Every RPA implementation is different, but some key pitfalls occur regularly and can be avoided when you follow best practices. Here are some key lessons learned to help your company in the RPA journey.
5 lessons learned during RPA implementation
Understand the complexity of the processes you want to automate.
Rule-based processes are ideal candidates for automation. However, these same processes can cause complications for RPA when you do not understand and document the complexity. People can learn patterns, but RPA has difficulty mimicking this ability. A rule-based process that requires ambiguous decisions may prohibit the RPA implementation from being effective.
Host design sessions to make sure business and technology users are on the same page.
It is important to hold “design sessions” which answer key technical questions before bringing RPA into your workflows.
The design sessions should include i) mapping out business processes, ii) gathering information from end users, iii) analyzing the frequency of process execution, iv) analyzing how often there are “exceptions to the rule”, and v) documenting process outputs
When you fail to hold technical design sessions, it can severely delay timeline and result in unnecessary re-work.
You will probably need to ask people to re-engineer their processes – and this requires change management.
When you evaluate processes to automate, re-engineering is almost always recommended to increase the ease of implementation. The scale of these modifications, however, can vary. Factors such as data inputs required, preexisting data standardization, and flexibility in the sequencing of tasks can influence the amount of upfront work required.
For a seamless RPA implementation, it is important to manage the expectations of the project team. We manage these expectations by establishing communication which outlines the reasons for the process re-design and the actions required by the team. In contrast to most change management initiatives, RPA requires a robust change management plan before go-live, given the magnitude of change required by your teams.
Test the robot to ensure it handles exceptions properly.
It is easy to start celebrating once your robot successfully automates a process for the first time. However, the real work begins in the testing phase to ensure the robot handles exceptions properly. Use activities which give the robot instructions on what to do in case of errors or when encountering unfamiliar grounds.
Finally, check, double check, and triple check that the process runs in all scenarios types before signing off on a successful implementation and handing over to the end user.
Keep growth in the back of your mind at all times. There are multiple ways to perform one objective, but some configurations are more conducive to scale than others.
As companies grow and change, processes tend to evolve. Think object-oriented and make the design process a central key of your RPA implementation.
RPA developers tend to code workflows with fixed values for certain processes, and do not think about potential scalability. The company must go back into the workflow and re-configure the values to reflect the necessary change because the original robot was not developed with scalability in mind.
There are a lot of scenarios where designing a robot to automate a task could result in headaches when change happens. We recommend taking a flexible approach for all RPA implementations, taking growth into account.
Achieving a return on your RPA investment
Robotic process automation, when implemented thoughtfully, can create tremendous value across your organization, as it has for ours. This value includes: Cost Savings and Employee Engagement.
Automated processes reduce the number of employee hours required, giving you the flexibility to grow your operations quicker without adding headcount.