Difference between intelligent document processing and automated document processing

intelligent document processing Automated document processing

When we talk about automating document processes, two terms often come up: Intelligent Document Processing (IDP) and Automated Document Processing (ADP). Although both fulfill similar functions, they operate at different levels.

IDP and ADP aim to automate document-related processes, but their technology, capabilities, and applications differ. 

IDP utilizes advanced cognitive technologies to deal with complicated documents and scenarios, while ADP focuses on efficiently automating structured and semi-structured document processing tasks.

Thus, it is essential to understand the nuances between these two technologies to use them effectively in different business contexts. This article examines both technologies, their use cases, key differences, and a guide to choosing the right document process automation for your organization.

Key Takeaways

  • Difference between IDP and ADP: IDP and ADP automate document processes, but IDP uses advanced cognitive technologies for complex documents, while ADP focuses on structured and semi-structured tasks.
  • Advantages of IDP: IDP speeds up data processing, reduces costs, minimizes errors, and handles various types of data.
  • Use Cases for IDP: IDP automates tasks like invoice processing, order processing, insurance claims, loan applications, customer onboarding, and medical records management.
  • Advantages of ADP: ADP saves time, improves accuracy, ensures compliance, and allows employees to focus on strategic tasks.
  • Use Cases for ADP: ADP automates tasks like invoice processing, expense management, loan processing, contract management, insurance claims processing, and HR document management.
  • Key Differences: IDP uses advanced cognitive technologies for complex tasks, while ADP focuses on structured data extraction. IDP is suitable for complex documents and tasks, while ADP is ideal for structured or semi-structured documents.

Intelligent document processing

intelligent document processing

Image credit: Appian

Today, we are drowning in data, and organizations need help with an overwhelming amount of information, making it challenging to distinguish valuable insights from mere noise.

Extracting actionable insights from massive data sets is often costly and time-consuming. This is where intelligent document processing (IDP) comes into play.

IDP is a complex workflow automation technology designed to efficiently scan, interpret, extract, categorize, and organize meaningful information from large data streams.

Its primary goal is to autonomously extract relevant information from large volumes of data without human intervention. IDP can process various documents, including papers, PDFs, Word documents, spreadsheets, and more.

Advantages of using intelligent document processing

The advantages of integrating IDP into document processing workflows are manifold. 

IDP eliminates the need for manual data capture and processing through artificial intelligence. It helps speed up data processing times and reduce operating costs simultaneously.

In addition, using IDP minimizes human error, increasing efficiency and data accuracy.

In a modern business environment, data usually falls into three categories: structured, unstructured, and semi-structured.

Structured data is organized and easily accessible to human processors. Unstructured data, on the other hand, poses a significant challenge due to its inherent complexity and lack of organization. Semi-structured data lies somewhere in between.

IDP solutions can automate the processing all three data types, highlighting their importance in modern data management.

Use cases for intelligent document processing.

Several use cases exist for intelligent document processing (IDP), including automating the extraction, classification, and processing of information from documents. Below are some everyday use cases for intelligent document processing:

Invoice processing

Relevant data such as invoice numbers, dates, line items, and totals are automatically extracted from invoices received from suppliers. 

This streamlines accounts payable processes, reduces manual effort, and speeds up payment cycles.

Order processing

Companies can improve procurement processes and order management by automating the extraction of essential details from purchase orders, such as order numbers, supplier information, item descriptions, quantities, and prices.

Insurance claims processing

Insurance claim forms are automatically processed to extract policyholder details, incident descriptions, claim amounts, and supporting documentation. This speeds up the claims processing cycle, improves customer satisfaction, and reduces processing errors.

Processing loan applications

Automated data extraction from application forms, which includes information about the applicant, financial details, employment history, and relevant documentation, reduces loan processing times. Compliance is improved, and customer satisfaction is increased.

Customer onboarding

During onboarding, automated extraction and verification of customer identity documents such as passports, driver’s licenses, and utility bills helps financial institutions and other regulated industries comply with KYC (Know Your Customer) and AML (Anti-Money Laundering) regulations. 

This process also speeds up customer account set-up, reduces wait times, and improves overall customer satisfaction.

Management of medical records

Medical facilities can improve coding, billing, and claims processing by automatically extracting patient information, diagnoses, treatments, and medications from medical records, prescriptions, and lab reports.

Review of legal documents

Automatically extracting key clauses, entities, and provisions from legal documents improves administrative efficiency and reduces risk.

Automated document processing

Automated Document Processing

Automated document processing (ADP) simplifies handling various documents by leaving this task to technology. Instead of humans manually reading through papers, machines do the work.

They use clever methods such as OCR to understand text, NLP to capture meaning, and ML/AI to learn and improve over time. 

This combination of technologies enables the automatic extraction of essential details from documents, such as names, dates, and numbers.

With ADP, organizations can save time and effort that would otherwise be spent on tedious manual tasks. Automating document processing reduces errors as machines are less prone to mistakes than humans.

This not only increases efficiency but also ensures the accuracy of extracted information. 

In addition, ADP can process large volumes of documents much faster than a human, making it ideal for companies with a lot of paperwork to complete.

ADP allows companies to rationalize their processes and use resources more effectively. As the technology takes over routine document processing tasks, employees can focus on more strategic and value-adding activities. 

