Revolutionizing Document Management: How AI Transforms Industry Processes

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Artificial Intelligence (AI) is revolutionizing industry processes, from supply chain management to retail to finance, claim processing, and customer support. Over 35% of global companies utilize some type of AI, which will only increase. Why will AI use increase? Partly because of the improvements AI brings to intelligent automation-based solutions.

AI speeds up and smooths time-consuming processes like document analysis (zero-based learning) and information retrieval (quickly search your ‘dark’ unstructured content). The benefits are apparent when AI tools are part of your document capture platform toolset. This article will examine how AI-powered document analysis in Intelligent Document Processing (IDP) products can radically benefit any organization. 

Document Analysis

AI-powered document analysis can extract useful content information by interpreting text, images, and handwriting. As subsets of AI, Natural Language Processing (NLP), Machine Learning (ML), and Computer Vision can categorize, summarize, and extract critical data points, classify data extraction and retrieval, and manage and utilize large amounts of information. However, to realize these advantages, a careful process must be followed, includuing:

1. Document Uploading and Pre-Processing: When different file formats are uploaded into the system, they undergo pre-processing. During pre-processing, documents are cleaned, formatted, and converted digitally, making them suitable for analysis.
   

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2. Document Classification and Information Extraction: Document classification involves categorizing documents into predefined classes or categories based on their content or structure. Information extraction algorithms then identify and extract critical data points or entities from the documents. These could be names, dates, amounts, or specific terms.
3. Data Validation and Error Handling: Once the information is extracted, it undergoes data validation and error-handling processes. This step involves verifying extracted data against predefined rules or criteria to address any inconsistencies or errors that may arise.
4. Visualization and Presentation: The analyzed and validated data is visualized in a user-friendly format to facilitate interpretation and decision-making. Examples are charts, graphs, or reports.

AI’s Impact: Compliance, Market Analysis, and Beyond

By delivering consistent results and detecting errors, AI significantly accelerates document processing. The results of AI can be found in optimizing resource utilization and allowing organizations to quickly handle large document volumes accurately. In addition, AI provides timely and accurate information by extracting valuable insights from documents and automating tasks such as trend identification. The data extraction also speeds up decision-making, reduces errors, and allows organizations to predict market shifts and navigate upcoming regulatory requirements.

In the service of compliance, AI identifies potential risks or deviations from regulations, allowing for proactive remediation. By reducing labor-intensive compliance checks, AI streamlines the processes and helps to mitigate the penalty risk.

As the volume of documents increases, AI resources can be allocated to accommodate the workload, allowing organizations to scale their document management processes in line with business growth or fluctuations in demand. AI also minimizes labor costs by automating data entry, classification, and extraction. AI systems process documents quickly, saving human resources, time, and potential errors.

AI: An Industry Agnostic Tool?

AI can analyze passports, driver’s licenses, and utility bills to verify identities by extracting and cross-referencing critical information, such as names, addresses, and identification numbers. This process can also accelerate customer onboarding and enhance security for better fraud prevention measures.

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AI enhances patient care and operational efficiency in healthcare by extracting and analyzing data from medical records to identify patterns and trends that assist in early disease detection and personalized treatment plans. In addition, AI has been shown to streamline administrative tasks and reduce errors and processing time by automating various aspects of insurance claims and patient billing. It also helps with HIPAA compliance by automatically identifying and flagging inconsistencies in documentation.

With governmental agencies, AI helps manage unstructured data, emails, and files by automating the processing of large volumes of paperwork, such as tax filings, grant applications, and public records. This process significantly reduces time-consuming tasks and processing time by streamlining documents such as visa and citizenship applications. Moreover, it aids in policy analysis by extracting relevant information from legislative documents, reports, and public feedback.

In real estate, AI streamlines the management of property listings and transactions by automatically organizing data from contracts and financial statements. It can analyze and verify large volumes of documents related to property history, zoning regulations, and compliance records. In addition, AI can identify market trends and provide insights by analyzing reports and economic data.

An open question is the effectiveness and underlying cost of AI large language models (LLMs) versus the more traditional and time-tested ML processing. For example, Does AI provide document classification with acceptable field-level recognition accuracy at scale?

