Intelligent Document Processing

Boosting Productivity: Document AI for Knowledge Management

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Boosting Productivity: Document AI for Knowledge Management

Knowledge management refers to the careful documentation, sharing, storage, and management of your company’s intricate processes, also referred to as organizational knowledge. The scope of knowledge management encompasses multiple company resources, like customer communications protocols, employee handbooks, intellectual property, and manuals, among many others. 

Companies often struggle with knowledge management as all the essential information is spread out over different devices, including stand-alone PDFs, physical documents, emails, and web content. 

This is where document AI for knowledge management uses technologies like ML and NLP to compile, classify, and categorize these documents for easier accessibility for employees and customers alike. 

Keep reading to learn how AI addresses the challenges of traditional knowledge management systems and how you can implement intelligent content organization.

Challenges in organizing and accessing knowledge in traditional systems

Traditional knowledge management systems are ill-equipped to handle the scale at which companies are processing information. Their rigid framework lacks essential features such as:

1. Modifiability

The documentation of different business processes and their implementation exponentially increases as the company scales. Consequently, the documents require constant changes and content additions throughout their life cycles. Traditional knowledge systems, fueled by paper-based documentation, Word documents, and PDFs, need to be manually edited to reflect these changes. 

2. Searchability 

Since knowledge is often distributed and recorded over different channels, it reduces its searchability. In addition, traditional knowledge management systems lack advanced search features like keyword search and filters, without which search functionalities may give outdated or irrelevant information.

3. Contextual relations

Traditional knowledge management systems have fixed categorization schemes and fail to capture any contextual relationships between two or more similar documents. It is challenging for the employees to understand the significance of the recorded business processes. Moreover, as the business grows, adding new categories and modifying the existing ones becomes time-consuming. 

4. Accessibility 

The lack of accessibility is most apparent for remote-first companies, as knowledge stored across different locations and devices becomes difficult to access. In the long run, it impedes the onboarding process and affects overall productivity. 

Benefits of using document AI for enhanced productivity in knowledge management

Integrating document AI offers several advantages, starting with eliminating inefficient documentation processes, which make the knowledge management system more intelligent.

1. Better automation

Intelligent document processing (IDP) software automates manual processes like categorization and tagging entries. 

Categorization

IDP platforms use Natural Language Processing (NLP) algorithms and machine learning (ML) to make the categorization process more adaptive. The AI learns from user interaction and feedback and continues to increase its categorization accuracy. This adaptability enables the systems to refine their categorization algorithm.

To help move things faster, advanced document AI for knowledge management like Docsumo uses pre-trained smart APIs to automatically categorize documents and help the company skip training the ML algorithm. 

Tagging

With the sophisticated data extraction process, document AI automatically captures metadata, such as keywords, document purpose, document type, date, and author. Then, it leverages these attributes to tag the documents and increase their searchability. 

2. Increase in business intelligence 

IDP software is capable of processing large volumes of data and quickly identifying patterns in them. But what does this mean for knowledge base management and business intelligence?

Better document connections

As the IDP software processes more documents, the ML system gets to analyze more data and refine its algorithm. 

This means the knowledge management software establishes better connections and relationships between different documents. These document associations help the employees get a holistic view of a business process or subject they are learning about. 

Improved contextual understanding

Document connections are not based on simple keyword matching. Rather, the NLP understands the documents’ semantics and context before linking them together. In simpler terms, it recognizes key concepts within the recorded documents and groups those with similar content. Consequently, the system helps with better document organization based on relevance and increases overall business intelligence. 

How document AI enables advanced search and enhances collaboration

The advanced search features offered by the addition of NLP and ML are far superior to regular keyword searches. Let’s have a closer look at how it works. 

1. Leveraging NLP for semantic search and recommendations 

AI-enhanced document processing systems improve knowledge discovery. NLP’s semantic search features go beyond generic keyword searches. Semantic searches consider the intent and content of employee queries to provide more accurate and contextually relevant results. 

Furthermore, employees can search for documents using concepts rather than precise keywords with intelligent content organization. NLP’s entity recognition feature allows the user to search for blogs, guides, and business processes, based on people, departments, dates, and locations. 

As the employees continue interacting with the knowledge base dashboard, they’ll start getting recommendations based on their individual preferences. 

For example, a financial data analyst who wants to stay updated about the latest accounting standards will automatically get recommendations about the same in their knowledge portal. 

