Modern businesses generate more documents than ever. Contracts, proposals, onboarding forms, NDAs, invoices, and more are part of the standard documentation required for many business deals, and companies add more information to their database with every new customer or employee.

However, most of that information remains trapped in manual processes. Even if the company has digitized its documents, an employee usually has to access the document directly to access all the information. If that data needs to be added to another system or database, workers need to key it by hand.

Intelligent document processing (IDP) changes that process. Using a combination of tools—AI, OCR, machine learning, etc.—IDP can read documents, understand their contents, and automatically turn that unstructured information into searchable, usable business data.

In this guide, we’ll break down how IDP works, why it matters, and how companies are using the technology and the information it provides to optimize their business workflows.

what is intelligent document processing (IDP)

Key takeaways

  • Intelligent document processing (IDP) uses AI to automatically read, classify, and extract information from business documents.
  • Teams can use IDP to automatically gather critical information from their documents, including renewal dates, payment terms, or customer details.
  • IDP is accessible to businesses of all sizes and can be used to improve visibility across workflows without adding additional administrators to the system.

What intelligent document processing actually does for businesses

Most businesses already store documents digitally, but the information contained in those documents is locked inside the static file.

To access critical information such as customer details or payment terms, workers still need to retrieve those files manually. Outside the document, the data is inaccessible and invisible, making it difficult for companies to know what information they have at their disposal.

IDP changes how companies can handle and access information stored in their files and databases. By combining AI with technologies like optical character recognition (OCR) and natural language processing (NLP), businesses can automatically parse their database to collect and organize important information into structured records and connect it to systems that teams use every day.

How IDP actually works

4 steps of intelligent document processing

At a high level, IDP follows a four-step workflow that allows AI to read, sort, and organize a company’s business data.

Every step in the IDP workflow happens automatically after upload or document completion, eliminating the need for employees to manually review files or re-key information. Instead, the system handles the work behind the scenes and compiles the output into a searchable database.

  1. Ingest. Documents are collected from multiple sources, including uploads, scans, emails, cloud storage, or connected applications. The platform prepares those files for processing regardless of format or layout.
  2. Read. OCR converts the document into machine-readable text so that the system can analyze its contents. This step enables AI to process PDFs and other non-editable formats that would normally slow or stall document workflows.
  3. Understand. AI models analyze a document to determine what it is and the value of the information it contains. For example, after determining that a document is a hiring contract, AI may parse the document for employee details, start dates, and pay rates.
  4. Route. Once the information has been extracted and validated, data can automatically flow to CRMs, approval systems, contract repositories, and other tools for additional access or analysis.

For brands handling large volumes of documents, IDP flows dramatically reduce the need for human data entry and the risk of manual errors. Instead, IDP enables automated document processing and accelerates every aspect of the process while also parsing information and improving data visibility across document operations.

IDP vs. OCR vs. RPA: What’s the difference?

Unlike other AI solutions, IDP isn’t a single tool or software. Instead, IDP consists of multiple tools working together to read, understand, and parse a document.

Because so many processes are involved, terms like OCR, RPA, or AI might sound like interchangeable automation tools. However, each piece of technology handles a different part of the IDP workflow.

  • OCR (optical character recognition). Some text from PDFs or scanned docs is difficult or impossible for AI tools to parse. OCR solutions are designed to convert scanned documents, images, PDFs, and other formats into machine-readable text. This technology can identify letters, numbers, and formatting, and is useful for digitizing fixed-format documents. However, it doesn’t understand what the information it reads actually means.
  • RPA (robotic process automation) automates repetitive, rule-based tasks like moving files, updating records, or triggering workflows. Often, this technology handles the menial tasks that are required for the entire system to function. While RPA can follow basic instructions, it can’t interpret unstructured data on its own.
  • AI (artificial intelligence) is the “brain” of the IDP operation. This technology sits on top of others and tries to organize the information it receives. It recognizes patterns, understands context, and decides what information is relevant based on those details.
  • IDP (intelligent document processing) combines all of the above technology and automation tools into a single solution that can read, understand, and organize business documents and data. The system can also route the data it extras to other systems for use in other company processes and initiatives.

These technology tools are designed to complement one another and work together to achieve a common goal. OCR handles text recognition, AI provides understanding, RPA executes tasks, and IDP connects everything together into a usable workflow.