Selecting The Right AI Tool For The Job

Similarly, a carpenter has multiple saws to select from when cutting; businesses have different IDP  document process tools with AI options to optimize efficiencies. The following are some of the best IDP  tools leveraging AI that automation  solution providers should consider for their tool belt:

1. Hyland IDP

Hyland’s Intelligent Document Processing (IDP) leverages ML, NLP, and Optical Character Recognition (OCR) to process unstructured and semi-structured data. These tools reduce manual data entry, which minimizes errors and speeds up document processing. Its key features include intelligent document capture, automated data extraction and validation, and integration with existing business processes and enterprise systems. The platform is scalable and uses a low-code environment, making it adaptable and easy to deploy across different industries.

2. IBM Watson Discovery with IBM Datacap

IBM Watson Discovery with IBM Datacap extracts insights from large volumes of unstructured data by automating business document ingestion, enrichment, and querying. This tool is particularly effective in enabling faster and more accurate decision-making processes. For example, the legal sector can rapidly analyze documents to provide relevant case information, improving efficiency and client service.

3. ABBYY FlexiCapture and ABBYY Vantage

ABBYY FlexiCapture and ABBYY Vantage combine OCR, Intelligent Character Recognition (ICR), and ML to extract and validate structured and unstructured data. The platform supports real-time capture, advanced classification, and automatic learning to enable quick deployment and continuous improvement through user interactions. It also integrates with applications like Robotic Process Automation (RPA) and Business Process Management (BPM) systems. It operates on a cloud-based infrastructure for scalability and security hosted on Microsoft Azure. ABBYY brings comprehensive visibility into Enterprise Content Management (ECM) with features like agentless monitoring, pre-built tests, automated resolution, and real-time user activity data. These features allow for easy deployment without interfering with existing systems. No coding is needed, and automated resolution capabilities help to address problems quickly.

4. OpenText Intelligent Capture

Capture solutions automate scanning and capture and produce metadata with an image file and OCR text. This information is migrated to an ECM system, allowing a search of the assets based on the metadata and viewing it using document imaging software. OpenText Intelligent Capture uses machine learning and AI to automate manual content processes, such as accounts payable, back-file conversion, and onboarding, and transform paper and digital content into actionable data. This enables organizations to securely and efficiently route information to the right users and systems, ensuring accurate information is available when and where it is needed.

5. Tungsten TotalAgility

Tungsten TotalAgility is a unified intelligent automation (IA) platform that harnesses advanced artificial intelligence (AI) to automate content-intensive workflows and unlock real-time business insights. TotalAgility streamlines business processes across various industries by blending intelligent automation with generative AI. The solution simplifies workflows from customer onboarding and loan processing to claims administration, inventory management, etc.

AI Is Not A Panacea

While AI offers enormous promise for IDP-based solutions, a sound business problem evaluation covering benefits, governance, value measurement, and ROI should be part of the AI solution analysis. AI is another tool in the tool belt but is not the ‘answer’ to all document processing challenges.

AI is advancing as a standard component of any organization’s operations—a paradigm shift in innovative business technology. Businesses that use AI to simplify information management and workflow will realize significant optimization advantages. However, regardless of the IDP solution provider, companies must have a document observability platform to mitigate downtime and understand processing and outages. A proper IDP observability solution will empower IT personnel with a single-pane-of-glass view into document flow to ensure AI has the information to digest (a.k.a training) to produce the abovementioned benefits. You would not drive a car without a steady flow of data displayed in a user-friendly format; why attempt to deliver AI-powered document management solutions without the same type of visibility?

About the author: Brian DeWyer is the CTO and Co-Founder of Reveille Software. With more than 25 years of experience in technology, Brian DeWyer provides product strategy and technical leadership in his role as Reveille CTO and board member. Brian leverages his extensive knowledge as a senior IT leader at a large financial services company and previous role as a process consulting practice leader for IBM Services delivering on-premises and cloud-based solution implementations for Fortune 1000 commercial and government clients. He has led process change efforts within large organizations, building on content-driven solutions for high-volume transaction processing applications. He is a past board member of the Association of Image and Information Management (AIIM) industry association. Brian graduated from Virginia Tech with a BSME and holds an MBA from Wake Forest University.

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