Case in point: In the past few years, Generative AI (GenAI) has revolutionized Intelligent Document Processing (IDP), filling in the gaps and automating the areas where existing solutions fall short. Indeed, GenAI is enhancing accuracy, speeding up automation of complex tasks, cutting costs, and boosting productivity like never before.
If your business handles a large volume of documents—medical records, claims, invoices, or loan applications—then a GenAI-fueled IDP service makes operations smoother, faster, and less expensive.
Spend a few minutes learning about Intelligent Document Processing. Just like upgrading to the latest smartphone gives you faster performance and new capabilities, embracing modern IDP technology will transform your business operations. This guide will walk you through all things IDP: evolution, best practices, and what’s next. This will future-proof your business.
GenAI-powered intelligent document processing
Intelligent Document Processing is an advanced technology that combines AI, machine learning (ML), optical character recognition (OCR), and automation to capture, extract, and process data from a wide variety of document formats.
Unlike traditional document processing, which relies heavily on manual input and predefined rules, IDP leverages AI to understand and manage both structured and unstructured data, improving efficiency and accuracy in document handling.
Analysts estimate that 80% to 90% of all data is unstructured, meaning it doesn’t fit neatly into predefined formats or databases. Unstructured documents encompass a wide range of formats, including PDFs, scanned images, emails, contracts, invoices, and other text or visual content.
Forms and emails often contain unstructured data like customer inquiries, feedback, or order details, which are typically presented in a freeform manner without following a consistent template. This type of data is more challenging to process and analyze, making it essential for businesses to have the right tools and strategies in place to manage it effectively.
Structured data is information organized clearly and consistently, making it easy to search, sort, and analyze. It’s typically stored in tables, spreadsheets, or databases, where each piece of data fits neatly into predefined categories, like rows and columns.
For example, a fixed form listing customer names, email addresses, and phone numbers in the same way repeatedly is considered structured data because each piece of information is stored in its own specific field. This organization makes it straightforward to understand, retrieve, and work with the data.
Booming and transformative. The global IDP market is on a remarkable growth trajectory, projected to reach USD 7.38 billion by 2028, driven by a compound annual growth rate (CAGR) of approximately 40.94% from 2023 to 2028.
This rapid expansion is fueled by the integration of big data analytics and cloud-based solutions, which are enabling organizations to automate document processing and derive valuable insights from unstructured data.
Manual data entry (Pre-1960s to 1970s): Before computers, businesses relied heavily on manual data entry, where clerks and employees would manually input data into ledgers or early mechanical systems. This era lasted until computers became more accessible and began replacing manual processes.
Optical character recognition (OCR) (1970s to 1990s): The early development of OCR technology began in the 1970s, but it wasn’t widely adopted until the 1980s and 1990s. Early OCR systems could recognize and digitize printed text but were limited by font types, image quality, and layout variations.
Rule-based automation (1980s to 1990s): Next up was rule-based automation. Businesses began using software that could follow predefined rules to automate simple document processing tasks. However, these systems required constant updates and were limited in handling unstructured data.
AI and ML (2000s to Present): The integration of AI and ML into document processing solutions and services started gaining traction in the 2000s. By the 2010s, these technologies became more mainstream, enabling significant advancements in processing unstructured data. AI and ML allowed systems to learn from data, improving accuracy and flexibility.
Intelligent Document Processing (2010s to 2023): IDP represents the gold standard of document processing, combining AI, ML, OCR, and other technologies. IDP services became more prominent in the 2010s as businesses sought to automate complex document workflows and enhance operational efficiency. The adoption of IDP has accelerated in the 2020s, driven by digital transformation and the need to process large volumes of unstructured data.
AI is the 4th wave for IDP (2023 to Present): Unlike previous waves focused on rule-based systems and basic ML, this new era leverages GenAI and LLMs to transform how documents are understood and processed. GenAI-powered IDP is enhancing accuracy, speeding up workflows, and automating complex tasks with unprecedented precision.
By integrating advanced AI, IDP solutions can now handle diverse document types and unstructured data, making them indispensable for organizations seeking to boost productivity, reduce costs, and remain agile in today’s fast-changing digital world.
See how that works by watching the following video:
First is the increasing integration of ML. As ML models evolve, they’ll enable systems to recognize and extract data from complex documents with greater accuracy and efficiency. Smart document solutions are also becoming more adaptive, allowing businesses to handle a broader range of document types and formats automatically.
AI is being used to predict and preemptively address issues before they arise, which makes document processing faster and more reliable. Data redaction is becoming essential, ensuring sensitive information is securely removed or obscured to meet privacy regulations like GDPR and protect against unauthorized access.
Another significant trend is the rise of cloud-based solutions, which offer businesses the scalability to expand their document processing capabilities seamlessly as they grow.
Meanwhile, GenAI is set to revolutionize IDP by going beyond simple data extraction. It can anticipate missing information, propose corrections, and even generate summaries or detailed reports, simulating human understanding to enhance both speed and accuracy.
