By Ian Barkin
One of the most exciting and rewarding aspects of implementing RPA technologies is that they are not just about plugging in some technology and calling it a day. There is also a lot of re-thinking and re-building that can be done to extract the full potential of the automation.
In helping enterprises apply RPA, we’ve come across two broader camps of transformation methodology. One involves quickly automating processes first, and then addressing inefficiencies or problems related to the automation over time. The other involves comprehensively investigating, streamlining and transforming a process before automating it. There are situations where a quick-fix can be valuable, even necessary. But, more often than not - the latter is far more ideal: transforming before you automate will yield more sustainable results.
Developing an automation solution of any complexity will require some degree of process redesign. Think about it: if you intend to assess a process for automation, you are already looking at it at a granular level. Assessing the process is the first step towards achieving automation as well as transformation, so you might as well transform your process for the better while you’re at it.
Any process transformation that improves efficiency will be very conducive to automation. And often the true value of an RPA initiative isn’t simply doing a process faster - as robots are apt to do but doing a process better, or in some cases, eliminating a redundant process all together.
Always remember that a process workflow for RPA is inherently different from that of an employee. Let's look at an example of how this difference can play out in the workplace:
Imagine a situation where a process involves copying 100 points of data from one application to another. An employee can process maybe 1 or 2 at a time, flipping through the applications and copying them sequentially. As a robot, you look at the page as a whole, take in all of the data points and process it all at once. This ‘batch’ processing method is optimal for a robot, but not possible by a human employee. And ultimately, it can affect how you structure your workflow for optimal efficiency.
Another example to consider is the necessity of quality assurance or auditing procedures if an upstream process is going to be fully automated. It might be the case that auditing procedures become obsolete, or at least much more simplified – employees audit other employees because mistakes happen and must be minimized. But in certain cases, such as calculations or data transfer, you can guarantee that an automation solution will perform as programmed and be 100% accurate.
Overall, it’s imperative to begin your transformation journey with both digital and human workers in mind to maximize output, efficiency, and satisfaction. And don’t forget downstream processes as well. These must evolve to accommodate the structural changes in output that result from automation. Always aim to challenge and redesign the process at hand before locking it in. So, the next time you hear the words ‘Digital Transformation,’ think of how ‘transformation’ comes before, and informs the ‘digital.’