However, no system is perfect. While IDP solutions are often ready to deploy out of the box, it may take some time for the AI to properly understand your documents and workflows. The system will improve over time but can make mistakes.

To counter this, manual review processes — typically added as part of the data approval process — are put in place to safeguard data integrity and prevent the AI from introducing incorrect information into the existing dataset.

OCR RPA AI IDP
What it does Reads text from images and PDFs Automates repetitive rule-based tasks Identifies patterns, context, and relationships in data Reads, classifies, and extracts data from any document
Handles unstructured documents Limited No Yes Yes
Understands context No No Yes Yes
Learns and improves No No Yes Yes
Best for Digitising fixed-format documents Automating structured data entry Understanding and interpreting information Turning any document into structured, actionable data

What IDP means for document-heavy workflows

In most companies, documents serve as the starting point for a larger business process. A buyer often needs a proposal or quote, as well as a contract. An employee needs an offer letter, hiring paperwork, and may need to sign an NDA or other disclosures.

All of these documents contain valuable information that can be used in other aspects of the business. For example, generating a second quote for an established customer is faster if the customer’s information is already in the database. However, building that customer profile is a manual process that may require reviewing old documents and importing past data.

IDP helps remove the friction and delays caused by manual tasks, making it easier for teams to keep systems, approvals, and reporting aligned as document volume increases. Below are three examples of how IDP can affect workflows for specific sets of documents.

Contracts and NDAs

Even after they’ve been signed, contracts can continue to create operational challenges.

To abide by contract terms, the legal and operations teams need to review critical details such as renewal windows, payment intervals, termination clauses, and notice periods. Unfortunately, this information is often buried in static PDFs, which become more difficult to manage as the number of contracts increases.

For example, a legal operations manager might set aside several hours every Monday to review contracts scheduled for renewal within 90 days. The process involves opening each individual agreement, locating renewal clauses, updating spreadsheets, and notifying internal stakeholders before those deadlines are missed. Without this level of oversight, the company may fail to lock in renewals before a contract expires, creating more work as teams are forced to renegotiate terms under a new deal.

With IDP, most of that work can happen automatically. As contracts are uploaded or signed, AI can extract details like renewal dates, counterparties, payment terms, and key clauses directly from the agreement and add them to a searchable database. Legal teams can generate reports without conducting line-by-line reviews while operations teams monitor upcoming renewals using structured data.

By allowing IDP to handle these operational tasks, workers can reclaim their time and turn their attention to other matters with no loss in overall efficiency.

Learn more about what data AI can pull from contracts.

Proposals and quotes

Sales documents such as proposals and quotes contain information that supports a transaction, including customer details, pricing and discounts, and approval notes. Once those files are sent or signed, these details may need to be manually transferred to another system to continue the sales process.

While that administrative burden may feel relatively minor for a single rep inputting the details of a single sale into a CRM or similar database, the manual approach doesn’t scale well. Growing teams may need to process dozens of proposals each week while keeping CRMs, reporting dashboards, and customer data aligned across multiple tools.

Doing all of that manually isn’t practical and is prone to user error. During transfer, a rep may miskey an entry or add information to the wrong profile, creating confusion down the line. Worse, these mistakes can remain hidden for months or years before being uncovered (often by accident) during an unrelated process or review.

With IDP, proposal data can be extracted and organized automatically as documents move through the sales cycle. For example, once a proposal is approved, AI can identify customer information, contract values, product details, and pricing terms before routing that information into a CRM or similar tool. Workflows and dashboards can also be updated automatically, giving sales leaders and managers a more up-to-date picture of where deals and opportunities stand in the pipeline.

Forms and onboarding documents

To properly onboard a new hire or customer, companies typically need to collect information from multiple sources, including forms, disclosures, applications, and similar documents. Even if all documentation is submitted digitally, employees may need to manually validate and transfer the information to other systems or files to move the initiative forward.

While historically necessary, these manual tasks slow down onboarding workflows, especially if organizations require large amounts of information spread across multiple teams. When that happens, it’s easy for important details to become lost in translation, as different teams have different, disconnected information.

For example, a customer onboarding team might receive signed agreements, tax forms, intake questionnaires, and compliance documents from a new client across several separate uploads. However, when the finance team realizes they’re missing a document, they may request that information directly from the customer rather than going through the onboarding team. Now, information is moving through two separate channels, but neither team may have full visibility into what was sent or where it went.