2. Improves collaborations 

Over the document AI/IDP software’s central dashboard, multiple users can collaborate on documents, edit them, and provide inputs in real time. Cross departmental employees can work together (think of product managers, marketing teams, and sales teams) to create a comprehensive knowledge base for the entity. 

Knowledge base admission manages access controls and permissions to ensure no unauthorized changes are made. They also have the ability to track document versions, annotations, and comments. In other words, the organization can allot admins to preserve the transparency of these documents. 

Lastly, the software automatically sends alerts and notifications to the relevant personnel whenever the document is updated or new categories are created. 

A step-wise guide to implementing document AI for knowledge management

It is important to have a step-by-step implementation plan to get the most out of your software. Here’s how. 

1. Define the problem

First, define the problem with your current knowledge management system, along with its scale. 

This includes highlighting: 

  • The type of documents to be processed, 
  • The total number of documents, 
  • The data type that needs to be extracted, and 
  • The current categorization accuracy. 

For knowledge base management, the number of documents to be processed will be higher in the initial implementation phase and will then gradually subside as employees get accustomed to the system. 

Most of the documents meant for the central hub will either be HTML pages, PDFs, videos, emails, or images. Intelligent document processing for knowledge base management has smart extraction processes to deal with the complexity and variety of these structured, semi-structured, and unstructured documents.

2. Automated document organization/Intelligent content organization 

If you are revamping the organization’s knowledge management system, look for the intelligent content organization feature in the document AI. This feature automatically assigns categories, classes, tags, and labels based on the extracted metadata. 

Eventually, it helps with document storage, analysis, management, and retrieval. In automatic classification, the documents are instantly routed to the relevant departments or document categories after they are scanned. Documents with missing pages are automatically flagged for manual verification. 

3. Required metrics

Straight-through-processing (STP) and accuracy rate are two critical metrics in the document AI platform. A high STP rate ensures the majority of the documents are processed and organized without any human intervention. And, for knowledge base management, the accuracy rate is directly related to the STP rate.

4. Need for customization

Most automated document processing platforms will offer generic solutions for their users. But some organizations might need more specific and custom solutions. 

For example, the format of healthcare guides and workflow documentation might be different at other companies. 

In such cases, the IDP platform will train the ML program to learn the format and data extraction processes required for your documents. Advanced platforms allow organizations to train the algorithm for security purposes. 

5. Cost and ROI of the project 

Lastly, you need to weigh the benefits and features against the cost of the project. The cost varies depending on various factors, like the level of automation required, data extraction complexity, licensing, and support and maintenance costs. 

While considering the overall ROI, the business should also consider other expenses, such as initial setup costs, ongoing costs, and maintenance costs. Similarly, the ROI should be positive and result in any of the following: increased productivity, improved customer satisfaction, better decision-making, reduced document processing times and cost, and/or a high STP rate. 

Data security and privacy concerns

The automated document AI system should comply with data security and privacy regulations. Docsumo adheres to: 

1. GDPR

GDPR is currently the European Union’s most stringent privacy law. Docsumo acts as the data controller per the norms of this security. In other words, third-party applications cannot access the data without proper authorization. 

2. SOC-2

Next, the SOC-2 certification establishes that the knowledge capture methodologies and data extraction techniques are thoroughly audited and comply with the principles of availability, integrity, confidentiality, privacy, and security. 

3. HIPAA

HIPAA compliance is essential for healthcare companies. These regulations protect the patient's private information that is stored in the central knowledge management system. 

4. SSL

In addition, SSL encryption ensures the security of the login system. In other words, the encryption system prevents cybercriminals from using malware, malicious scripts, and brute-force hacking to access classified information. 

Integration with existing systems

Integration with third-party systems and business sources allows for the real-time flow of information across different software without manual input. 

Docsumo’s native integration with Zapier, Google Sheets, and other tools gives streamlined workflows with reduced errors. 

Conclusion 

Companies use Docsumo’s ML algorithms and document processing to revolutionize legacy knowledge management systems. 

  • Docsumo has a high STP rate of 95%, which means faster document processing times with less human intervention. 
  • The document processing and data extraction time can go as low as 30-60 seconds, making the process extremely efficient. 
  • 99% data processing accuracy improves the turnaround time. 

Sign up for Docsumo’s 14-day free trial to create your organization’s knowledge base. 

Suggested Case Study
Automating Portfolio Management for Westland Real Estate Group
The portfolio includes 14,000 units across all divisions across Los Angeles County, Orange County, and Inland Empire.
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Pankaj Tripathi
Written by
Pankaj Tripathi

Helping enterprises capture data for analytics and decisioning

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