2021 Trend Almanac: Technologies and trends that will dominate the business and consumer landscape. Get instant access
Robotic Process Automation (RPA) refers to software that can be quickly and conveniently programmed to operate basic functions across multiple applications, effectively eliminating the need for humans to perform mundane, repetitive processes. It is rule-based, does not require extensive coding, and uses an ‘if-then’ method to processing.
RPA leverages structured data to more precisely and accurately execute repetitive human tasks. Rule-based tasks that do not require analytics such as performing calculations, responding to inquiries, and maintaining records can all be done using RPA. Unlike cognitive automation, RPA relies on basic technologies that are easy to understand and complete, such as workflow automation and macro scripts.
RPA can be rapidly implemented, reduce attrition, and increase employee productivity by taking over the operation of tedious, repetitive tasks. Because of this, RPA supports business innovation without the usually high tab to test different ideas, and it gives employees more time to do the more intricate and cognitive tasks.
Examples of RPA uses include the banking/finance industry or call center sector. In banking, RPA can be used for a variety of retail branch activities, commercial underwriting, anti-money laundering, and loan processing. In a call center, there are a large number of repetitive tasks that do not necessitate decision-making proficiency. RPA can automate processes such as data capture and integrate workflows to identify callers/customers and providing all manner of information for the call operator, bypassing their usual need to access multiple systems to collect contextual information, and resulting in shorter call lengths and better employee and customer experiences.
RPA with cognitive technology can achieve optimum end-to-end automation solutions for business processes. A typical process has two components, one in which rules are easily defined and another where the workflow is too involved to be plainly outlined. The first part can be approached utilizing a rule-driven RPA and the latter can be worked out by a cognitive engine to handle the unstructured data.
Most people think of processes with scanned documents or voice inputs as candidates for cognitive RPA, but processes like reconciliation of data are also suitable candidates.