Without automation, someone has to review all of those documents manually and update records across all existing systems. If internal systems aren’t connected, customer details may need to be re-keyed across multiple systems, or data may be split into smaller, isolated profiles based on team usage (e.g., all profiles have customer details, but the sales team keeps transaction data while the legal team stores contract terms separately).

With IDP, AI can automatically extract customer details, signatures, approval information, and onboarding data while routing it to connected platforms and workflows. Office admins won’t need to spend hours organizing paperwork, and information flows automatically into the appropriate profile, allowing teams to onboard more quickly and without administrative delays.

Learn more about how you can automate customer onboarding with PandaDoc.

What to look for in IDP tools

Modern document AI is much broader than single-function solutions like OCR or automated data entry. Businesses can now access systems that read documents, extract information, and organize it into actionable data points that automatically trigger downstream workflows.

IDP and similar tools are at the heart of that change. AI solutions, supplemental tools, and machine learning allow the system to read and understand documents, extract data, and organize it in ways that integrate well with the other software tools that the businesses use.

Because IDP solutions have such a broad range of features and applications, it’s important for brands to consider workflow impact as much as overall functionality.

Here’s what to look for.

  • Accuracy across document types. Strong IDP systems should process both structured and unstructured data reliably across a variety of document types. A solution that can only read specific types of documents or that forces brands to conform to a specific layout or format may not fit well with existing processes.
  • No-code or low-code usability. Ops teams should be able to manage IDP-related workflows without calling in a technical specialist or relying solely on the IT department. While advanced features and integrations may require configuration and setup from an expert, the majority of workflow programming should be doable by the teams that will be working with the system.
  • Integration with existing systems. Extracted data should move seamlessly into CRMs, approval workflows, reporting platforms, and other connected tools. The IDP solution you ultimately choose should be able to send the data it collects into these systems.
  • Security and compliance. Companies handling sensitive records should evaluate governance controls, access management, and compliance standards very carefully. When IDP is part of a larger system, as with PandaDoc’s IDP options, it’s important to understand how data is governed by TOS agreements and security commitments.
  • Continuous learning. Effectively, IDP systems are equipped with machine learning, which allows them to become more refined as they process additional documents. Without built-in learning functionality, the AI will require constant retraining as formats evolve and new documents are introduced.

As brands continue to invest in workflow automation, the ability to use existing data to its full potential will become increasingly important across sales, legal, and other teams.

How IDP fits into modern document AI

Contract AI tools can expedite document generation and keep employee headcount low, but many of these processes require data found in existing quotes, proposals, and other business documents.

IDP helps to solve that problem by allowing AI systems to read, understand, and structure document information automatically. That structured data can then support faster workflows, better reporting visibility, and more connected business operations.

Put another way, IDP collects the data that downstream systems use to function and organizes it in such a way that makes it easier to use and apply for various use cases. By combining IDP with a user-friendly interface, like PandaDoc’s AI Assistant, locating relevant data becomes even faster.

Here’s a closer look at where IDP assists in modern business workflows.

Turn documents into usable business data

Any business workflow runs on information, including customer names, contract terms, points of contacts, and more. Companies have that information, but it’s usually locked in static PDFs or files in a disconnected repository.

In its current state, that data is unstructured and difficult to utilize. Employees need to access each document, manually search for the relevant information, and then manually export it by typing or copying/pasting.

With IDP, extracting and organizing information happens automatically. Details are added to searchable databases, making them easier to use across connected systems. While those static records still exist, employees won’t need to sift through them to get the information they need.

Eliminate repetitive data entry and review

Many business documents contain information that teams will eventually need somewhere else. Without automation, employees need to review a document manually, find the relevant information, and then re-key those details into another system.

This process may repeat itself multiple times. The sales team may access the document and key information into the CRM, while the legal team might need the same information added to an entirely different system. And the problem only gets worse as document volume grows.

IDP streamlines that workflow by automating document review and data extraction behind the scenes, so teams can process larger volumes of documents without adding headcount or wasting time.

Connect documents to automated business processes

Because automated processes require structured information to work properly, IDP can become a foundational component for business workflows across all departments. Contracts, proposals, forms, and other documents can extract customer information, sales data, legal language, and other elements that teams need to make important decisions or launch specific initiatives.

