Unstructured Data Process Automation

23 Aug 2019
by Sarah Burnett, Ankur Verma, Prannav Srinivsan

The advent of Robotic Process Automation (RPA) helped enterprises automate documents with structured data sources; however, content-centric documents – accounting for almost 80-85% of enterprise document load – cannot be automated using conventional rules-based solutions. Consequently, manual processing of these documents often leads to issues such as long turn around time, high cost of operations, high incidence of errors, and difficulty in automating heterogenous data.

Driven by these factors, enterprises, today, are looking for solutions that incorporate elements of Artificial Intelligence (AI), so as to process documents with unstructured data sources. The AI-based solutions, often called Intelligent Automation (IA) solutions, possess capabilities such as computer vision, machine learning, and NLP that can be integrated with RPA and BPM workflow to provide an end-to-end automation experience. Technological advancements, such as transfer learning, are further easing the barriers or inhibitions that enterprises have while adopting any intelligent automation solution.

This viewpoint covers the entire document processing conundrum in detail, with details on applicability of AI to provide an end-to-end automation experience. The sections covered in the viewpoint include:

  • Limitations of RPA and OCR / template-based solutions in processing content-centric data
  • The role of AI in document processing
  • How AI augments RPA and BPM to provide an end-to-end process automation experience
  • Key technologies powering AI capabilities, including transfer learning
  • A case study explaining how an enterprise utilized an intelligent automation solution to automate document processing


Service Optimization Technologies (SOT)


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