Best Practices for Using AI for Document Digitization

If your organization still manages lots of physical documents, you are not alone.  

Despite the growth of electronic documents and forms, many organizations are still overrun with paper documents.  As a result, staff waste lots of time manually keying data, shuffling paper, fixing errors, filing and retrieving documents, and responding to inquiries about the status of things.      

Artificial intelligence (AI) – technology that performs tasks that would typically require human intelligence – has the potential to transform the way that organizations manage documents.  

But achieving the full benefits of AI in document digitization requires organizations to take the right approach to implementation.  Read on to learn best practices for using AI for document digitization. 

What is AI-powered document digitization? 

AI technologies such as machine learning (ML) and Natural Language Processing (NLP) make it easy for organizations to digitize physical documents.  Here’s how the process typically works:

• Capture.  The first step of the document digitization process is to use a high-speed scanner or other device to convert paper files and other physical documents into a digital format.
• Pre-processing.  Before AI algorithms are applied, captured images are enhanced with denoising, de-skewing, contrast adjustment and other pre-processing steps to ensure quality.
• Extraction.  Document digitization solutions use AI, optical character recognition (OCR) and other technologies to convert text within images to machine-readable format.
• Classification.  From applications and contracts to sales orders and correspondence, the documents that organizations receive must be categorized before they can be processed, to ensure that the right business rules are applied to how each document is handled.
• Data extraction.  Next, pattern matching, NLP and other techniques are used to identify and extract names, dates, addresses, amounts, and other pieces of information from documents. 
• Validation.  To identify errors or discrepancies, AI algorithms compare extracted data against pre-set business rules or information residing in a system of record or reference database. 
• Understanding.  For documents containing free-form text or other unstructured data, NLP algorithms are used to extract insights, sentiments, or themes from textual content. 
• Integration.  Extracted data is integrated into document management systems, centralized cloud repositories, databases, or downstream workflows for storage, retrieval, and analysis.  
• Learning.  Corrections and updates are fed back into the AI training models so the solution can deliver better results over time and adapt to new document types, languages, and formats. 

These capabilities transform the way organizations manage, analyze, and retrieve their documents. 

Why use AI-powered document digitization solutions?

AI-powered document digitization solutions deliver significant benefits.  

1. Efficiency.  AI improves efficiency and frees up staff time by eliminating the paper sorting and other manual tasks associated with converting physical documents into a digital format.
2. Accuracy.  Errors are inevitable whenever humans are involved.  AI-powered document digitization solutions reduce the risk of typos and transposed numbers by using NLP to accurately extract information from documents, even handwritten or poorly scanned ones.
3. Savings.  Digitizing documents with AI eliminates the expense of paper and paper-related consumables such as printer toner.  And archiving documents in a centralized cloud-based repository means organizations can avoid the expense of filing cabinets and off-site storage.
4. Accessibility.  In a digital environment, authorized users can instantly access documents at any time, from any location, using any device.  Keyword searches make it easy to find documents even with large document repositories.  And remote teams can collaborate on digital documents, while ensuring version control and tracking of all actions taken.
5. Control.  AI-powered document digitization solutions safeguard sensitive data and ensure privacy with built-in controls such as user access permissions, segregation of duties, systematic workflows, logging of all actions taken on a document, and document retention.
6. Scalability.  The efficiencies provided by AI enable organizations to scale their operations without the need to hire additional staff.  And the technology can easily scale to handle large volumes of documents and adapt to new documents, varying workloads, and requirements.
7. Insights.  AI analyzes vast amounts of data to provide insights into trends, patterns, and customer preferences.  The insights gleaned through AI can also inform predictive analytics. 

These are some of the reasons that organizations are deploying AI for document digitization.   

What are best practices for using AI for document digitization? 

Achieving the full benefits of AI for document digitization requires the right approach to deploying and using the technology.  Here are 10 proven best practices for AI document digitization.  

1. Prioritize pre-processing.  High quality images are critical to the success of a document digitization project.  Deploying a best-in-class high-production scanner is essential.  But be sure to find an AI solution that can enhance images, reduce noise, and normalize resolution.
2. Choose the right AI models.  Whether you are trying to extract text or detect elements within a document, AI models are better suited to different business applications.  Consider each model’s accuracy, speed, and compatibility with your document types and languages.
3. Don’t skimp on training.  The more data that an AI solution sees, the better its ability to handle a wide range of document effectively.  That’s why it’s critical to train AI models using diverse datasets that encompass your various document types, formats, languages, and styles.
4. Fine-tune AI models to your needs.  Pre-trained AI models deliver impressive results out-of-the-box.  But you can improve the performance of the technology by fine-tuning AI models or developing custom models for your unique document digitization requirements.
5. Validate the accuracy of AI.  Improve performance over time by incorporating human review, feedback loops, and other quality assurance processes in your document digitization lifecycle to regularly evaluate and validate AI-generated output and refine AI models.
6. Think long-term when evaluating AI solutions.  AI-powered document digitization solutions are foundational, not throwaway technology.  Determine whether prospective AI document digitization solutions can scale and adapt to meet your anticipated future needs.
7. Be sure an AI solution can integrate with your legacy systems.  AI can aggregate and analyze vast amounts of data from diverse systems and databases.  But that can only happen through seamless integration.  Ensure that prospective solutions can connect with your organization’s legacy document management platform, customer relationship management (CRM) application enterprise resource planning (ERP) platform, and other legacy systems.
8. Protect your mission-critical content.  A lot of sensitive documents and data can flow through an organization’s digitization process.  Safeguard privacy and confidentiality by deploying an AI-powered document digitization solution that enforces access controls, encrypts data, and adheres to your organization’s regulatory compliance guidelines.
9. Train your staff.  Poor user adoption can undermine an otherwise solid business case for AI-powered document digitization.  Ease your staff’s transition to AI by developing a plan to train users on how to use the system effectively, interpret results, and address any issues.
10. Strive for continuous improvement.  Achieve better results from AI over time by collecting user feedback, tracking performance metrics, and iterating on models and workflows.

These best practices will help organizations harness the power of AI for document digitization. 

Conclusion 

Physical documents make it hard for organizations to manage and leverage mission-critical content.  AI-powered document digitization solutions enable organizations to unlock new levels of efficiency, accuracy, and security, positioning themselves for success in the digital transformation era.  The best practices above will help organizations achieve optimum results from AI document digitization.