Intelligent Document Processing with GenAI: Key Use Cases

Plus, if you’re considering implementing IDP in your organization, you’ll gain valuable guidance on whether to build a custom solution or opt for an existing platform that fits your needs.

What is Intelligent Document Processing (IDP) technology?

Unlike traditional systems that follow set rules and basic conditional logic based on lexicons, IDP uses AI to automate document processing. It converts unstructured data—like scanned documents, PDFs, and emails—into structured, helpful information. This helps organizations handle large amounts of documents quickly and accurately. 

But IDP does more than scan or digitize documents—

The technique enables AI to read, understand, and categorize data, simulating human comprehension but at a much faster pace.

For example, IDP can automatically process invoices, extract important details, classify the content, and even check the information against set rules.

To understand IDP better, compare it to basic OCR. While OCR simply converts printed text to digital text, IDP goes beyond – it understands the content, makes sense of the data, and integrates it into business processes.

In other words, IDP uses AI to make document management smarter, more efficient, and less dependent on manual work. With less manual effort, the risk of human error drops, which allows businesses to save time and boost productivity in many areas. 

TLDR: Modern Intelligent Document Processing uses AI as a document management copilot. It takes care of repetitive tasks, such as data extraction and sorting, with minimal human effort and high efficiency.   

Benefits of implementing document processing automation

Analysts estimated the global IDP market size at $1.45 billion in 2022, and recent industry reports expect it to grow at a compound annual growth rate (CAGR) of 30.1% from 2023 to 2030.   

Key factors driving market growth include increased investments in digital transformation and the need for cheaper and more efficient document processing solutions. Also, ongoing digitalization in developing countries offers significant growth opportunities.  

For example, IDP has grown beyond finance and accounting. It now works in many areas, such as:

• Managing medical records

• Financial report analysis

• Contract search and reviews

• Know Your Customer (KYC) processes

• Operations in various institutions (e.g., universities, libraries, and government agencies)

• Marketing lead scoring and data enrichment

• Research paper summarization and translation

And many more. 

But what are the actual benefits of implementing IDP automation? 

In no particular order: 

1/ Cost savings 

Automating document processing can lead to significant cost reductions across industries. For instance, according to Payables Place, companies that automate invoice processing have an 80% lower invoice processing cost.

Companies that automate invoice processing with AI have reduced invoice processing cost by 80%

2/ Increased efficiency

Automation accelerates document processing by handling tasks at a pace far beyond human capabilities. Automated systems can process hundreds or thousands of documents in a fraction of the time it would take a human worker. This boost in processing speed translates to quicker turnaround times, allowing businesses to handle more tasks simultaneously and meet tight deadlines more effectively.

3/ Improved accuracy

Human errors in document processing can lead to costly mistakes and inefficiencies. Automated systems, powered by AI and machine learning, significantly reduce the risk of errors by following consistent rules and algorithms. These systems are designed to interpret and process documents with high precision, ensuring that data is captured accurately and reducing the likelihood of costly mistakes.

4/ Enhanced scalability

As businesses grow, so does the volume of documents they need to process. IDP automation solutions are scalable, meaning they can easily handle increased volumes of documents without a corresponding increase in manual labor. This scalability ensures that businesses can adapt to growth seamlessly and maintain efficient operations regardless of document volume.

5/ Streamlined workflows

Automation helps to streamline workflows by integrating document processing tasks into a unified system. This integration reduces the need for manual intervention and ensures that documents move through processing stages smoothly and without delays. And since humans are free from manual, repetitive tasks, they are able to focus on more strategic and value-added activities, making them much more productive.

6/ Enhanced data security

Automated document processing systems often have advanced security features protecting sensitive information. These features include encryption, access controls, and audit trails that ensure data is secure from unauthorized access and breaches. Enhanced security measures are crucial for maintaining compliance and protecting confidential information.

