The first generation RPA focuses mainly on structured data, where data extraction is straightforward and it usually results in just 30%-40% Straight Through Processing (STP). In an effort to bring structure to the unstructured data, enterprises turn to cognitive automation technologies, which is a convergence of RPA and AI and ML. RPA use cases are content-dependent and in cognitive automation models, the idea is to make the RPA bots learn from human behavior. For this, vision technology like optical character recognition (OCR), document extraction tools, ML or a combination of these capabilities are leveraged to bring structure to the unstructured and semi-structured data. The major challenge in automating data extraction is due to the presence of voluminous unstructured dark data.

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