Robotic Process Automation (RPA) is gaining momentum by the day, all thanks to the advancements in automation technology over the last few years. Businesses of all sizes— from enterprises to start-ups— have realized the value that automation brings. We now see automation solutions delivering high impact, focused results across various domains— from shipping and logistics to software development and ecommerce. And all businesses realize the need for RPA across their different functions— Finance, Payments, HR, IT, Operations, and more.
What we have observed over the past couple of years is that enterprises in the early stages of their automation journey are still apprehensive about implementing the RPA and the changes that come with it, while enterprises who have advanced implementing RPA across their journey are still struggling to remove human intervention in some of the complex processes—thereby not really getting the best out of their RPA strategy.
Added to this is all the talk of Artificial Intelligence, Machine Learning, NLP and what not, to confuse enterprises further when they are still struggling to get to grips over RPA. It’s still murky waters from a decision-making standpoint, as there is no clear structure to how it may be approached, and this has bottled RPA down to being treated as a mere tactical, immediate solution. From where we stand, this is far from true. Let us elaborate.
Cognitive or AI is the current flavour with anyone engaged with RPA right now. But all the talk still masks the fact that it’s just a lot of rule-based automation with an AI layer to the bots to pass it off as cognitive. True cognitive capabilities are supposed to replicate how a human would be engaged during the process—this involves self-learning, decision-making, and the ability to process unstructured data into rational information.
This gives enterprises the capability to plan for the future without worrying about changes or transitioning of processes or getting bogged down with operational bottlenecks, and can truly enable them to engage a highly advanced digital workforce to complement their business.
New entrants to RPA still treat it as an immediate fix because it requires minimal effort to implement. Define a process, and automate the workflow. Save time and add more efficiency to the process, to achieve better and faster results. However, when you want to scale up and add more processes to automate, you may hit a roadblock due to localization, complexities, unexpected data formats and architectural challenges. This is precisely why it’s still considered as a tactical productivity enabler.
However, with the right RPA solution—one that is self-learning, scalable to handle any type and volume of processes and can automate the most complicated tasks easily, with the ability to process and provide insights into unstructured data that it deals with—enterprises can now engage more on optimizing processes rather that getting stuck in RPA operations. This takes the benefits from RPA to a different level altogether.
Consultants, service integrators, and solution providers for the most part only advise businesses about considering cognitive automation once they have attained a familiarity with RPA, and this advice is consumed without an afterthought. Nothing could be farther from the truth. That is one of the reasons RPA 2.0 is declared to be the next gen RPA. But, enterprises need to define what they need to achieve out of cognitive automation. The very fact that they are still considering this means that rule-based automation or traditional RPA needs to evolve.
While efficiency and speed can be highly improved with traditional RPA, cognitive RPA—with the ability to engage with processes with self-learning capabilities as much as a human would —is where RPA is going to define itself in the coming years. With the right information at hand to effectively optimize processes, enterprises stand to gain a lot by considering cognitive RPA as a differentiator and a strategic asset to drive their business.
It’s never late to future-proof your RPA. Here are the key considerations to make your RPA to an iRPA (Intelligent RPA):
• Continuous learning—Machine Learning models should be trained frequently to match the decision-making frequency depending on the diversity of the input data.
• Robust Decision making—Enabling your RPA to take decisions on input data that was never encountered before.
• Taking your OCR to next level—Making your OCR intelligent is key to making your RPA self-sustained.
Sudhir Sen is the Co-Founder of Option3, and Products Head of JiffyRPA.
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