Robotic process automation (RPA) can be the true antidote to manual, rote work, or it can be our worst nightmare if you listen to all the drama or the hype. RPA centers on the use of artificial intelligence (AI) to apply human-like thinking to streamline a typically manually intensive process or activity; and whether we like it or not, it’s here to stay.
Take, for instance, the process of data extraction from documents such as invoices. Application of advanced optical character recognition (OCR) and intelligent document recognition can automate a significant amount of the job of data entry typically performed by clerks or specialized data entry staff.
Interestingly, human effort is still involved with attaining the ability to hand off a process or task to a machine. Whether the process is discovery of configuration rules for the email server, training a speech recognition engine to understand a request or configuring data extraction rules for invoices, human effort is required. The work is complex because the individual doing the work needs to understand the range of input required and how to measure results. This work is not a one-time affair. An investment in time needs to be made each time a new automation task is necessary.
What if the initial configuration could also be automated using machine learning, which is a part of AI? Such a task would almost certainly benefit anyone wanting to implement AI-based automation of any kind. In the age of big data, the input required would be readily available. Consider this opportunity when using invoice capture. The current approach for invoice data extraction typically uses one of two models:
These models are implemented in one of two ways: either tuning beforehand using samples of documents or tuning after the fact as they are encountered in production. In practice, both are typically done. One trick some vendors employ is to use established rules for identifying those invoices that underperform and introduce those examples into a workflow that has a staff member identify the location of fields.
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