Intelligent Document Processing Market Size, Share & Trends Analysis Report By Component (Solution, Services), By Deployment Mode (Cloud, On-Premises), By Technology (Natural Language Processing (NLP), Optical Character Recognition (OCR), Machine Learning (ML), Artificial Intelligence (AI), Robotic Process Automation (RPA), Google Vision, Deep Learning (DL)), By End-User (BFSI, Government, Healthcare and Life Sciences, Retail and E-Commerce, Manufacturing, Transportation and Logistics, Others) and By Region(North America, Europe, APAC, Middle East and Africa, LATAM) Forecasts, 2024-2032

Market Overview

The global intelligent document processing market size was valued at USD 1.8 billion in 2023 and is projected to reach USD 2.9 billion by 2032, registering a CAGR of 35.4% during the forecast period (2024-2032). The amount of data enterprises generate continually increases, driving the growth of the Intelligent Document Processing Market.

Intelligent Document Processing (IDP) is a cutting-edge technology that automates the extraction and processing of information from unstructured documents using a variety of artificial intelligence (AI) and machine learning (ML) approaches. Unstructured documents include data in the form of emails, bills, contracts, reports, and other text-heavy documents that do not fit easily into structured databases.

The Intelligent Document Processing Market share is driven by several factors, including the increasing volume of documents businesses must process, the need to improve their document processing efficiency and accuracy, and the growing availability of AI and machine learning technologies. Furthermore, increased digitalization in developing countries presents considerable potential opportunities for the industry. IDP systems gather, classify, and extract meaningful data from documents using technologies such as machine learning (ML), natural language processing (NLP), optical character recognition (OCR), and computer vision.

Highlights

• Solution accounts for the largest share of the market based on components.
• On-premises occupied the maximum share of the market based on deployment mode.
• Machine Learning (ML) is the most commonly used technology in the market.
• BFSI influenced the market growth based on end-users.

Market Dynamics

Global Intelligent Document Processing Market Drivers:

Increasing Automation in Business Process

The trend toward automation in business processes is driving the use of Intelligent Document Processing. Automating document-centric procedures such as data extraction, classification, and validation helps organizations simplify operations, decrease manual labor, and increase accuracy. Gartner found that implementing IDP technologies can significantly reduce manual work and processing time for document-centric tasks. In invoice processing, firms have claimed up to 80% efficiency gains, allowing staff to focus on more strategic and value-added tasks. When metadata or attributes lack the structure required for application, algorithm, and machine utilization, data goes unused and accumulates in large numbers. This data management method unavoidably increases IT complexity and depletes critical storage resources. Even worse, corporations require insights that cannot be obtained elsewhere. According to Process Excellence Network research co-sponsored by SAP, some firms address this difficulty by implementing intelligent document processing (IDP) technology. This next-generation technology, which includes AI capabilities, is quickly becoming a valuable ally for process optimization, innovation, and providing excellent user experiences.

Moreover, According to a recent Deloitte report, more than half of firms expect to implement AI and automation technology by 2023. While many senior executives are concerned about the risks of AI use, other high-achieving firms are using new technologically advanced operational processes. According to a poll of Global 500 firms, leaders that invest in AI and automation business tools and software solutions anticipate significant growth in the following years.

Thus, the growing use of automation platforms and the incorporation of AI-powered technologies for document processing highlight the importance of IDP in the larger context of business process optimization. Businesses understand the benefits of IDP in managing various document types, from invoices and receipts to contracts and forms, which eventually contribute to streamlined processes and increased overall efficiency.

Global Intelligent Document Processing Market Restraints:

Accuracy and Reliability Challenges

One of the most challenging difficulties for Intelligent Document Processing (IDP) systems is guaranteeing constant accuracy while extracting information from a wide range of complex document formats. For example, invoices from different vendors may have varied layouts, fonts, and structures, making it difficult for IDP systems to correctly detect and extract essential data fields such as invoice number, date, and total amount. AIIM (Association for Intelligent Information Management) revealed that data accuracy remains a significant challenge in document processing. According to the AIIM Industry Watch Report, just 18% of those polled indicated "perfect" accuracy in their capture processes. 