7/ Better compliance and reporting

Automated systems generate detailed reports and maintain records of document processing activities. As a result, it is easier for businesses to adhere to legal and industry standards. This improved documentation and reporting support better governance and compliance management.

8/ Improved customer experience

In customer-facing sectors like banking and healthcare, automating document processing can greatly enhance the customer experience. For example, when processing loan applications, automation allows quicker and more reliable handling of documents. This means faster responses to customer inquiries and quicker resolutions to issues, which boosts customer satisfaction.

What is the difference between OCR, IDP, and RPA?

Optical Character Recognition (OCR), Intelligent Document Processing (IDP), and Robotic Process Automation (RPA) are technologies used for document automation, each with a different focus.

As we mentioned before, OCR converts printed or handwritten text into digital, machine-readable data. It's good for digitizing documents but only extracts basic text without understanding it. IDP takes OCR further by adding AI-like NLP and machine learning. It doesn’t just extract text; it understands, classifies, and processes information, turning unstructured data into useful insights.

RPA automates repetitive tasks by mimicking user actions in digital systems. For instance, it can be used to create workflows that open documents, extract data, and send information to databases. Traditional RPA needs detailed setups, but GenAI tools, like V7’s Go Copilot (AskGo), simplify this with single-command automation.

How are IDP and RPA different?

In some use cases, RPA and IDP can be used interchangeably. RPA is a broad term that involves solving a variety of problems through pre-recorded actions and highly customizable automation. IDP, as the name suggests, focuses specifically on document automation. While it is possible to address document processes with RPA, there are typically dedicated document processing platforms that are more specialized and document-focused.

These technologies form the foundation for modern AI-based document processing, enabling IDP to deliver intelligent document management. If you need a quick overview, here is a detailed description of the evolution of OCR and IDP software.

Let’s take a deeper look at what other technologies play a role in this process: 

Key technologies used in IDP

OCR

OCR is a technology that converts different types of documents—such as scanned paper documents, PDFs, or images—into machine-readable text. It identifies characters from an image and translates them into text that a computer can process. 

For example, imagine you have a scanned image of an invoice—

OCR can "read" the text on the invoice (such as the date, amount, and item list) and turn it into editable and searchable data. While OCR is excellent for digitizing printed text, it doesn't understand the context of the information.

In an IDP workflow, OCR is often the first step, converting the visual content into a machine-readable format before more advanced AI processes (like classification and analysis) take over.

Learn more: What Is the Best OCR Software for Business?

NLP

Natural Language Processing (NLP) is a branch of AI that enables machines to understand, interpret, and manipulate human language. It focuses on the interaction between computers and natural languages to extract meaning, identify patterns, and process unstructured data, such as text-heavy documents.

For example, when analyzing a legal contract, NLP techniques can be applied to identify key clauses, extract details (e.g., parties involved, dates, obligations), and classify the document based on its content. NLP also performs tasks such as sentiment analysis, which determines the tone or emotion in the text, summarization, and named entity recognition to detect names, dates, and other specific entities.

NLP plays a crucial role in IDP. Namely, it enables systems to move beyond basic text extraction, helping categorize documents by context, summarize key points, and understand the overall meaning.

GenAI

Generative AI refers to AI models that can create new content based on the data they have been trained on. This can include generating text, images, or even video. Models like GPT (Generative Pre-trained Transformer) are commonly used for creating human-like text responses based on input prompts.

In document processing, GenAI can automatically generate summaries for long reports or create template-based responses to common queries. For instance, after analyzing a lengthy research paper, GenAI can produce a concise summary highlighting the key findings.

In other words, GenAI enhances IDP by automating tasks like document summarization or report generation. Rather than just extracting information, it can create new insights or interpretations based on the content, significantly reducing the time and effort needed for tasks like reading long documents or producing custom summaries.

