The evolution of Robotics Process Automation

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Branden B. Dean

Suits The C-Suite

A recent report by Etventure, Digital Transformation 2018 highlighted that “Digitalization is getting to all parts of our lives and it is deeply disrupting how companies do and therefore structure their business. Most companies (55%) will need to change their business models due to digitalization in the upcoming years. However, the wide majority (62%) of companies state that the knowledge and capability to manage digitalization is not sufficiently available.”

Robotic Process Automation (RPA) and Artificial Intelligence (AI) have shifted from being science fiction to reality. According to the Artificial Intelligence Market Analysis report released by Research and Markets, the market size is growing at a rapid rate – from being valued at $641.9 million in 2017 to over $58.97 billion by 2025. These figures alone illustrate the importance of adapting a company’s operations and processes to integrate RPA & AI.

In this article, we will focus on RPA as one of the major disruptors in this digital age. There has been an inevitable growth in the RPA market as evidenced by the emergence of new vendors and players, the increased number of robots being implemented, and the rising demand for RPA services. For example:

• Pegasystems (a software company that develops and supports customer engagement and operations solutions) acquired RPA specialist OpenSpan to enhance its own Customer Relationship Management (CRM) application and Business Performance Management (BPM) platforms.

• Information Service Group (ISG), a research, digital, sourcing, and managed services company, acquired Alsbridge, a sourcing, automation, and transformation advisory firm to increase its presence in the RPA space

• More and more new RPA vendors are entering the market (Antworks and AutomationEdge for example) and choosing to specialize by industry or function.

RPA is certainly becoming a more established field, and the industry’s confidence in this technology is becoming more pronounced.

• The IPO success of Blue Prism (one of the largest RPA vendors) is evidence that investors have welcomed the entry of RPA into the market. Blue Prism’s stock price continues to rise since its initial debut on the London Stock Exchange.

• Most vendors are focused on making RPA more accessible to their clients by offering free trial versions (e.g. UiPath, Automation Anywhere), improved training platforms including video tutorials and a cornucopia of materials, structured implementation methodologies, RPA projects quality standards (e.g. Blue Prism), and easier configuration interfaces (e.g. Workfusion).

• We’ve witnessed more clients proceeding directly into implementing pilots versus starting their RPA journey going through Proofs of Concept (POC)/Proofs of Value (POV) — an indication that more and more organizations are confident in the technology.

• RPA is likely to be included as an integrated part of larger and long-term initiatives such as ERP, Shared Service Centers, or other large transformation engagements — additional evidence of how RPA implementation projects are continuously evolving.

Vendors, therefore, are in a race to differentiate themselves from their competitors while adding more value to their clients and in the RPA space. This can best be accomplished by increasing the robots’ functionalities by:

• Integrating more intelligence, while allowing their software to be more accessible (and free, when possible) and actionable by the users.

• Incorporating more advanced generations of intelligent automation by becoming cognitive — adding RPA (which only “does”) to functions which “think & learn” and “interact” with the environment.

• Moving towards the long-term of automation via Artificial Intelligence (AI) by including, for example, the ability to autonomously drive other robots.

RPA is beginning to implement and eventually incorporate the following functionalities to keep up with the higher levels of automation:

• Connection of RPA with data analytics systems (big data) to analyze actual data and predict future trends (e.g. share price trends): The robot is able to understand, think, decide, and act (e.g. sell or buy shares) on the basis of the outcome of the analysis.

• Combination of Natural Language Processing (NLP) and cognitive: Virtual assistants (e.g. chatbots) that interact with internal or external clients or operators to facilitate their work. Chatbots will enable a better/faster adoption of RPA, making it more user-friendly and interactive.

• Combination of NLP and Machine Learning: Enabling the understanding of unstructured data received via texts or pictures (for example, unstructured data from invoices received using different formats). The robot is able to learn by itself through repetition, build patterns, and understand new formats based on what it has learned from experience.

• Robot configuration assistants (a possible next step in machine learning): The robot is capable of learning by itself by solely observing a human execute tasks on a computer, identify the tasks repeated regularly (e.g. daily, weekly, etc.), propose to robotize them by itself, and then configure them. When considering the investment in adopting RPA, the largest amount is always the configuration cost, which this functionality will drastically reduce

• Integration with AI robots (e.g. IBM’s Watson and Google’s AlphaGo). Given the current state of technology, talking about AI has to be focused on a specific, narrow topic. In finance functions, for example, finding mistakes in finance compliance audits and summarizing information out of reports spanning a thousand pages. More and more RPA vendors are working on such integration (e.g. partnership between Blue Prism and IBM Watson), but this remains in its infancy.

RPA still has many more growth areas, and with the development of other generations of intelligent automation, one can only imagine the full potential of robots as RPA continues to simplify and ease the lives of humans and their operators. To stay ahead of the digitalization curve, organizations should consider developing digital strategies that encompass a broader automation spectrum to eliminate manual processes, including RPA, AI, NLP and Machine Learning. However, organizations should also keep in mind that for RPA and other options in the automation spectrum to truly evolve and advance, organizations will also need to focus on new talent and people strategies that support and synergize with holistic digitalization initiatives. In a nutshell, let RPA take the robot out of the human, so that your people can focus on the problems that only humans can solve.

This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinion expressed above are those of the author and do not necessarily represent the views of SGV & Co.


Branden B. Dean is an Advisory Senior Director of SGV & Co.