For example, a company that wants to connect with dormant accounts might create an outreach campaign using data extracted from completed contracts more than six months old. Since IDP has already collected and sorted that data, marketers with access to the sales database can grab the names and contract information necessary for their campaign without wasting time accessing each document individually.

The same is true for processes related to CRMs, finance, outreach, and more. As downstream initiatives become more automated and data-heavy, IDP becomes an essential component that keeps new data flowing into the system.

Surface important document data automatically

Static documents create visibility problems because the information inside isn’t always connected to the systems teams use every day.

Important operational details might technically exist somewhere in the document repository, but actually locating those details can require a huge time commitment. As initiatives get bigger and document volumes increase, searching for a single, disconnected document can feel like searching for a needle in a haystack.

IDP makes information more accessible by identifying and organizing key data points automatically. During the extraction process, that data becomes available for use in other parts of the network, greatly reducing the time required to find necessary details.

Move faster without adding administrative overhead

During growth periods, it’s possible for document workflows to become operational bottlenecks. Manual reviews, repetitive data entry, and disconnected systems all contribute to delays and slow approvals that cost time and profits.

This problem doesn’t go away. Systems become overloaded with so much information that teams struggle to stay organized and workflows eventually begin to break down. Traditionally, this fallout requires the company to bring in new resources or reallocate support from other departments to help.

IDP can help brands move faster by automating parts of the document workflow that would otherwise require heavy manual effort. In doing so, teams can process more documents and support larger workflows while avoiding the administrative burdens that would otherwise slow them down.

Build smarter document workflows with PandaDoc IDP

Documents already drive most business processes, and volume only increases as a business grows.

Eventually, the time that teams spend updating systems, organizing records, and searching for information will begin to chip away at the meaningful contributions they make to the brand. Until automated systems are implemented, this work is usually necessary. Without taking the time to keep systems organized and up to date, companies will miss opportunities visible in their own datasets.

PandaDoc IDP helps organizations reduce document information by offloading those housekeeping tasks to an automated system. When documents are completed or uploaded to PandaDoc, our built-in system parses and extracts details so they’re available in your contract repository.

It’s fast, easy, and automatic, allowing teams to stay efficient while making the best use of previously hidden information.

Get a closer look at how IDP works by signing up for a 14-day trial.

Not sure how to use that information or have questions about bringing your documents into PandaDoc? Get in touch with a product specialist for a personalized demo.

Disclaimer

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Intelligent document processing (IDP) is a technology that uses AI to read, classify, and extract information from business documents automatically. Unlike traditional OCR tools, IDP understands context within the document and organizes information into structured data that can support reporting, automation, and in-house workflows.

OCR converts scanned files and PDFs into machine-readable text, but it doesn’t understand what that information means. IDP is built on top of OCR technology and uses AI to establish context and fill in those gaps.

Put another way, OCR reads the text while IDP understands, interprets, and structures document information. The two aren’t competing technologies but instead work together to make IDP possible.

IDP can process both structured and unstructured documents, including any of the following and more:

  • Contracts
  • Invoices
  • Forms
  • Purchase orders
  • Onboarding documents
  • Insurance claims
  • Proposals
  • Tax forms
  • Compliance paperwork

Unlike template-based automation tools, IDP can handle varying layouts and document formats without requiring teams to build documents around specific, rigid structures in every file.

No. Historically, early IDP systems were associated with large (and costly) enterprise automation projects, but modern platforms like PandaDoc offer IDP solutions that are much more accessible for SMBs and mid-market teams.

Any business handling recurring document workflows can benefit from reducing manual reviews and repetitive data entry tasks while connecting document data to the rest of their tech stack.

IDP is the broader technology framework that allows AI to read and understand documents.

AI data extraction is a specific application of that capability focused on identifying and organizing fields like customer names, payment terms, renewal days, etc.

IDP uses AI data extraction as part of its process, but IDP includes other aspects (reading data, establishing context, etc.) that aren’t covered by AI data extraction.

Robotic process automation (RPA) automates repetitive, rules-based tasks like moving files, updating records, or triggering workflows. However, RPA usually depends on structured information in order to function.

IDP helps provide that structure by extracting and organizing information from documents automatically. The two technologies work together inside larger workflow automation systems.

Yes! Contracts are one of the most valuable use cases for IDP because agreements contain important data that businesses need to track over time.

With IDP, data points like renewal dates, payment terms, counterparties, or points of contact can be automatically extracted, reducing the need for manual review and improving data visibility in other systems.