These technological advancements will greatly benefit businesses by reducing manual tasks, lowering costs, and improving efficiency. Companies will be able to spend less time managing documents and more time on strategic initiatives. By leveraging these smart tools, businesses can gain valuable insights, ensure compliance, and maintain a competitive edge.
To understand how IDP software works, it’s important to understand these 5 steps:
This initial step prepares documents by enhancing image quality, categorizing documents by type, and separating multi-page documents. Ensuring clear, organized, and correctly classified input lays the groundwork for accurate data capture and efficient processing.
Next is intelligent data capture, which uses technology to understand and capture information from documents. This means it can read and recognize text, numbers, and even handwriting in both digital and paper formats.
Once information is captured, automated data extraction kicks in. This step pulls out relevant data from the documents. It automatically picks up details like names, dates, or amounts without needing someone to do it manually.
AI and ML help the system get smarter over time. As more documents are processed, the system learns to recognize patterns and improves its accuracy, making the whole process faster and more reliable.
Humans are necessary to ensure accuracy, particularly in complex or ambiguous cases. In this step, humans validate any extracted data with a low-confidence threshold, correct any errors, and provide feedback that helps the system improve. This combination of automated processing and human expertise ensures the highest levels of accuracy and reliability.
This video explains the critical role humans play in IDP solutions:
Intelligent Document Processing automates the extraction of data from complex and unstructured documents, a task that traditionally required significant manual effort. It uses advanced technologies like OCR, natural language processing (NLP), ML, and GenAI to process these documents.
OCR converts scanned images and handwritten text into machine-readable formats, but on its own, it can’t interpret the content’s meaning. This is where NLP comes in, allowing the system to understand the context of the text, much like a human would.
ML further enhances this by learning from patterns and improving accuracy over time, allowing the system to extract specific information like names, dates, and amounts from various document types accurately.
GenAI takes IDP to the next level by extracting and interpreting data and generating new, meaningful content based on the information. It enhances the system’s ability to handle diverse and complex document types by predicting and filling in missing data, refining document categorization, and even simulating human-like understanding. This allows IDP solutions to provide more accurate insights and automate decision-making processes that would typically require human judgment.
IDP can automatically classify and categorize documents, and separate documents from one another. After OCR converts the document into a readable format, ML models classify the documents based on specific features like keywords, structure, or patterns. NLP helps by understanding the text’s context, further refining the classification process.
When a file contains multiple documents, the system can segment and categorize each one separately using computer vision techniques. This automated classification streamlines document management, reducing processing time and minimizing human error.
IDP tools ensure the accuracy and reliability of extracted data through a robust validation process. After data is extracted, it’s checked for completeness and consistency.
Advanced algorithms cross-verify the data against predefined business rules, such as ensuring an invoice date is not in the future or that an order number follows a specific format.
The system can also compare extracted data with other documents or external sources for further data verification. If any data fails these checks, It’s flagged for manual review, ensuring that only accurate and reliable data moves forward in automated business processes.
IDP solutions transform raw data into valuable insights by leveraging AI technologies like NLP and ML after data is extracted and validated. These tools analyze the information to uncover patterns, predict trends, and even assess sentiment in customer communications.
IDP visualizes data through graphs and charts, making it easier for decision-makers to understand and act on insights and integrates seamlessly with existing Business Intelligence tools. This integration provides high-quality data that enhances the accuracy of business reports and analytics, enabling more informed decisions.
Yes, especially in processes that require high accuracy and judgment. While IDP automates much of the document processing through technologies like OCR, NLP, and ML, human involvement is absolutely necessary.
Intelligent Document Processing is a total game-changer for businesses. You might think, “Do I really need this?”
You do. Here’s why.
By automating document workflows, you can bid farewell to tedious manual data entry and say hello to more time for important tasks. You can have all your documents processed and sorted without lifting a finger.
We all know that humans make mistakes—especially when it comes to repetitive tasks like data entry. With Intelligent Document Processing, those errors become a thing of the past. IDP uses advanced AI and ML plus HITL to ensure accuracy, so you can trust that your data is spot-on.
Implementing IDP can lead to big cost savings. Think about all the money you spend on manual labor and correcting errors. With IDP you can cut those costs drastically. Plus, with more efficient processes, your team can focus on value-added activities that drive your business forward.
Staying ahead means embracing new technologies. Intelligent Document Processing is a key player in the digital transformation of document management. It modernizes your workflows and seamlessly integrates with other digital tools and systems of record, creating a cohesive and efficient system.
As your business grows, so does the volume of incoming documents. Manually processing all that data just isn’t feasible. IDP tools scale with your business, effortlessly handling increasing workloads. Whether you’re a small startup or a large enterprise, Intelligent Document Processing adapts to your needs, making it a smart investment for long-term growth.
For instance, see how a partnership with Noventi enabled them to scale their document processing capabilities without compromising on speed or accuracy. By leveraging ScaleHub’s IDP service, Noventi was able to manage unexpected surges in document volume efficiently, ensuring consistent, high-quality output and client satisfaction.
