Five Lessons Learned From Our Recent Robotic Process Automation Implementation
Article Synopsis :
Robotic process automation (RPA) is a powerful tool on the verge of transforming the business world as we know it, automating repetitive and rules-based tasks currently performed by humans.
RPA implementations are a mix of change management, technology, and workforce consulting. In this report, Colin Bryan, of West Monroe Partners (WMP), shares experience implementing RPA into WMP’s new-employee onboarding process and accounts receivable process.
The five lessons learned are as follows:
- Understand the complexity of the processes you want to automate well in advance.
Today, rules-based processes are prime candidates for automation. However, these same processes can cause complications for RPA software implementations when complexity is not completely understood and documented. While people can make decisions by learning patterns, RPA software has difficulty mimicking this human ability. A process that’s almost entirely rules-based but requires just one ambiguous decision may prohibit an RPA implementation from being effective. In addition, data entry processes that touch multiple applications can become increasingly complex to automate if the data format differs between systems.
There are workarounds for these nuances, but the resulting increase in process complexity can cause RPA software to become more tedious and costly to configure than continuing to use a human to perform the same task. More complex processes requiring human decision-making may be automated in future as machine learning becomes tightly coupled with RPA software, it may not make sense to attempt automation now.
- Host functional and technical design sessions to make sure business users and technology users are on the same page.
Before bringing RPA into your workflows, it’s important to hold “design sessions” that answer key technical questions. From a functional perspective, this includes mapping out business processes, gathering requirements and information from end users, collecting information on the frequency of process execution, analyzing how often there are “exceptions to the rule,” and documenting process outputs. However, these are not the only details that influence your RPA implementation. Failing to hold technical design sessions can severely delay timelines and result in unnecessary re-work.
- People will likely need to re-engineer their processes – and this requires change management before go-live.
When evaluating processes to automate with RPA, process 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, pre-existing data standardization, number of system touches, and flexibility in the sequencing of tasks can influence the amount of upfront work required before configuration begins.
For a seamless RPA implementation, it’s important to manage the expectations of the project team by establishing a communication schedule that outlines the reasons for the process re-design and the actions required by the team. Change management is critical throughout the RPA project lifecycle to ensure employees are properly prepared to take on new tasks that come with process re-design and the upcoming robot deployment. In contrast to most change management initiatives, RPA requires a robust change management plan before go-live, given the magnitude of change required by teams.
- Over-test the robot to ensure it handles exceptions properly.
It’s 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. There’s nothing worse than believing you have successfully deployed your first robot only to realize a week later that it fails when triggered on Employee X’s computer because her screen resolution is different from Employee Y’s. To ensure “robot sustainability,” use activities in the workflow configuration that give the robot instructions on what to do in case of errors or when encountering Condition B instead of Condition A. Finally, check, double check, and triple check that the process runs in all scenario types before signing off on a successful implementation and handing over to the end-user.
- Growth considerations: There are multiple ways to perform one objective, but some configurations are more conducive to scale than others.
Consider this example: A company has a centralized accounts receivable department that processes all invoices out of the corporate headquarters in Chicago. When configuring a robot to automate invoicing, the RPA implementation team instructs the robot to type “Chicago, IL” into the address line of the invoice being sent to the customer by hardcoding this deep into the workflow. Years later, the company expands and decides to divide accounts receivable operations into East Coast and West Coast departments, as business offerings have differentiated and unique invoicing procedures must be followed. The company must now go back into the workflow and re-configure the value typed on the address line to reflect this change because the original invoicing robot was not developed with scalability in mind.
Take an agile, or iterative, approach to all RPA implementations – meaning every time a change or new exception is introduced to the process, the robot needs to be reprogrammed.
Though RPA is tricky to implement, it can create tremendous value for an organization, including:
- Cost savings and employee engagement
- Attracting and retaining employees
- Increased productivity and better customer experience
- Reduction in human errors
- Regulatory compliance
- Laying a foundation for the future (for other AI deployments and smarter bots down the road)
Link to Full Article:: click here
Digital Insurer's CommentsFor all the talk about RPA eliminating jobs, what it really does in the insurance context is eliminate drudgery—key to attracting primarily millennials to the industry. Though RPA technology is indeed complex, the three steps for RPA implementation are relatively simple:
- Evaluate current processes to determine whether they can be automated through RPA
- Facilitate design sessions to understand the people, process, and technical implications of RPA
- Configure and manage the RPA software
Once up and running, we recommend establishing an RPA “center of excellence” for sustainable maintenance of your new robotic workforce.
Link to Source:: click here