This illustrates the existing difficulty in reaching high levels of accuracy, as well as the necessity for developments in IDP technologies to overcome differences in document formats. Furthermore, they mentioned that For a period, the dominant player was intelligent document processing (IDP), also known as "capture." However, robotic process automation (RPA) increasingly matches the functionality of IDP solutions and competes in the data collecting space.

Global Intelligent Document Processing Market Opportunity:

Cloud-Based Solution and SaaS Model

The popularity of cloud-based Intelligent Document Processing (IDP) systems and the Software-as-a-Service (SaaS) model has increased as enterprises seek more scalable, flexible, and cost-effective alternatives to traditional on-premises installations. Cloud-based IDP solutions have various advantages, including lower infrastructure costs, increased accessibility, and the capacity to expand resources in response to demand. According to Gartner, the global public cloud services market is expected to grow by 18% in 2023, reaching USD 495 billion. Meanwhile, IDC predicts worldwide public cloud services revenue will reach USD 663 billion in 2023, up 20.0% from 2022. The growing use of cloud technology across industries reflects a more significant trend of leveraging cloud infrastructure for various business purposes, such as document processing and automation.

Furthermore, organizations using IDP increasingly look to cloud-based solutions to optimize their document operations. Cloud-based IDP solutions, such as those supplied by top providers, let users securely upload, process, and manage documents via the Internet. These systems frequently include advanced AI and machine learning capabilities to ensure reliable data extraction, allowing users to use the platform from anywhere with an internet connection. Utilizing a cloud-based IDP solution gives the firm the agility, scalability, and accessibility the cloud environment provides. Furthermore, the pay-as-you-go pricing model matches the company's changeable document processing requirements, lowering expenses and increasing overall efficiency.

Regional Analysis

North America Dominates the Global Market

The global intelligent document processing market analysis is conducted in North America, Europe, Asia-Pacific, the Middle East and Africa, and Latin America.

North America is the most significant global intelligent document processing market shareholder and is estimated to grow at a CAGR of 35.6% over the forecast period. The United States, Canada, and Mexico have the largest market share in this region. Regional enterprises excel in machine learning, artificial intelligence, computer vision, and natural language processing, driving market growth. Further, Transportation and logistics, healthcare, BFSI, and industrial industries are among the first to use intelligent document processing technologies.

Furthermore, businesses automate internal procedures to streamline activities through intelligent document processing. For example, In May 2023, ABBYY, a smart automation firm, and Pipefy, a process automation platform, announced a cooperation to provide an integrated solution for insurance, finance, and human resource operations. This solution combines ABBYY's Optical Character Recognition (OCR) technology with Pipefy's process automation capabilities to reduce time-consuming manual document processing. All of these factors are predicted to increase the use of intelligent document processing in the North American region.

Asia-Pacific is anticipated to exhibit a CAGR of 36.0% over the forecast period. The expansion can be ascribed to improved technology infrastructure and the presence of intelligent document processing solution vendors such as India's HCL Technologies Limited, Datamatics Global Services Limited, and Singapore's AntWorks. Countries in the region are progressively adopting new technologies, including artificial intelligence, machine learning, big data analytics, and cloud computing. These technologies are critical components of decision intelligence solutions, allowing firms to rapidly handle and analyze massive amounts of data. APAC's expanding technology use fuels the region's decision intelligence market rise.

The European market will likely develop modestly over the forecast period due to increased acceptance of IDP solutions in BFSI industries, particularly in the UK, Germany, and France. Businesses in this region rapidly embrace document processing solutions to assist underwriters and increase their involvement in the insurance ecosystem. IDP-led transformation allows commercial insurance underwriters to focus on orchestrating processes and generating value rather than simply extracting data. Government regulations and compliance standards stimulate the adoption of intelligent document processing systems to secure sensitive client data, leading to revenue growth in the industry.

The Middle East and Africa (MEA) market growth is primarily due to the region's growing embrace of digital technology to improve operations and services.

