We have evolved from room-sized mainframes to laptops, from using stick shifts to autonomous vehicles, from personal assistants to virtual assistants, and to so much more in just the blink of an eye. The fast-paced technology-driven world has made our lives extremely convenient now. We see breakthroughs happening in our lives with technological applications doing the heavy lifting most of the time. This level of sophistication and ease is only possible because of industries becoming digitized. The ultimate aim - to have increased efficiency, enhanced accuracy, improved customer satisfaction and optimized workflows - has engaged organizations to invest in automation and operating tools. One such automation tool that is widely used today is robotic process automation.
Designed to take up low-quality jobs, robotic process automation has helped organizations reduce human errors significantly. Low to no manual errors have led to increased productivity, ultimately profiting the industries in business and revenue growth. Hence, organizations across the world are largely deploying robotic process automation, which is why its market size is expected to hit 3.11 billion dollars by 2025. Despite the revolutionary benefits, robotic process automation lacks certain features. Due to this downside, organizations are augmenting technological capabilities to rule-based robotic process automation, giving rise to a new automation tool - intelligent process automation.
Robotic process automation tools are applications that run predetermined codes to carry out a specific set of tasks. For the tool to provide accurate results, it should be fed with the right inputs. Inputs should be in an understandable manner, which means the data should be in a structured format. If the tool is fed with unstructured data, the tool will not understand how to analyze the data in the first place. To add to the complexity, most of the data that organizations collect is in an unstructured format. Hence, to make the most out of all the data gathered, organizations are opting to make automation tools capable of not only handling workflows smartly but also making decisions.
How is it different from robotic process automation?
ML/ big data analytics - With ML, robotic process automation will move beyond basic automation, thereby increasing productivity significantly. By analyzing and learning from historical data actions, ML-powered robotic process automation tools will detect patterns to deal with sentimental, perceptual, and conceptual inputs and predict better.
Smart workflow management tool - Smart workflow software is a tool that takes care of the end-to-end processes executed by both employees and automated systems. Such a management tool enhances visibility in the process.
Natural language generation - These AI-powered engines are capable of creating narratives from the collected data. RPA, when coupled with NLG, cannot only replace manual jobs but also can present a better story for analysts to make decisions.
What are the possibilities of intelligent process automation?
Emails - The intelligent process automation senses the incoming emails using NLP capabilities. After comprehending the context of the email the system carries out the required follow up actions. Finally, with the help of ML the system collects the data to learn from past events to make better and faster decisions. Alternatively, the system will automatically draft ‘thank you’ emails for potential customers after the concerned service desk authority gets in touch with them.
Invoices - Determine payments due. Accordingly send invoices to clients, vendors and business partners reminding them about the payments due.
Event promotion- Intelligent process automation tracks the location of clients, analyzes their chances of attending an event and sends them invitations.
Documentation - In an insurance firm, every customer will have a different document, depending on the claim selected, including age, gender and perhaps financial status. The firms, once upon a time, had to spend a lot of time doing this tedious task. Today, the ML components in intelligent process automation help the system to analyze the different requirements of customers, extract meaning out of the inserted data and generate documents.
Talent management - The new age HRs are not confined to just searching for skillful candidates who will best match vacant profiles. Instead, they are supposed to do more difficult jobs such as creating strategies to achieve business growth, focusing on offering high-level employee satisfaction and tracking competitor moves. IPA, however, reduces the stress of recruiters. By enabling recruiters to focus on business growth, IPA does the heavy lifting of talent management. IPA traces incoming emails from candidates, reads and comprehends the email, scans the resume and gets back to recruiters if the candidate matches the profile.
How to become a leader with IPA transformation?
With an evolved version of robotic process automation, organizations can gain double-digit returns, slash out the processing time, and enhance customer satisfaction. So, why use something good when there exists something better? The real winners in this era of globalization are the ones who find what’s more profitable for them and leverage those identified models as soon as possible. Organizations that only scratch the surface of technological capabilities and stay stagnant in their developmental phase will be left behind in the digitization race, for sure.