How to make your enterprise data accessible, usable, and insightful.
For most organizations, information is everywhere—but that doesn’t mean it’s easy to use. A lot of business data lives inside documents, including contracts, purchase orders, invoices, and bank statements. All of these documents contain valuable information, but accessing and acting on that information is a challenge.
That’s because most of this information isn’t stored in tidy databases or neatly labeled fields. It’s buried in text, scattered across file types and formats, and often hard to find. So organizations spend lots of time and effort trying to get data out of documents and into their systems through manual data entry or with multiple different tools. This is where AI document processing—using AI to extract and process data from documents—comes in.
The trouble with unstructured data
Most organizations are well-equipped to handle structured data—the kind that fits neatly into rows, columns, and databases. This includes things like customer records, financial transactions, or inventory lists. Structured data is predictable, machine-readable, and easy to analyze using traditional tools.
But structured data only tells part of the story. In fact, as much as 80% of information flowing through modern organizations is unstructured data—content that doesn’t conform to a predefined format. This includes everything from email messages to scanned documents, contracts, handwritten forms, audio, video, and image-based PDFs. It’s where some of the most valuable data lives—data that could improve decision-making, speed up processes, and enhance customer experiences.
Unlike structured data, unstructured data can’t be easily searched, sorted, or analyzed without additional processing. And somewhere in between lies semi-structured data—think of things like XML files or online forms that follow a loose structure but still require interpretation.
Much of this complexity comes from the nature of unstructured documents, which vary widely in format, language, and layout. For example:
One invoice might list a due date at the top; another might bury it at the bottom.
A scanned application form might contain handwritten notes in the margins.
A customer complaint might arrive as an email, with key details embedded in a long paragraph.
Intelligent document processing (IDP) unlocks the value of your documents
To solve the challenges of working with unstructured and semi-structured data, organizations are turning to intelligent document processing (IDP)—an advanced form of AI document processing.
IDP doesn't just digitize a document; it classifies it, extracts key data, and validates it. It works by transforming unstructured information into structured, usable data that can then be used to fuel workflows, drive analytics, and power better decisions.
IDP combines optical character recognition (OCR) with artificial intelligence techniques like machine learning and natural language processing. It goes beyond just reading—it understands meaning and context.
Under the hood, IDP systems use a combination of deep learning, computer vision, and large language models to analyze everything from structured documents like forms to semi-structured documents like purchase orders or emails.
Examples of AI document processing at work
Let’s say you upload a pile of insurance claims. One is a PDF, another is scanned, and a third is an email thread. AI can locate the policy number even if it appears in different places or formats and extract the relevant data with high data accuracy.
It can also flag missing fields, validate entries, and pass extracted data into a process to route the information into other workflows or downstream systems. If the AI has low confidence in a particular field—say, a total amount on a purchase order or a signature date on a claim form—it can trigger a reconciliation task, assigning a human reviewer to confirm or correct the entry. This human-in-the-loop model ensures high data accuracy while maintaining speed and efficiency, especially in complex or high-stakes processes like claims processing or financial reporting.
Or consider a government agency reviewing grant applications. With advanced AI document processing like IDP, teams don’t need to spend hours combing through forms and attachments to extract key data points. Instead, the system does the heavy lifting, so analysts can focus on evaluation and decision-making. The same applies in banking, healthcare, legal, and every other sector.
When integrated into broader intelligent automation initiatives and embedded into workflows, IDP acts as a bridge between data and action—enabling everything from faster claims processing to streamlined customer support. By automating document processing with AI, organizations:
Accelerate workflows
Free up teams from tedious manual work
Reduce the risk of errors and inconsistencies
Uncover patterns across thousands of files—patterns that were invisible when the data was locked inside PDFs or paper forms
Challenges with intelligent document processing (IDP) tools
Even with IDP tools in place, challenges persist. Many solutions on the market today fall short in critical ways. Their limitations include:
Clunky or inflexible workflows
Minimal UI control
Lack of integration with core systems of record
Inability to handle long, messy, or highly unstructured formats (think: legal agreements or regulatory filings)
These gaps lead to fragmented processes, manual workarounds, and missed opportunities to fully leverage enterprise data.
Organizations often face steep accuracy challenges when dealing with lengthy PDFs—and with some tools, “lengthy” can mean anything beyond eight pages. The longer and more complex the document, the greater the chance of missed or incomplete data extraction. Now picture a contract, compliance report, or legal filing running into the hundreds of pages. Every section must be reviewed, every field verified, every exception handled. Without the right tools, this becomes a painstaking manual effort that drains resources, slows processes, and delays decision-making.
The future of AI document processing is integrated
AI document processing is evolving from a standalone extraction tool into a core component of end-to-end business automation.
This new approach is built on an enterprise-grade automation platform where AI is a core, native component. It moves beyond just providing an AI model; it simplifies the entire process.
The platform handles complex backend tasks like prompt generation, allowing developers and business experts to use no-code tools to easily configure, validate, and refine how models perform. And because the intelligence is native to the automation environment, users can easily build rules, trigger next steps, and orchestrate entire end-to-end workflows that are faster and more resilient. The result is not just smarter data extraction, but more efficient business processes from start to finish.
Whether you're modernizing your claims processing system or looking to boost data accuracy across your organization, AI document processing tools can help you move faster, work smarter, and unlock more value from your documents.
Learn how to scale your organization with AI Document Center—a no-code experience for enterprise-grade intelligent document processing on the Appian Platform.
