Evaluating learning instance success

When you evaluate the success of your learning instance, focus on building a solution that will address the widest range of possible document varieties. Such a solution will result in comprehensive automation across all documents and not just a specific document format.

Here are some examples on how success is determined for specific use cases:

  • Email categorization: An organization dealing with high volumes of customer emails can leverage data processing to automatically categorize and route email to relevant departments. This use case minimizes the manual effort of sorting and directing emails on a daily basis. Even with data extraction efficiency of 50% or above, the organization can gain significant benefits from this solution.
  • Bill of lading (BOL) processing: An organization handling large shipments daily, where each shipment requires a BOL, can leverage data processing to manage BOLs that might include both handwritten and typed information. While typed documents can be processed with minimal human interaction, handwritten documents might require human interaction due to illegible writing, poor-quality scans, complex descriptions, updates and corrections, and similar issues. In this use case, efficiency depends on the quality of the input documents. Therefore, the organization will benefit from reducing any manual effort involved in the document processing workflow.
  • Legal document review: A law firm dealing with contracts with various legal agreements for their clients can leverage data processing to perform an initial review and classify contracts for each client. Legal reviewers can then do a final review of the contracts to check for any discrepancies and process them further. This process reduces the time spent by reviewers on initial document sorting. Even with an efficiency of 50% or above, the firm can gain significant benefits from this solution.