- Business leaders often question when to use enterprise automation tools like business process management (BPM) versus robotic process automation (RPA).
- RPA uses software or cognitive/AI robots for performing process operations instead of human operators.
- BPM is an approach to re-engineer and streamline underlying processes to drive efficiency.
Earlier this week, I had an opportunity to meet with the C-Suite team from one of the largest insurance companies in the US. We engaged in a vigorous discussion around when to use enterprise automation tools such as business process management (BPM) versus robotic process automation (RPA)—a topic that is frequently on the minds of business leaders. Many organizations have a similar question if they have either deployed BPM or are considering deploying BPM and RPA.
By definition, RPA is a software code that runs virtual workforce (robots) for process operation. RPA processes rules- based, structured data through the user interface of the robotic software that supports the process. Examples would include repetitive data entry functions and enterprise resource planning (ERP) downloads and uploads. This technology aims at automating processes without changing, replacing, compromising or adding maintenance overhead onto existing applications, reducing costs and enabling the long tail of change. Given that these software robots have similar capabilities as existing users, there is no real requirement for additional system testing.
On the other hand, BPM and IBM Operational Decision Manager (ODM) are established as an overall approach to streamline business processes for optimized efficiency and value. The BPM platform has been an integral part of process workflow automation solutions. It involves re-engineering of the underlying process to drive efficiency and create a more consistent customer experience. Data from the underlying system of record is passed between the new application and back-end systems, bypassing the established user interfaces. BPM requires extensive additional testing as data layer integration creates brittle interfaces between applications. This chart delves deeper into the differences between the two approaches.
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