How RPA, NLP and ML will create the virtual workforce of tomorrow

RPA, which stands for ‘robotic process automation’, is a technology that consists of software robots (bots) that can mimic a human worker performing operations on their laptops. RPA bots can log into applications, enter data, calculate and complete tasks and then log out, send emails, extract data from websites and excel, and generate reports.

RPA software is not part of an organization’s IT infrastructure. Instead, it sits on top of it, enabling a company to implement the technology quickly and efficiently – all without changing the existing infrastructure and systems. What distinguishes RPA from traditional IT automation is the ability of an RPA bot to function without making any changes to the existing applications. This in itself saves organizations millions of dollars.

Once RPA software has been trained to capture and interpret the actions of specific processes in existing software applications, it can then manipulate data, trigger responses, initiate new actions and communicate with other systems autonomously. It is ideal for operations that involve rule based repetitive tasks to be done in large volumes.

Consider a simple operation where an operator has to extract data about a hundred customers from excel, input the data into a web based application, generate a report, and then import the result into another application provided by a vendor. It is expensive to build an interface that works in the backend to exchange information between different applications if the inputs and outputs are not standardized. Traditionally, companies have had to hire human workers to do this kind of work. But RPA has the ability to reduce the workforce by 80-90%, with only a few humans needed to support the bots, handle errors and deal with exceptions.

RPA, though very useful, is a very break-fix approach to automation. Since RPA software interacts with the user interface of websites and applications, a slight change in the design of the user interface could cause an RPA bot to start returning errors. Also, the inputs need to be highly structured meaning that there is a lack of flexibility in implementing the solution.

Enter machine learning (ML)

Machine learning is technology, or a set of algorithms, that allows you to continually learn from data and make predictions. It is being used across industries to improve processes, identify patterns and anomalies in large datasets, and generate valuable insights. It can be fused with RPA to build a powerful solution. For example, an RPA bot would fail to recognize if the input field for an email address changed from the left to the right corner of the user interface, but an ML enabled bot would quickly learn from the data and know where the input field is most likely situated.

Sometimes the inputs are not structured for a bot to start working immediately. For example, you might want a bot to look for relevant information in a set of documents. In scenarios like this, Natural Language Processing comes handy.

What is NLP?

NLP is the ability to train computers to understand written text and human speech. NLP techniques are needed to extract meaning from the unstructured text in a set of documents or communication from a user. Therefore, NLP is the primary way that systems can interpret text and spoken language. It is also one of the fundamental technologies that allows non-technical people to interact with advanced technologies. For example, rather than needing to code, NLP can help users ask questions about complex data sets. Unlike structured database information that rely on schemas to add context and meaning to the data, unstructured information must be parsed and tagged in order to extract meaning from the text. NLP makes use of tools like categorization, ontologies, tapping, catalogs, dictionaries, and language models.

Combining RPA, ML and NLP to create an end to end solution  

An end to end solution of a virtual worker would be one in which a ‘chatbot’ interacts with users and employs an NLP engine to understand their needs. It then triggers an RPA bot that can go online or read through a set of documents to find relevant information. A machine learning engine assists the RPA bot with recognising patterns, understanding what information is relevant to the user, and generating insights. The end result is then communicated back to the user through the chatbot.

Use case: HR Bot

Let us look at a use case of a virtual worker that combines these three technologies.

Employees in large companies often have common questions regarding things like insurance benefits, leave policies, compensation, or how to correctly fill in a timesheet. It can be tedious for HR to find the relevant information. Instead, a chatbot could interact with employees to collect relevant information and understand what information they need.  It could then trigger an RPA bot to search for specific information. The RPA bot could apply some machine learning algorithms to make sense of the relevant information in the documents. It can then return information to the user through the chatbot. Some large corporations have already begun to implement and scale this kind of solution.

As it becomes easier to create bots, the virtual worker of the future need not be confined to larger corporations but rather could be created by anyone. There could be a bot that brings you the latest prices for flights and hotels on specific dates, a bot that shares stock prices with you at 9 am every morning, a bot that helps you compare prices from different websites, a bot that formats your power-point presentation, a bot that scans your receipts to track your spending and give you financial advice, a bot that files your annual tax return. Much like personal computers, laptops, and smartphones, virtual workers may become ubiquitous, dramatically boosting productivity and freeing people to focus on creative tasks and social interactions.

With RPA bots taking up most of the mundane tasks, humans will be able to focus on what they do best, take up more cognitive and creative work, build relationships with customers and co-workers, and spend more time with loved ones. In short, these new technologies will help us become more human and less like robots.

Mudassar Shaikh is an engineer at heart, management consultant by profession, entrepreneur by spirit, and student by essence. He is passionate about enabling organizations to develop new technologies and make them accessible to the masses, positively impacting the life of many and pushing the human race forward.

Image: Pexels

🔴 Interested in consulting?