The introduction of Robotic Process Automation (RPA) has enabled organizations to effectively deal with manual and mundane back-office operations. But, with the advancement of technology and acceleration of digital transformation initiatives, RPA is beginning to fall behind with issues related to scalability surfacing.
Since its introduction, RPA has enabled organizations to effectively deal with manual and mundane back-office tasks. It has empowered employees to save millions of hours and helped them to scrap significant repositories of documents while updating databases and generating invoices.
However, the past couple of years have shown that restricting the RPA to simple back-office operation is not enough. As per a 2020 report from Gartner titled “Forecast Analysis: Robotic Process Automation, Worldwide,” around 90% of the organizations were expected to adopt RPA in some form or another. But, with the continuous advancements in the technological landscape, organizations have found themselves in a rut to scale their RPA.
Today’s RPA processes are growing more complex than ever. With development time growing at an exponential rate, maintenance is becoming challenging and most of the minor change requests have turned into significant projects. Also, when RPA is used to scale up an entire process, it is a struggle since every possible outcome has to be predetermined using linear decision trees.
Instead of deploying strategic RPA implementations, organizations should incorporate intelligent automation strategies that will enable them to keep or gain a competitive advantage. It enables organizations to seamlessly replicate complex processes and has advanced to the point that it can reliably and transparently automate recurrent human decision-making processes.
Here are a few intelligent automation strategies that help organizations effectively deal with RPA:
Determine each technology for the issues it can address
RPA enables organizations to seamlessly move data from one place to another hard-to-reach system, however, if it encounters an issue such as missing data, then the entire automation is stopped in its tracks as well as referred back to the user.
While there are many ways to mitigate RPA process failure, it often means more processes, complexity, and cost. Organizations should understand that there is no native intelligence in RPA; processes are fragile and often consume a significant amount of time to build and manage. Hence, they should be discrete in business logic and technical processes. This ‘separation concern’ is critical in software development and devises the basis of any effective operation.
Organizations should rely on an accessible decision engine. This will enable them to retain control, responsibility, and business logic while ensuring that every change does not require a software development cycle. On the other hand, if organizations end up opting for a non-linear decision engine, they can emulate the application of human-like reasoning over the entire processes to reach efficient, high-quality outcomes.
Design a strategic approach
Many automation-mature organizations have reached a place where RPA no longer provides accumulative value. They desired more from the platform, and now seek a paradigm shift to deliver better value.
Intelligent automation needs the whole alignment with a broader business strategy of the organization to act efficiently. Those keen to invest in RPA often begin with more minor, top priority use cases and then strive towards achieving scalability from there. On the contrary, intelligent automation, which is strategically adopted to achieve scalability results in organizations in multiple sectors across a diverse geographical location, can go beyond just improving the efficiency of their business operations. This helps to transform their business operations while opening opportunities for generating new revenue streams.
Organizations aiming to remain competitive in today’s marketplace should consider moving beyond strategic RPA while incorporating an intelligent automation strategy that enables them to scale while remaining competitive.