Expert Insights

Even with the pandemic-induced current economic climate, the RPA Industry has been growing at exponential rates throughout 2020 and is predicted to continue that trend over the next several years. Currently, the market is flooded with a plethora of “Success Stories”, best practices, and use cases that guide companies towards successful automation programs and specific delivery methodologies. However, as an RPA Services provider, we have noticed that the industry is severely lacking stories of failed implementations. Without these stories of “What went wrong?” companies new to the technology often fall victim to the same mistakes that other organizations make early on. In this 5-part series we wanted to highlight a few of the pitfalls we see quite often in the industry and offer our advice for how best to avoid these scenarios and solve for these issues when building out an RPA Program.

Choosing a Process with Insignificant Business Impact

A common pitfall that organizations encounter when starting out with RPA is choosing processes with insignificant business impact. Often an organization will ask people within the selected department what types of tasks they do daily, weekly, etc. that they would like an automation bot to do instead. The mistake here is that employees will suggest tasks they dislike or want to get rid of as opposed to ones that offer real value to the business when automated. The other way companies fall victim to this pitfall is when organizations look to automate a department’s large predominate process as it takes up the most time. The mistake here is that a process as such could be difficult and cost too much to automate, or worse involve difficult steps and numerous human validation points causing it to not save as much time as they expected and making the investment not worthwhile. When deciding what to automate, organizations should prepare a list of goals and required metrics for what their automations should accomplish. They should also be sure to fully understand the list of attributes that make a process ideal for automation. In other words, companies should avoid automating for convenience and prioritize automating for ROI.

Selecting the first round of processes in an RPA journey can be daunting. The high-overhead costs add pressure on the organization to select processes that give a large amount of ROI in order to justify the investment. This leads to rushed projects that are high-risk and fall short of business expectations at the end of development. Taking the time and making the decision to invest in a strong Process Discovery practice is the best solution for running a successful automation program while guaranteeing ROI.

Process Discovery is viewing business processes under a lens for automation. A strong Process Discovery practice should aid businesses in selecting processes that are fruitful and remove mundane tasks from their employees. It works by observing how standardized the process is, acknowledging the inputs and outputs expected from the process, and deciding whether technologies outside the realm of RPA are needed for implementation. The most important resource required for smooth process discovery operation is an SME who understands the process and knows what is necessary to complete it. When an employee or manager proposes a process for automation, the next step in the discovery phase is for a business analyst to interview the SME to determine if the process is fit for automation. What makes or breaks this decision is the process SME providing all relevant information, from there, the process discovery team can easily identify if the process is worth pursuing. Overall, based on the business goals and standards set in the early stages of building an automation program, if the ROI, time savings, feasibility, and ease of implementation do not meet the standards the company has set then the process will fail to make it through the discovery phase. Evaluating business needs against the set metrics is what process discovery is made to do. Building out a strong process pipeline and proposing automations that deliver real results back to the business are what prevent companies from making the mistake of selecting processes whose output does not justify the investment made to create and deploy it.