Robotic Process Automation: Caveats To Be Aware Of
Blog: Business Analyst Learnings Blog
Robotic Process Automation (RPA) is a type of technology that can be applied to the automation of high-volume and repetitive tasks. It is particularly suited to manual tasks that involve data entry, copying and pasting data from one source to another, and other categories of tasks that are prone to errors. RPA is also suited to the automation of rules-based processes, with very few exceptions as rules typically have to be defined upfront for robots to work as expected. By using a workforce of robots to accomplish repetitive tasks, staff will have time to focus on more strategic tasks that have the potential to add value to the business.
Despite the well-touted benefits of robotic process automation, there are a few caveats implementation teams should keep in mind for a successful implementation of robotic process automation projects:
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Processes might still require some human supervision to tackle exceptional scenarios. As robots don’t respond well to exceptions, humans still have to manage exception handling and deal with any scenarios the robots cannot handle.
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Though RPA is marketed as a tool for business users to achieve “quick improvements”, IT involvement is essential to ensure that support, disaster recovery and change management procedures are followed. This is particularly important because broader architecture and system changes can affect the operation of robots. Involving IT expertise early on in the automation project will ensure they are on board with RPA and can inform the implementation team whenever a change is made. This will ensure that a comprehensive impact assessment is done to evaluate and minimise the impact on the robot. Bear in mind that changes like modified data field mappings, vendor upgardes, system integrations can affect robot functionality
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Don’t automate bad processes. Processes needed to be streamlined and mapped out to ensure automation can deliver the anticipated benefits. Processes that are unstable/prone to change are not good candidates for automation.
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Taking RPA to the next level will involve incorporating artificial intelligence technology, which will ensure that the robots can learn, in order to keep human involvement to a minimum.
What lessons have you learnt from RPA implementation?
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