The rise of AI: Signifying the end of manual data entry

Why is it that your employees are still manually typing data from documents? Is it because you feel automation would be too expensive? Or are you unsure what options are even available? Fact is, there are solutions available today that are based on Artificial Intelligence (AI) that can automate various business processes of your company.

We will look at these three topics in this article:

  • Manual data entry and its obstacles.
  • Artificial Intelligence and how the technology helps for data entry.
  • AI-based Intelligent Document Processing to automate document processing.

I) The problems with manual data entry

Virtually every one of your business processes involves documents from which you need to extract data to move the workflow forward. The reasons why you haven’t already automated the data entry process probably vary. On the one hand, outdated systems may be preventing you from implementing it smoothly. On the other hand, it may be the complex documents with variability that you need to process and have not yet found a suitable solution for.

Whatever the reason is that your employees are typing in the data manually, one thing is certain: in the digital age, it is increasingly creating difficulties for your company:

Slow processes mean higher costs: Manual data processing is enormously time-consuming and labor-intensive. Once your employees have to manually type data into systems like ERPs, it makes processes inefficient. Every interaction in the processing of documents means slow processes. This, in turn, means additional costs for your company, often in the form of additional employees.

Error-proneness: One of the main concerns for you is that the data in your systems is accurate. However, manual data entry is inherently prone to errors. Even small typos can have a big impact on payments and matching invoices to purchase orders, for example. Furthermore, inaccurate or incomplete information can have a direct impact on your decision making.

Data security and compliance: Data security and compliance requirements are becoming increasingly complex. You cannot afford to be non-compliant and for data to get to the wrong people. However, with manual document processing, there is always a risk that employees will share data with the wrong people.

Unhappiness among highly skilled employees: Repetitive work like typing data is tedious and demotivates your employees. You don’t want to use your skilled (and expensive) employees for such tasks. Neither from your company’s perspective, nor from your employees’ perspective.

Vendor relationships are affected: You have a responsibility to your vendors. If your payments are delayed due to manual data processing, it can quickly affect customer relationships. In the worst case, late payments result in warnings or even contract cancellations.

II) How artificial intelligence is revolutionizing the work environment


Artificial intelligence (AI) as a technology is becoming more advanced and its use is becoming more widespread in the commercial sector as a result. Across a wide range of industries, new opportunities are opening up for use cases that are either not possible or too expensive with humans. Let’s take a look at some of the use cases that use AI.

A) AI for automation

The following are two possible use cases of AI for automation:

Chatbots: Chatbots are mainly used for customer service. With the help of AI, chatbots are created that can handle customer inquiries or complaints or route them to the right employee. This allows customer service representatives to deal with customer inquiries that are more complex. At the same time, chatbots using AI can work through customer inquiries. This serves the human customer service agent to be more knowledgeable.

Medical imaging: In medical imaging, AI is increasingly used to detect diseases faster, which promotes early intervention. Specifically, in cancer screening, AI is a tremendous help to pathologists. For example, researchers have found that when analyzing tissue scans, colorectal cancer can be better detected and diagnosed with the help of AI.

B) AI in decision making support

The following are two of the possible use cases of AI in supporting decision making:

Predictive Analysis: One of the most common methods of AI in helping decision making is predictive analysis. In this method, machine learning algorithms are used to analyze huge data sets. These are then in turn used to make predictions about future events. This could be predictive sales data for a product, for example.

Loan analytics: It is essential for banks to make the most data-driven decisions possible when granting loans. AI-based credit scoring tools consider applicants based on their financial profile, credit history, and account behavior. With the help of AI, decision makers in banks can make better decisions when analyzing loans.

C) AI in support of problem solving

The following is one of the possible use cases of AI in supporting problem solving:

Pattern recognition and anomaly identification: Document fraud is a serious problem for banks and financial institutions. AI is now massively assisting humans in solving this problem. Machine learning algorithms are trained on big data to recognize patterns in financial data, for example, and detect fraudulent transactions. The AI detects the smallest indications of fraud and flags this for investigators so they can take specific action.

III) AI-based IDP for document process automation

A key part in the digital transformation of your organization has to do with the intelligent automation of document processing. According to Gartner, well over 80% of all data is unstructured, which has made it difficult to automate these documents until recently. In the last few years and with the rapid advancement of artificial intelligence, new solutions have been realized: One of them is Intelligent Document Processing (IDP), which brings business efficiency to your document processes.

A) The solution to eliminate manual data entry: Intelligent Document Processing

Intelligent Document Processing uses artificial intelligence: Intelligent Document Processing (IDP) or AI OCR uses advanced artificial intelligence technologies to automate data entry, among other things. This powerful solution combines AI technologies such as Machine Learning, Deep Learning, Natural Language Processing, Computer Vision and Optical Character Recognition (OCR) into one. Not only does this combination realize the capture and extraction of unstructured data entirely without templates, but the system is also constantly learning thanks to AI. In addition, with IDP, your company taps into new data sources and automates end-to-end.

The AI-based Parashift IDP platform is continuously improving: The core technology of the Parashift IDP platform is Document Swarm Learning. Through this unique approach, learning from all documents, customers, and industries is shared at the field level – in a fully GDPR-compliant manner, of course. AI-based continuous learning exponentially accelerates the accuracy and efficiency of the IDP solution. This ensures maximum efficiency for your organization.

Parashift IDP is industry-agnostic and suitable for all use cases:Parashift’s AI-based IDP solution is industry-agnostic and exclusively takes care of these four functionalities: document separation, classification, extraction, and validation. This approach enables a best-of-breed solution for your document processing. This means that you can use only one solution for Intelligent Document Processing and ditch expensive vertical solutions. In addition, the versatility of use cases, ultra-short setup time, and rapid scalability speak for the AI-based platform. 

B) Companies that are already using Parashift successfully

How an ocean freight payer uses Parashift to reclaim ocean freight charges: Transportation and logistics companies still struggle to speed up the process when it comes to document processing. Documents are semi-structured or completely unstructured, which means they are only partially verified automatically. This leads to an increase in delivery costs. By using Parashift’s IDP solution for processing invoices, audits and ocean freight charges, carriers can significantly minimize these errors. The platform automatically extracts the right information and structures it. Understanding of the information is improved and communication between the different people dealing with the documents is fluid.

Please find the case study here.

How an insurer uses Parashift to streamline its data processing and save time and money: Medica’s nonprofit health plan plays an important role in the U.S. by providing access to care for millions of people. However, triggered by manual processing procedures, the workflow was lagging, which was impacting customer satisfaction. Therefore, automating the document extraction process was an important decision. This now guarantees a reliable and cost-friendly process, benefiting both the customers and the insurer itself.

Please find the case study here.

How an electricity and water department uses Parashift to improve customer experience and reduce costs: When reviewing its existing workflows, the electricity and water department of the city of Buchs (EWB) found that using different systems and interfaces to manage customer information resulted in complex and time-consuming workflows. To improve workflows and provide the best possible customer experience, EWB decided to deploy Parashift IDP. By streamlining internal processes, it can guarantee a high-quality service while reducing costs.

Please find the case study here.

Parashift’s technology enables organizations to reduce their document processing costs by more than 80% while solving new, previously “impossible” use cases easily and quickly. And all this without replacing existing business applications. – Fabian Seimer, Head Information Management, Inacta AG

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