How to automate document intensive workflow.


Document intensive workflow automation

Every business deals with documents and these documents arrive to the business from different sources such as email, external websites, printouts or scanned copy, downloaded from internal systems etc. If the number of documents is less, it’s ok to manage them manually, however when the volume increases, it becomes difficult for workers to manage them manually and automation becomes mandatory. 

To automate document intensive workflow, one needs to use document AI or Intelligent document processing platform along with some other technologies such as RPA, API etc.

Document processing workflow

Generally, a document intensive workflow contains 4 or more steps as described below:

Document Sourcing: Documents may need to source from inboxes, folders, other applications or websites. It is important to automate the document sourcing process for end-to-end automation. Different integration tools such as RPA or API etc. can be used to collect these documents from different sources. There are some of the document processing AI platforms such as Doc Dog, Abbyy etc. that come up with the amazing integration capabilities, using which one can automate the document sourcing easily and efficiently.  

Data extraction:  To extract data from any documents, we need either OCR or a document AI model that combines with OCR and machine learning algorithms. Document AI is an amazing technology that not only blindly captures data but also it understands the context of the document and content that it is capturing. For example, it understands address, vendor information, line items, tax information etc. when capturing these data from an invoice. However OCR simply captures data blindly word by word or line by line without knowing what it is extracting. Developers need to process the extracted data separately and put some logic on the extracted data returned by OCR. Based on the document type, one should choose the technology. 

Data cleaning, formatting and validation: This is the post processing that sometimes is needed for the automation. The ultimate goal is to take the extracted data to one or multiple systems for further processing. However the targeted system may not support the data format within the actual documents. Also all the extracted data may not be needed in the workflow. To maintain the data integrity, automation experts clean up and transform the data before they go to the targeted system for further processing. Some document AI platforms provide features for this data transformation, clean up and validation. This saves time and automation becomes faster.

Integration: Extracted data have to be inserted into the one or multiple existing software systems for further processing. This integration can be done through APIs or RPA or some other integration tools such as Zapier. If the targeted system provides an API, it is suggested to use the API for data integration. However, in some legacy systems such as desktop applications, APIs are not available and we need to use RPA bot for data integration. 

Data processing and repetitive task automation: Once the data are imported to the software system, someone may need to follow some steps to complete the workflow. Most of these steps, especially those that are repetitive and time consuming can be automated using RPA bots. 

To automate a document intensive process, one needs to use multiple technologies, techniques and expertise and depending on the documents and process, the automation expert needs to choose the appropriate technology and platform.

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