Enterprises leverage RPA to automate rule-based, high-volume repetitive tasks; not well-equipped for non-standardized processes requiring human intervention. Though RPA has numerous benefits, many organizations fail to realize its value.
Businesses today need to identify the right processes to automate, including exceptions and deviations. This is where Process Discovery comes in — helping organizations to choose the right processes to automate and enabling RPA scalability.
In order to implement automation enterprise-wide, owners need to have a microscopic view of how the existing processes work, the nuances involving human and system interaction, and the outcomes post-successful deployment of automation in bite-sizes.
Manual process discovery is divided into Process Mapping and Process Mining. The traditional, human approach to Process Mapping and Mining fails to accurately capture how business processes work and interact.
Manually mapping the business processes is subjected to biased judgments, which often overlook subtle nuances existing in the processes. Also, capturing granular data remains mainly invisible to human eyes. Moreover, once mapped, processes remain in the shape of static documents. However, business processes tend to change over time. Unfortunately, the documents carrying insights post mapping are never updated to keep up with the change. So, these documents rarely carry real-time insights; hence, any measures taken on these data are often flawed and short-sighted.
Other roadblocks include:
Further, manual process mapping documents do not include data about process performance, process scalability, and business exception/failure statistics.
If the automation implementation strategy is based on such data, the outcome will remain largely ineffective, reflecting poorly on ROI.
Process mining is a better version of process mapping, which uses various automated tools to record system event logs and apply sophisticated algorithms to automatically identify and map business processes. This is by far the most reliable method of identifying proper business process candidates for automation than traditional process mapping catered to by stakeholders.
However, process mining has its share of challenges. For example, process mining tools can capture events only, not user and activity tasks. Hence, various nuances remain primarily under the wrap due to increasing human and system interactions. Also, process users lack the required expertise to correctly interpret data captured by the tools or identify the changes in the business processes. Here, experts have to step in to interpret the generated information, which is often colored by biased opinions of the former.
“It’s an AI-enabled method that helps organizations identify process variations and create detailed process maps to maximize the value of RPA.”
There are many benefits of Automated Process Discovery, a few of which are underlined below:
Improved quality and performance: When Process Discovery captures and interprets empirical data, the outcome presents a clear picture of tasks that need automation attention instead of what employees or consultants think to be done. Hence, the outcome is more accurate, reliable, and captures real-time, up-to-date process workflows minus historical, biased, or guesswork data.
Enhanced visibility: With automated process discovery, the enterprise’s visibility of specific process steps, ownership, and overall processes is guaranteed. The process discovery map becomes the blueprint for identifying new pathways and future automation opportunities.
Minimized risks: When information about the business process is shared with fewer users, the risk of data capture, loss, or corruption is minimized. Also, process maps created after process discovery will help owners understand if the suggested changes will add value to the organization or not.
Increased cost efficiency: Process discovery is a measurable way of implementing changes in the existing business processes. So, more tasks are brought under automation; hence, less need for extra human resources. In the absence of unnecessary repetitions, duplications, errors, and misjudgments, the business processes become more cost-efficient.
Improved scalability: Obviously, expanding RPA using manual data analysis is time-intensive. But, using automated process discovery, data captured is real-time, granular, and can help organizations make intelligent decisions on which processes to automate. This aims to unlock growth opportunities and enhance overall performances using minimal resources and time.
Maximized ROI: When time, cost, and resources are better optimized following business process automation enterprise-wide, the ROI from the automation program will precede the expected ROI figure.
AssistEdge Discover is designed to streamline and automate the process discovery and mapping of business processes. This non-intrusive software leverages user keystrokes and sophisticated neural network algorithms for creating insightful process maps; hence, a powerful blueprint for scaling effective and continuous change management is realized.