For enterprise organizations, now is the time to embrace document data extraction and intelligent document processing. 

Despite the recent progress made in the move to digital, companies continue to extract data from business documents using manual methods. In fact, paper processes within organizations are only increasing as the scale of data being handled increases. 

Here are some statistics to illustrate that AI document extraction is still the exception rather than the norm. 

The Unsettling Truth About Relying on Paperwork Rather Than on Document Data Extraction 

These are statistics from the Archive Corporation that showcase the negative impact of relying on paperwork rather than on automated data extraction for regular work processes. 

The inconvenience of handling paper documents results in a productivity loss of 21.3% annually. 

On average, employees spend around $120 worth of time daily searching for documents. 

Knowledge workers spend about 30% of their day, or 2.5 hours, searching for information. 

25% of all poorly filed documents are never located and over 7.5% of all records get lost. 

Businesses in the United States alone waste $8 billion annually on managing paper documents. 

8 in 10 workers have recreated documents that already existed due to difficulties in locating them. 

As is amply clear, paper-based processes, being inefficient, lead to significant productivity losses. Embracing intelligent data extraction from documents is therefore imperative. 

What Types of Paper-Based Documents Do Companies Work With? 

There is much scope for using intelligent document processing as many companies still work with paper-based documents, either in the form of physical paper or as PDF files and images from which data is hard to extract and use. Transformation is needed across industries as legacy data entry methods are still widely used to transcribe data fields. 

Invoices: Invoice date, invoice number, vendor name, amount due, payment terms 

Financial statements: Revenue, expenses, assets, liabilities, net income 

Purchase orders: Order number, supplier name, order date, items requested, delivery date 

Shipping and receiving documents: Shipment number, shipping carrier, weight, consignment destination 

Employee records: Employee name, job title, salary, benefits, performance evaluations 

Customer information: Customer name, contact information, purchase history, demographics 

Contracts: Contract number, date, parties involved, terms, expiration date 

Insurance claims: Claimant information, diagnosis, treatment, billed amount, payment status 

Medical records: Patient information, diagnosis, treatment, medications, lab results 

Marketing materials: Campaign name, target audience, budget, performance metrics 

Product catalogs: Product name, SKU, price, inventory level, product descriptions 

Sales data: Sales figures, revenue, customer demographics, marketing ROI 

Inventory data: Product name, SKU, current inventory level, reorder point, lead time 

Email correspondence: Sender, recipient, subject, message content, attachments 

Supply chain data: Supplier name, delivery schedules, purchase order history, inventory levels. 

Logistics data: Carrier, shipment tracking, delivery times, freight costs 

Paper-based methods still rule the roost, but they are inefficient and lead to significant productivity losses and cost escalations. Let’s take a close look at why using paper-based processes at work rather than intelligent document processing solutions has many disadvantages. 

What are the Disadvantages of Using Paper-Based Processes Rather Than Intelligent Document Data Extraction at Work? 

Manual data entry typically is of two types. The first one involves employees transcribing information from actual paper documents or from images. The second type involves the copying and pasting of information from one document to the next. The second type is only marginally more efficient than the first. However, even such data extraction from documents does not match the efficiency of automated data extraction. 

Broadly, these are the major disadvantages of using manual, paper-based processes at work. 

  • These methods are expensive because they entail significant costs associated with procuring and using human labor. 
  • These methods are hard to scale and make it impossible to handle increasing document volumes efficiently. 
  • These methods lead to subjective and inconsistent output due to reliance on human operators as operators are likely to have differing judgments and interpretations when doing document information extraction. 
  • These methods inevitably lead to reduced productivity and employee morale as they are repetitive and monotonous. In the worst cases, burnout and fatigue can result, leading to reduced accuracy. 
  • These methods rely heavily on experienced operators with domain-specific knowledge who are context-aware and able to extract relevant data accurately. Sourcing and retaining such talent can be a challenge, particularly in niche industries or for specialized document types. 

As evident, paper-based and manual methods aren’t the most efficient way for document information extraction. These methods’ limitations can be overcome by using intelligent document capture methods powered by many technologies including Artificial Intelligence (AI) and Robotic Process Automation (RPA). 

What Benefits Does Document Data Extraction Offer Over Manual Paper Processes? 

Speed and efficiency are two of the most obvious benefits that using document data extraction powered by cutting-edge technologies provides over manual processes. Let’s take a closer look at these benefits. 

  • Amplified efficiency: RPA bots infused with AI can process documents faster than humans and ensure faster turnaround times and better operational efficiency.  
  • Increased accuracy: Intelligent document extraction reduces errors associated with manual data extraction and entry as it has a high degree of accuracy.  
  • Cost savings: RPA document extraction helps organizations achieve significant cost savings through improved resource use and reduced labor costs.  
  • Scalability: Unlike manual data entry methods, intelligent solutions for document extraction are virtually infinitely scalable and can handle massive document volumes without necessitating additional manpower. This allows organizations to experience seamless business growth.  
  • Better compliance: Intelligent capture and document processing maintain consistent and accurate data extraction procedures, thus ensuring regulatory compliance.  
  • Increased productivity: Intelligent capture and document data extraction enable your team to focus on higher-value tasks, thus increasing productivity over time. 
Document Data Extraction

Data extraction using automation and artificial intelligence is clearly the preferable alternative to manual, paper-dependent methods. 

Document Data Extraction is the Right Alternative to Paperwork 

Without a doubt, the future of document extraction lies with intelligent document processing solutions that offer automated document data extraction. There is a definite move toward digital transformation ongoing in the business world and document data extraction promises to be a strategic lever of growth. 

The fact that AI document extraction handles unstructured data extraction as skillfully as structured document data extraction is a definite boon that cannot be overlooked and which empowers employees to focus on higher-order strategic initiatives rather than getting bogged down with repetitive tasks. 

The ultimate power of automated document data extraction is that it doesn’t merely extract information quickly and accurately at scale but also makes the extracted information actionable. 

The growth in paper processes because of the increasing amounts of data being handled globally is a real and present challenge and document data extraction offers the solution. 

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