Read more: Impact of GenAI & Large Language Models on Enterprise: Benefits, Risks & Tools

RAG

Retrieval-Augmented Generation (RAG) is a cutting-edge approach that combines information retrieval with generative AI. Unlike traditional systems that either generate new content or retrieve existing content, RAG can pull relevant information from a database or document repository and then use that data to create a more informed, accurate response or output.

For example, If a user needs a detailed report on financial statements across different quarters, RAG can search the company’s document archive to retrieve relevant data, and then use generative AI to create a coherent, summarized report that integrates all the key information.

Take a look at this example of AI analysis of financial reports:

Multiple 10-Q forms are uploaded to V7, and then a workflow extracts key information and identifies aspects such as regulatory risks or financial opportunities.

In IDP, RAG is particularly useful for complex document queries where retrieving and generating responses is required. For example, it can assist in legal research by pulling relevant cases or regulations from a large database, summarizing them, and then creating an output based on that combination of retrieval and analysis.

Intelligent document processing examples & use cases

As you can see, processing, categorizing, and analyzing large volumes of unstructured data has become a critical business challenge that is, in fact, easier to solve than ever. AI document processing addresses this by leveraging AI to automate tasks that were once time-consuming and prone to human error.

One of the most significant advantages of IDP is its ability to streamline workflows that involve a high volume of documents. With IDP solutions, organizations can automate these processes, speed up turnaround times and enhance accuracy.

Here are some key IDP tasks:

These tasks represent a fraction of the capabilities offered by AI document processing technologies, which can be tailored to address specific challenges in various industries.

Here are some popular types of document automations across different industries and document types: 

Invoice processing

Automated invoice processing involves several critical steps to ensure that invoices are handled efficiently and accurately. The process starts with capturing data from incoming invoices, including invoice numbers, dates, amounts, and vendor information.

Once the data is captured, it is validated against purchase orders and contracts to confirm that the amounts and details match. The system then integrates the validated data into financial systems for further processing, such as initiating payments and updating accounting records. 

This automation significantly reduces manual data entry, speeds up approval workflows, and minimizes errors associated with human handling, leading to more streamlined accounts payable processes and improved financial oversight.

Financial statement analysis

IDP automates the data extraction and analysis of financial statements, such as balance sheets and income statements. Pulling out key figures and metrics enables IDP to speed up the process of financial analysis and reporting, which improves decision-making and ensures regulatory compliance.

Insurance claims processing

IDP can greatly speed up the claims review process by extracting and evaluating information from claims documents, such as applications, supporting materials, and correspondence. The system captures important data and validates it against existing records. This leads to quicker claims approvals and better customer satisfaction.

Contract analysis

Automated contract analysis uses IDP technology to extract and analyze critical terms, conditions, and clauses from various types of contracts. The system identifies key elements and compares them against predefined templates and organizational standards to identify any deviations or inconsistencies. This process helps ensure that contracts comply with legal and regulatory requirements and meet organizational policies.

Automated form processing

IDP systems are adept at processing a wide range of forms. The technology automates the extraction of data from form fields and, once extracted, categorizes the data and organizes it according to the form type and specific requirements. Thus, automated processing eliminates the need for manual data entry and reduces errors associated with human handling. This leads to faster data collection, improved accuracy, and more efficient analysis.

Pitch deck and fund portfolio analysis

IDP tools can analyze pitch decks by extracting and evaluating key components, such as financial projections, business models, market analysis, and team information. The system uses natural language processing (NLP) and machine learning to identify and summarize critical data points, essential for assessing business proposals' viability. This capability helps investors and decision-makers quickly understand a pitch deck's core elements without having to manually review it in detail.

Shipping document processing

Automated shipping document processing involves the extraction of data from shipping documents such as invoices, bills of lading, delivery notes, or shipping labels. The IDP system captures critical information and then uses that data to cross-reference it with inventory records and order details to verify accuracy. By automating these processes, IDP systems streamline logistics management, reduce errors in shipment handling, and ensure real-time updates to inventory records, which contributes to more efficient supply chain operations and accurate shipment tracking.