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There’s the buzzword “Robotic Process Automation” (RPA), software that is programmed to handle high-volume highly-repeatable tasks normally performed by humans. A couple examples include data entry via OCR page recognition, or the manual match-up, pairing and copying of data between an ERP and a CRM system.
Just as 30 years ago when OCR software was enhanced with greater intelligence to integrate better with business processes and renamed accordingly to Intelligent Character Recognition (ICR), the name Intelligent Process Automation (IPA) is beginning to be used to describe the combination of RPA with process automation and machine learning.
McKinsey says that IPA is “a suite of business-process improvements and next-generation tools that assists the knowledge worker by removing repetitive, replicable, and routine tasks. And it can radically improve customer journeys by simplifying interactions and speeding up processes.”
Analysts at Cognilytica wrote that “while RPA is making significant improvements into company’s operations by replacing rote human activity with automated tasks, Artificial Intelligence (AI) is poised to give this new engine of productivity a gigantic boost. RPA tools get stuck when judgement is needed on what, how, and when to use certain information in certain contexts. What if systems can learn from its human supervisors about how to utilize that information? Systems that leverage machine learning (ML) to dynamically adapt to new information and data will shift these systems from mere robots that automate processes to Intelligent Process Automation (IPA) tools that can significantly impact the face of the knowledge worker economy.”