Segmental Analysis

The global intelligent document processing market is segmented based on components, deployment modes, technology, and end-users.

The market is further segmented by component into Solutions and Services.

The solutions category leads the market, accounting for over 68% of total revenues. This is due to the availability of several intelligent document processing systems and software packages that provide various functions, including quick processing and smart data classification. The solution component is the software or technology that offers the central capability for automating the extraction and processing of information from unstructured documents. Key players like Deloitte, KPMG, PwC Accenture, and other system integration providers want to combine all processes, applications, and software into a unified platform. Several system integrators have begun offering IDP services in response to the demand for AI-determined OCR or IDP solutions.

The service component of Intelligent Document Processing includes support, customization, and continuous assistance for companies during the implementation and use of IDP solutions. The services segment is expected to increase significantly in the future years. This can be explained by the availability of services provided by market participants. IDP services assist enterprises in satisfying these standards by automating data extraction, classification, and verification processes while maintaining data privacy and security. Service providers in intelligent document processing are driving market growth. For example, Canada-based Open Text Corporation provides training and professional services for its OpenText Intelligent Capture product.

Based on deployment mode, the market is fragmented into Cloud and on-premises.

The on-premises category is predicted to hold the largest market share during the forecast period. This can be linked to the increased security provided by on-premises deployment, particularly in industries such as healthcare and banking, financial services, and insurance (BFSI), which are expected to have stricter compliance requirements. A substantial offering of on-premises deployments, such as faster processing times and lower latency than cloud-based solutions, drives market growth. Organizations with significant document volumes or those requiring real-time processing benefit from on-premises deployment, providing faster document access and processing speeds.

Cloud deployment is the transmission of Intelligent Document Processing (IDP) solutions via the Internet to external servers operated by third-party cloud service providers. Cloud implementation benefits organizations by providing flexibility, scalability, and accessibility. In a cloud-based deployment architecture, IDP solutions are accessible via web browsers, allowing users to upload, process, and manage documents from any location with an internet connection. Cloud deployment reduces the need for enterprises to invest in and manage their infrastructure, resulting in a more cost-effective and agile option. Furthermore, cloud-based IDP solutions frequently come with automatic upgrades, ensuring users can access the most recent functionality and security improvements.

Based on technology, the market is fragmented into Natural Language Processing (NLP), Optical Character Recognition (OCR), Machine Learning (ML), Artificial Intelligence (AI), Robotic Process Automation (RPA), Google Vision, and Deep Learning (DL).

The machine learning category dominated the market, accounting for over 50% of worldwide revenue. This is due to the extensive use of ML approaches to automate the extraction and processing of information from various documents, including invoices, purchase orders, contracts, and forms, which has aided market growth. Machine learning is critical in accelerating the evolution of Intelligent Document Processing (IDP). Machine learning algorithms are trained on massive amounts of labeled data to extract information from documents accurately. These models learn from document patterns, context, and structures, which improves data extraction accuracy, reduces errors, and increases reliability. For example, ML models learn to differentiate between invoices, receipts, and contracts, allowing the IDP system to handle each document form correctly.

The natural language processing (NLP) segment is expected to expand significantly throughout the projection period. IDP depends heavily on NLP technology, which can understand and analyze human language. NLP also allows you to extract and analyze data from unstructured sources such as emails, reports, and publications. IDP tools like NLTK, SpaCy, and Stanford NLP drive market growth.

For example, MALTA, an Accenture NLP-based solution, automates the analysis and classification of textual data, making it easier for insurers to get critical information. Accenture claims that the technology provides a 30% more accurate classification than when the procedure is performed manually.

Optical Character Recognition (OCR)

This OCR technology aims at the conversion of different types of documents, such as paper documents that have been scanned, PDFs, or images, into searchable and editable data formats. This functionality is very useful in creating efficiencies of data entry methods, enhancing the efficiency of document management, and improving the whole automation of workflows. It utilizes sophisticated algorithms and machine learning techniques to identify, hence extract text from these various document formats, with the result of reduced manual entry of data, which in turn creates an avenue for reduced errors and increased speed in information processing.

