September 2025, Gartner published its first Magic Quadrant dedicated to Intelligent Document Processing Solutions, evaluating eighteen vendors including ABBYY, Amazon Web Services, Appian, Automation Anywhere, Google, IBM, Microsoft, OpenText, Tungsten Automation, and UiPath. The note accompanying the report is significant: the IDP market is vast, with over one hundred vendors including those from adjacent markets, and this creates a crowded and complex landscape that makes it difficult to distinguish the real differences between solutions.
Gartner forecasts that the IDP market will reach $2.09 billion by 2026, with a compound annual growth rate of 13% from 2021. It is a growing market, but also intensely competitive. The proliferation of vendors reflects real demand, but makes it harder for companies to evaluate options and choose based on actual capabilities rather than marketing messages.
Where the value chain breaks down
The problem that companies bring to IDP vendors is not "better document archiving." It is a chain of connected problems: receiving new vendors or new customers requires processing onboarding documents that arrive in different formats; processing reimbursement claims means extracting data from heterogeneous documents and validating them against business rules; managing contracts means understanding clauses, identifying expiration dates, and detecting variations from standard templates.
In all these cases, the document is not the final product: it is the medium through which information enters the process. If extraction is manual or partially automated with traditional technologies, it becomes the bottleneck. Operational teams spend hours copying data from documents to systems, correcting recognition errors, and managing exceptions. IDP does not eliminate all exceptions, but it dramatically reduces the share of documents requiring human intervention and focuses operator attention on the cases the machine cannot handle with sufficient confidence.
The role of GenAI in next-generation IDP
Large language models are changing IDP system capabilities on a specific front: contextual understanding. A system based on OCR and templates detects the fields it expects to find; a system incorporating GenAI can understand that two documents with completely different layouts contain the same information, express a judgment on content consistency against a business rule, or identify anomalous clauses in a contract without anyone having explicitly defined what they look like.
This capability opens new use cases that were previously beyond the reach of automation. At the same time, it introduces new challenges: language models hallucinate -- they produce plausible but incorrect output -- and in a document context, this can mean erroneous data entering downstream systems without anyone noticing. For this reason, in the 2025 IDP Magic Quadrant, Gartner lists among the critical capabilities to evaluate not only extraction and orchestration, but also data review, ModelOps, and secure handling: the ability to manage the model lifecycle, correct errors, and ensure that sensitive data is handled in a compliant manner.
From IDP to agentic automation
The next step that many vendors are taking is integrating IDP into agentic architectures: no longer a system that extracts data and delivers it to a separate workflow, but an agent that reads a document, understands what needs to be done, executes the necessary actions in connected systems, and handles exceptions autonomously. This is exactly the type of integration that requires both advanced IDP capabilities and a clear governance structure defining where automation stops and where human oversight begins.
Companies that treat document management as a storage problem remain stuck at the first level: digitized documents, but still manual processes. The value is unlocked when IDP becomes the intake layer for broader automation, and when document content stops being the domain of the operators who read them and becomes the domain of the systems that process them.
