High TechFebruary 23, 2018

Robotic Process Automation: The First Step in Digital Transformation

Robotic Process Automation (RPA) has been leading the digital transformation movement which…
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Robotic Process Automation (RPA) has been leading the digital transformation movement which has upgraded operations and business models in many enterprises over the last few years. Regardless of its success, however, the name chosen for this technology still generates confusion among uninitiated business executives.

No, it does not involve the type of hardware-based robots which we typically associate with the term. It goes a step or two beyond business process automation which has been around for at least thirty years. But it does not go so far as to justify its description, as many RPA vendors tend to do nowadays, as an “artificial intelligence” technology.

RPA is software that mimics human action in performing a repetitive, rule-based task that sometimes requires a decision or action. It is particularly suited to integration tasks where employees provide a bridge between disparate databases and systems. Unlike traditional process automation technologies, the RPA software can interpret the user interface of specific applications and follow the steps previously executed by the human operator. And in another departure from traditional process automation technologies, these steps are not programmed, but are described to the RPA tool, much like an employee would demonstrate the process to a colleague.

As such, RPA is a great example of augmentation technology. With RPA, employees can set up, launch, and manage “digital workers” or “virtual employees” that assist them in performing their jobs, freeing them to work on higher value added (and potentially more fulfilling) tasks. At the same time, this “self-service” dimension of RPA also frees up IT staff to focus on more significant (and potentially revenue-generating) enterprise-wide initiatives.

Millions of repetitive and time-consuming tasks are performed every day by employees of a typical large enterprise. These are well-defined tasks, involving structured data, such as updating customers’ contact information, finding the most recent status of a sales transaction, opening email attachments, or issuing a purchase order.

The main difference between the RPA software and the employees it assists is that it works all day, every day of the year. This constant process monitoring provides a number of important benefits such as better data collection and analysis and improved detection of process anomalies. Other benefits include increased speed in performing routine tasks, improved employee and customer satisfaction, reduced labor costs, better execution consistency, and improved accuracy.

More generally, RPA improves standardization across the enterprise and ensures compliance with regulation and with internal policies. As the burden of regulatory compliance is increasing in the financial services and other industries, RPA allows enterprises to move beyond throwing people at their compliance challenges.

Speaking of challenges, implementing RPA involves a number of them. These challenges are the ones typical of any process or activity becoming digital: The threat of cyberattacks, upfront costs, training staff in the required new skills and adjusting work processes, and the cost of ongoing maintenance, revisions and upgrades of automated work routines.

In the near future, RPA tools themselves will undergo major upgrades by incorporating recent advances in artificial intelligence (AI) software. Adding machine learning capabilities, for example, moves RPA forward in the evolution of enterprise automation, allowing the software to “learn” from its constant observation of the process and adjust required decisions and actions or even the process itself. It also helps in moving RPA from the realm of structured data to the more challenging one of unstructured data, enlarging the scope of activities it can handle.

Furthermore, combing RPA with new deep learning-based methods of speech recognition and natural language processing (and their encapsulation in enterprise chatbots) will make them highly versatile and capable of tackling higher level, judgment-based tasks. These developments will eventually get enterprises closer to the day when many of their employees will have their own automated personal assistant, improving productivity and the quality of life at work.

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