ML technology takes a lead in the tag toward creating advancements in automated document management. Machine Learning makes an IDP system able to learn from the patterns in the data and develop itself over some time. This technology realizes more accurate data extraction, document classification, and comprehension of complicated documents. ML algorithms can handle unstructured data, recognize vital information therein, and adapt to different formats of documents, making them very valuable for organizations dealing with various and large volumes of documents.

AI-driven IDP solutions leverage advanced algorithms and models to understand, interpret, and process complex documents with high precision. This includes capabilities such as natural language processing (NLP) for extracting meaningful information from unstructured text, and computer vision for recognizing and analysing visual elements. AI technologies allow IDP systems to continually improve their accuracy and efficiency by learning from data patterns and user interactions.

The RPA technologies mimic human interaction with any digital system. Thus, with predefined tasks, a robot will execute the tasks of data extraction, validation, and entry without manual intervention. RPA is efficient in automating workflows, reducing possible errors in handling data manually within the context of an IDP. It is a technology that uses software robots or 'bots' to execute volumes of structured and routine tasks at higher accuracy and faster speeds than document processing.

Deep learning, a subset of artificial intelligence (AI), leverages complex neural networks to automatically analyse and interpret large volumes of data with high accuracy. In IDP, deep learning algorithms could be applicable for better document extraction, classification, and processing. It is highly adept at recognizing patterns and extracting insights from these unstructured data, such as scanned images or handwritten texts, through training on huge amounts of data to enhance its predictive capabilities. Deep learning integrated into IDP solutions enables better data extraction, lesser manual intervention, and the ability to process documents of a wide array of types and languages.

By End-users, the market can be further bifurcated into BFSI, Government, Healthcare and Life Sciences, Retail and E-Commerce, Manufacturing, Transportation and Logistics.

The BFSI segment had the greatest revenue share, accounting for more than 26%. This is due to the high volume of documents handled in the BFSI sector to supply products and services. The BFSI industry uses intelligent document processing technologies to improve operational efficiency, improve client experiences, and assure compliance. IDP uses bank statements, salary stubs, and tax returns to determine creditworthiness and eligibility. These advantages enhance the adoption of IDP technology in the BFSI industry.

The government category is expected to experience significant growth over the projection period. IDP technology enhances the efficiency and effectiveness of government and public sector activities. IDP helps companies speed up workflows, eliminate errors, and increase compliance by automating document processing and data extraction. For example, the US Department of Defense uses IDP to automate handling military contracts. This has helped the DoD save millions of dollars while reducing the danger of fraud.

Impact of covid-19

Intelligent document processing solutions help companies automate client delivery automation, classify incoming credit applications for bank loan approval, online verification of documents, and clause detection in legal documents.

The COVID-19 pandemic has hastened the use of intelligent document processing in businesses. Employees have limited or no access to tangible documents due to the lockdown. Thus, there is a raising need for digitalization and document automation, with intelligent document processing becoming a need by 2021.

Solution Segment to Grow Exponentially

The solution segment is projected to get the largest market share during the forecast period. Unstructured and semi-structured records can be converted into new records through intelligent document processing technology.

Intelligent document processing is a new type of technology that collects, retrieves, and analyzes information from a variety of file types automatically. It uses Artificial intelligence, including natural language processing (NLP), machine learning (ML), image processing, and deep learning to classify, categorize, and extract useful information and authenticate the information obtained.

Recent Developments

• February 2024- ABBYY Vantage, an artificial intelligence (AI) platform for intelligent document processing (IDP), saw a 60% rise in new annual recurring revenue (ARR) in 2023. Organizations wanted to use large language models (LLMs) for business applications. Thus, they needed ABBYY's purpose-built AI to transform their enterprise data and fuel models with relevant information.
• February 2024- Wipro and IBM expanded their partnership to offer clients new AI services and support.
• December 2023- Thomson Reuters will partner with WorkFusion to help organizations reduce risk and costs using AI.
• December 2023- EY and Appian will collaborate to provide AI process automation solutions and services for business transformation.