A quality IDP solution or service will go to the ends of the earth to make sure your sensitive information is secure. Automated processes reduce the risk of human error, and advanced security features ensure that your data is protected at every step. This peace of mind is invaluable, especially in industries that deal with uber-sensitive documents like medical records or insurance claims.
Our solution allows you to keep the full document image within your own environment, sending only snippets into the cloud for processing. This approach ensures that sensitive data remains secure and under your control.
Snippeting is when document images and/or fields are broken down and separated into small bits of information called snippets. These completely anonymous data nuggets are deprived of any context that could jeopardize data privacy. And if, for some reason, sensitive data must be exposed, it can be sent to a specialized crowd—one with more specific skills and data compliance certifications to match the task.
Watch this video to learn more about snippeting:
IDP systems must manage sensitive data, such as that found in financial records or medical documents, which requires strict adherence to data protection laws like GDPR or HIPAA. Ensuring an IDP solution meets all compliance requirements while maintaining high levels of data security can be challenging, particularly in heavily regulated industries.
Integrating an IDP solution with legacy systems can be a royal pain. The complexity of ensuring smooth data flow between the IDP solution and other tools can lead to workflow disruptions if not managed carefully. Poor integration may result in data silos and reduce the overall efficiency of document processing.
Businesses often overlook the crucial role of a human workforce in the IDP process. It’s important to acknowledge that while IDP technology is advancing, it still cannot fully replicate human cognitive abilities.
Completely shifting a process from human operation to IDP can produce mixed results, which is why it’s essential to implement these technologies with human-in-the-loop capabilities.
Processing everything from structured forms to unstructured emails and scanned images can result in inconsistencies and errors in data extraction if an IDP solution is not properly configured. This complexity requires significant effort to ensure the solution can accurately process all types of documents.
Implementing an IDP solution involves substantial costs, including software, hardware, training, professional services, and ongoing maintenance. Allocating the necessary resources, both financial and human, can be challenging, particularly for small and medium-sized businesses that may struggle with the high initial investment.
People hate change. Shifting to an IDP system requires significant changes in workflows and employee roles. Resistance to change, coupled with inadequate training, can hinder the adoption of a new system. Effective change management strategies are essential to ensure that employees adapt smoothly and that the IDP system is fully utilized.
As a business grows, its document processing needs may expand, requiring its IDP solution to scale as well. Not all IDP solutions are designed for easy scalability, which can lead to performance bottlenecks and inefficiencies as document volumes increase. Ensuring that the chosen IDP solution can scale with the business is crucial to maintaining smooth operations.
Many businesses start with a proof of concept (PoC), but without a solid business case, it’s challenging to demonstrate how IDP can be scaled.
This can lead to difficulties in justifying the investment to business leaders, ultimately resulting in unmet expectations. Without a well-defined strategy in your PoC, managing the vast number of documents your business handles—often in various formats—can become overwhelming.
This lack of clarity can lead to disappointing outcomes as the system struggles to meet demands.
Once you’ve identified your process bottlenecks and selected Intelligent Document Processing (IDP) as a solution, the first step is to gain a complete understanding of your current processes. If you’re processing insurance forms, for example, start by identifying which invoices are still being handled manually. By doing this, you’ll gain a clear understanding of where inefficiencies lie, allowing you to present a compelling case to your finance team that highlights the labor savings an IDP solution can provide.
After securing internal buy-in to purchase and implement an IDP solution, the next step is to research and identify the minimum viable product (MVP) that IDP can deliver. This MVP is an example to your C-Suite, demonstrating the quick returns and efficiency gains that IDP can provide. Showcasing these early successes will help build momentum and support for broader implementation across the organization.
A common mistake businesses make when working with IDP platforms is not fully exploring the range of solutions it can offer. Many organizations focus solely on the labor savings associated with automating invoice processing.
However, it’s crucial to consider other areas where IDP can add value. For example, IDP can streamline contract management by automating the extraction and organization of key terms and dates from vendor agreements. This ensures important deadlines are met, reducing the risk of missed renewals or compliance issues, and ultimately strengthening vendor relationships.
Whether it’s paying a supplier on time, approving a loan for a new business, or making a patient’s health records available at a specialty clinic, time is critical when processing the documents that enable these actions. An IDP-fueled process ensures that documents are processed quickly and accurately, allowing payments to suppliers to be made promptly—or even faster. When suppliers are paid on time, they view your business as trustworthy and reliable, strengthening those relationships.
To optimize Intelligent Document Processing, focus on regularly updating your solution to handle new types of documents and data. Ensure that your team is well-trained to use the technology effectively, and consider starting with a small-scale implementation to test and refine the process.
Additionally, it’s important to avoid common pitfalls like neglecting data security, overlooking the need for regular maintenance (if an on-premises solution), or failing to integrate the solution with your existing tools and workflows.
Read how NOVENTI enlisted collective intelligence to relieve staff amidst extremely strict patient data protection regulations and significant day-to-day fluctuations in medical processing.
LEARN MOREGet the full story on how Vital Records Control (VRC) implemented IDP and gifted themselves the time to focus on getting new clients.
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