What to do when robotic process automation results aren’t meeting expectations.
Successfully completing initial robotic process automation (RPA) projects can be exhilarating. But early successes can sometimes divert attention away from dealing with priorities that are crucial in ramping up RPA to an enterprise-scale operational level. The earlier these risks are addressed, the faster organizations can springboard to the higher levels of efficiency promised from leveraging RPA.
RPA is a reliable and scalable approach that uses software bots to automate transaction processing, data manipulation, and cross-platform communication. Various industry studies have stated that using RPA could reduce processing costs by up to 80 percent. However, attempting to achieve full-scale implementation goals by simply replicating early successes on a larger scale is likely to be slower and less successful than desired.
Blind Spots Can Derail Automation at Scale
RPA eliminates tasks like rekeying data, removing rekeying errors, and improving process quality. Many organizations view it as a way to cut costs, speed processes, and enhance customer satisfaction. But initial successes can produce unachievable expectations if organizations don’t anticipate typical speed bumps that can impede progress as they seek to ramp up their RPA efforts to enterprise scale.
“When organizations first approach RPA and get some quick wins, there will be a strong bias to blindly use RPA to attack lingering issues or inefficiencies,” says Paul Joseph, Robotic Process Automation Leader with SDLC Partners. Different processes may have different levels of automation, and organizations need to realize that not every automation effort can produce an ROI sufficient to justify it. Also, there are many processes that can be automated by simple configuration changes that enable the functionalities within the application itself.
Some typical speed bumps that organizations should anticipate:
- Processes and systems can change, which can lead to “broken” software bots if those bots aren’t designed to automatically or intelligently accommodate the changes.
- Automation efforts need to account for exceptions that should be diverted to manual intervention.
- Both IT and the business need to buy into RPA implementations to ensure prioritization of what needs to be automated.
- Change management issues may surface as staff, currently engaged in manual activity, worry how automation will impact their jobs.
- Coordinating HR, compliance, and audit functions.
- Absence of disaster recovery and business continuity plans for RPA.
Standardize to Success and Scale with RPA
As Joseph observes, it’s important to utilize a standardized model for determining what and how to automate so that expectations can be fulfilled and the organization can develop a repeatable method for identifying, building, deploying, and scaling RPA projects.
“Well-defined process mapping is important to automate the process,” he says. “Often people place so much focus on the automation in RPA and overlook the process side. The software bot and the automation are only as good as the process.”
SDLC Partners engages Lean Six Sigma Black Belts with RPA experience at the beginning of every RPA journey to carry out process mapping. Senior RPA architects collaborate to optimize the process for the selected RPA tool, such as UiPath, Blue Prism, or Automation Anywhere. Even though RPA tools are similar in principle, there’s no one-size-fits-all as each has its own preferred path to implementation.
Automating high-volume activity can result in faster processing with more consistent results than manual processing, leading to greater accuracy and higher customer satisfaction. An effort to automate a claims audit process may encompass 50 or 100 different ways of pricing claims. But, often, a much smaller subset, perhaps 10 to 15, accounts for a large volume of activity. Trying to automate all activity may lower the overall ROI, and Joseph recommends that organizations forecast at least a four-fold improvement to justify automation.
Well-defined process mapping is important to automate the process. Often people place so much focus on the automation in RPA and overlook the process side. The software bot and the automation are only as good as the process.
— Paul Joseph, Robotic Process Automation Leader, SDLC Partners
Key strategies that can speed up RPA efforts and improve the likelihood of realizing expectations include:
- Engaging business process analysts in forging the solution.
- Formalizing standards to establish clear, well-controlled execution patterns and practices that will ultimately encompass the entire organization.
- Developing standardized operational metrics to objectively manage product lifecycle execution.
- Committing to retraining/reassigning staff freed up from manual processes.
- Developing a roadmap to achieve future-state objectives.
RPA success strategies and stories
Defining upfront what success looks like is critical to meeting expectations. Is your goal:
- To reduce manual effort and attain efficiency?
- To speed digital transformation and increase revenue?
- To improve quality of operations and compliance?
- Or to create structured data quickly and cost-effectively to support evidence-based management with data analysis?
Forecasting the ROI and defining post-implementation benefit realization methods are also key factors in achieving RPA goals.
It’s also important to determine what will happen with the resources freed up from manual processes. SDLC Partners worked with a healthcare product and service organization to automate a manual claims audit process that required 30 to 35 minutes to process each one of approximately 5,000 claims per week. RPA bots were programmed to perform the entire audit process, freeing up auditors to solve meaningful business growth issues, with half the auditors taking on more senior roles.
In another RPA implementation, SDLC Partners worked with a state licensing agency to design a system that could automatically verify each professional license case within three minutes, cutting verification costs by 30% and allowing process engineers to focus on other issues to support growth. Many organizations realize early on that they need outside expertise to fully pursue RPA goals, justifying the cost of outside expertise both in terms of long-term savings and reduced startup costs.
The faster that companies ramp up RPA to enterprise scale, the earlier they can leverage its benefits for future growth. Organizations interested in avoiding the speed bumps and accelerating their RPA success, can request a complementary review of their RPA project/outcomes.