This improves productivity and allows companies to remain competitive in today’s fast-paced business world. Ultimately, automated document processing simplifies document management and improves overall business performance.

Benefits of automated document processing

Automated document processing (ADP) brings several benefits to businesses. First, it saves time and reduces the need for manual labor.

With ADP, machines automatically perform tasks such as data entry and document sorting, allowing employees to focus on more critical aspects of their work. 

This efficiency leads to cost savings for companies as they can do more in less time.

Secondly, ADP improves accuracy by minimizing human error. Machines are less prone to error than humans, especially regarding repetitive tasks such as extracting data from documents. 

By ensuring the accuracy of extracted information, ADP helps organizations make more informed decisions based on reliable data.

ADP also improves compliance with legal regulations. By automating document processing, organizations can enforce consistent and accurate data processing, reducing the risk of compliance issues. This not only helps to avoid penalties and legal consequences but also builds trust with customers and stakeholders.

The benefits of ADP include increased efficiency, improved accuracy, cost savings, and better compliance. By using technology to streamline document processing, companies can achieve higher productivity, reduce operating costs, and stay ahead of today’s competition.

Automated document processing use cases

Automated document processing

Automated document processing (ADP) is used in various industries and business functions. Everyday use cases include:

Invoice processing

ADP automates invoice data extraction, streamlining accounts payable, reducing errors, and speeding up payments.

Expense management

ADP simplifies expense management by automatically capturing and categorizing expense data from receipts and reports. It ensures compliance with expense policies and provides employees and finance teams real-time insight into expenses.

Loan processing

In the banking and finance sector, ADP can automate the processing of loan applications by extracting borrower information, verifying income, and analyzing credit history to streamline the loan approval process.

Contract management

ADP helps organizations manage the contract lifecycle more efficiently by extracting key terms and clauses from contracts and agreements. This improves contract compliance and reduces legal risk by identifying obligations, deadlines, and risks.

Insurance claims processing

ADP automates insurance claims processing by extracting relevant details from documents to speed up processing, reduce fraud, and improve customer satisfaction.

HR document management

ADP automates HR document management by extracting employee information from resumes, applications, and other documents. This optimizes recruitment, onboarding, and HR administration processes.

Key differences between Intelligent Document Processing and Automated Document Processing

intelligent document processing VS Automated document processing

The main differences between Intelligent Document Processing and Automated Document Processing

Intelligent Document Processing (IDP) and Automated Document Processing (ADP) aim to automate document-centric processes but differ in technology, features, and applications.


Intelligent document processing (IDP) is an advanced technology that utilizes artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and robotic process automation (RPA). 

IDP systems are trained to understand the content of documents in a human-like way, extract complex information, and make decisions based on context.

In contrast, automated document processing (ADP) relies on technologies such as optical character recognition (OCR), pattern recognition, and rule-based processing. 

While ADP systems are efficient at automating routine document processing tasks, they lack the advanced cognitive capabilities of IDP systems, which can process more complex documents and scenarios.


Intelligent Document Processing (IDP) technology uses advanced cognitive capabilities to understand the meaning and context behind document content. 

This enables IDP systems to extract unstructured data from documents, analyze it, and make informed decisions based on the extracted information. 

IDP can process various document types and adapt to changing layouts and formats.

Automated Document Processing (ADP) technology, on the other hand, focuses on automating structured and semi-structured document processing tasks. 

ADP systems are ideal for extracting predefined data fields from documents, such as names, dates, amounts, and addresses. However, ADP can need help extracting unstructured data or processing documents with different layouts and formats.


Intelligent Document Processing (IDP) technology can process complex documents and perform advanced cognitive tasks. 

It is often used for contract analysis, regulatory compliance, fraud detection, and customer service automation. 

Industries such as finance, legal, healthcare, and insurance often use IDP systems.

Automated Document Processing (ADP) is suitable for extracting structured data from documents. 

ADP systems are widely used in various industries to automate routine document processing tasks, such as invoice processing, expense management, customer onboarding, and HR document management. 

ADP is an effective way to improve operational efficiency.

The choice of IDP or ADP depends on the specific requirements and complexity of the document processing use case.

Conclusion: Choosing the right solution for your business

When deciding between Intelligent Document Processing (IDP) and Automated Document Processing (ADP) for your organization, you should consider the complexity of your documents, the level of cognitive capabilities required, and the specific requirements of the use case.

If your documents are complex, contain unstructured data, or require nuanced interpretation, IDP may be the better choice due to its advanced cognitive capabilities. 

IDP is ideal for use cases such as contract analysis, regulatory compliance, and fraud detection, where understanding context and making decisions based on document content is critical.

On the other hand, if your documents are mainly structured or semi-structured and require simple data extraction, ADP may suffice.

ADP is suitable for use cases such as invoice processing, expense management, and HR document management, where structured data extraction is the main requirement and no advanced cognitive features are needed.

In addition to these considerations, it is important to consider other factors such as scalability, budget, integration capabilities, and vendor reputation when making a decision. 

While IDP systems offer more scalability and flexibility, they tend to be more expensive and require AI and machine learning expertise to implement.

ADP systems, on the other hand, can be less expensive and easier to implement, making them suitable for companies with limited budgets or technical resources.

Make sure the solution you choose integrates seamlessly with your existing systems and workflows, and research the reputation and track record of the vendors to ensure the long-term success of your document processing initiative.

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