What is the Future of RPA? According to Gartner’s research, the RPA market value will exceed $7 billion by 2024, riding the strength of a 27 percent compound annual growth rate that will continue until at least 2023. 

While RPA has already had a significant impact on the industry, we are at a juncture poised to see a genuinely transformative shift in the economy as it blends with cognitive technologies. 

Cognitive technologies are a roundup of machine learning technologies designed to interact, reason, and learn in a way similar to that of humans. 

In a recent American game show Jeopardy, IBM’s Watson, a supercomputer in the cognitive category, was able to ascertain a probable answer from a question postured in the form of a pun, riddle, or metaphor. Computer science experts were amazed to learn that a computer could parse a complicated topic, understand its true meaning, investigate volumes of data, form a correct interpretation, and ring the buzzer – all as fast as a human. 

At this point of convergence, let’s understand how smart robotics ( RPA + Cognitive technologies) will evolve to support business matters moving ahead.

What does the near future of RPA look like? 


Future of RPA


Simplifying RPA:


Simplifying RPA has been equated to being as impactful as the launch of Excel, which changed the working style of many working communities permanently. 

No-code (Low-code) development platforms allow the creation of software through GUI and configuration (defined wizards, administrative registries) instead of traditional programming (or with less programming). 

These platforms are focused on the design and development of the data layer, business processes, or presentation layer, such as web applications. Such platforms may present entirely operational applications and require minimal (or no-) coding to extend the functionality of the application.

Low-code development platforms reduce the amount of traditional hand-code writing, facilitating expedited delivery of business applications. A common benefit is that a wider-range of human beings can contribute to the application’s development, not only those with a more formal programming skillset. These platforms also lower the initial cost of setup, training, and deployment.

An example of low-code has been Twilio Inc, a cloud communications platform as a service company based in San Francisco, California. Their latest addition, Twilio Studio, is a visual development platform that empowers developers and non-developers across cross-functional teams to build customer engagement apps, notification workflows, and more. The low-code tool facilitates business users to power with more than 1.6 million Twilio developers worldwide. 

Likewise, for the future of RPA, simplifying RPA can make it more useful to a broader community. 

Low-code and no-code tools in recent years have been an answer to the scarcity of development talent in business organizations, and as an effortless way to connect data sources during the automation of business processes.


Solution Convergence:


RPA and Cognitive Technologies


RPA and Cognitive automation represent two ends of an intelligent automation continuum. At the first end is RPA. In a nutshell, RPA automates repetitive, rule-based, and predictable tasks to free up resources empowering them to focus on tasks that need creativity, decision-making, empathy, and judgment. RPA technologies are dependent on human intervention for procedural adjustments. 

At the other end of the continuum is cognitive automation. Cognitive automation brings intelligence to information-intensive processes by leveraging different algorithms and technological approaches. 

RPA can get smarter with cognitive technologies like NLP ( Natural Language Processing), OCR (Optical Character Recognition), and Machine Learning. 

An example would be an automatic document management system. For instance, in an invoice, the name of the supplier will be identified, which will then trigger an action in Accounts payable without the intervention of the bookkeeper. 

In another legal document, RPA will use an NLP tool to extract data such as parties in the contract, the terms of the mentioned clause, parties impacted by a legal procedure, and how they are affected. The RPA tool will take this information and directly insert it into the ERP.


Future of RPA – Marketplaces:


RPA marketplace- Future of RPA


No single provider can provide all the functionality to automate the diverse number of processes in use at companies. RPA companies can not possibly build a custom solution for every process or task that will be automated, and they rely on vendors to provide customized reusable solutions. 

Marketplaces allow operating systems and numerous other platforms to extend their reach via marketplaces. Vendors provide customized and reusable solutions that help reduce implementation time, reduce programming effort, and enhance efficiency for one’s processes. 

An example of a market place is the BluePrism Digital Exchange. It has a simple goal: to make the consumption of Intelligent Automation solutions fast and easy. It creates an ecosystem of many forward-thinking companies that join hands on a journey to democratize RPA. 

Intelligent Process Automation:


Intelligent Process Automation- Future of RPA

In several organizations, there are seldom up-to-date manuals for the most repetitive processes. An RPA process model is hence dependent on interviews, gaining a high-level understanding of the process, check for exceptions and running pilots. 

To solve this, several companies are exploring self-learning RPA models/ intelligent automation technologies. Self-learning RPA acquires unstructured data as input and attempts at using this knowledge to automate tasks. 

Fundamentally, in the future of RPA, Intelligent automation uses tools like machine learning. With ML, robotic process automation moves beyond basic automation. By analyzing historical data, patterns are detected to deal with perceptual, sentimental, and conceptual inputs to predict better. 


An example for e-mails is:

-IPA can service incoming e-mail using RPA capabilities.

-Using NLP capabilities to understand the context of the e-mail, the system can then carry out the required follow up action.

-Lastly, with the help of ML, the system collects data to learn from such events to make better and faster decisions for the next time. 


The Next Big Wave…


In conclusion, the next big wave in the future of RPA will involve more of AI, cognitive technologies, and witness digital transformation across domains and industries such as BFSI, Retail, Aviation, Oil and Gas and legal

A digital workforce can promote efficiency, productivity, and foster innovation. It has to be supported, however, with a focus on cultural change to yield good results. 

The real winners in this era of digitalization are the ones who ride the wave to find what’s more profitable for them and leverage those technologies to stay ahead of the race. 

Get ready to invest in RPA, speak to our team of experts at Auro.