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From robotic process automation (RPA) to artificial intelligence (AI): the intelligent automation journey

Source |Linkedin.com  |by:  Pascal BORNET, Director – Leader for Robotic Process Automation (RPA) and Global Business Services (GBS) consulting at EY

 

As digital technologies and concepts evolve at a very rapid pace, we are hearing company leaders raise the following questions:

  • What are the next steps after RPA?
  • How AI, chatbots and RPA are linked together, which one brings value to the other?
  • We have already implemented some chatbots, do we really need to implement RPA?
  • Our competitors are already using AI. Should we be doing the same? Are we late?
  • Where should we start our intelligent automation journey? How can we get prepared for the next steps in this journey, and make sure we build this journey in a sustainable and consistent manner?

To try providing anchor points to answer these questions, my colleagues and I have built a simple and very useful framework. In this article, we will describe this framework and provide the keys to understand it. In our next article, we will explain how to use it by providing use cases, and explaining how to build a successful strategy and implementation approach.

Please give us your comments, and / or “like” the article; we would love to hear your views and get your reactions.

Summary:

  • Companies are going through the intelligent automation journey by digitalizing their process activities using robots. Their goals are mainly to improve business efficiency, reduce costs, enhance customer experience (internal and external clients), and reach a higher level of process excellence (e.g., improve quality, accuracy).
  •  The “intelligent automation journey” framework, illustrated in the image above, describes the four main generations of robots that companies are implementing, their characteristics and associated benefits.
  •  It is more logical to deploy the robot generations in a sequential order, starting from generation one to four (e.g., implementing traditional RPA before implementing the next ones). In doing so, companies will be able to avoid experiencing the “empty shell effect.”
  •  Above all, to create a maximum of value out of this journey, effective interactions between the generations of robots need to be implemented. New generations are not meant to replace existing ones, but instead, they are to work hand-in-hand. We have identified that these interactions create synergies, where each generation will add value beyond its single intended benefit.

 

A. THE INTELLIGENT AUTOMATION JOURNEY

Building on the “future of RPA” as described in my previous article, below are the descriptions of the different generations of robots presented in this framework:

The traditional RPA generation includes robots which can perform repetitive, rule-based actions in a digitalized environment (“dumb robots”). On the basis of our experience, this is applicable to the largest part of the automated process activities in a company (more than 60%). Benefits delivered are large in terms of cost savings, improvement of user experience, quality and accuracy. The limits of RPA lie in the absence of capacity to manage unstructured data, to interact using natural language and to handle judgment activities. Still for the next 10 years, due to the large volume, we expect this generation of robots will remain the key focus of companies.

Cognitive RPA widens the application of RPA to process activities using unstructured data, which we estimate at 15% to 20% of the automated processes in a company. Cognitive RPA delivers similar benefits to traditional RPA, but unlocks the capacity for the robot to manage unstructured data, such as free text messages (e.g., emails), or scanned images (e.g., invoices, or people IDs). Adding the functions of natural language processing (NLP) to a traditional RPA robot enables it to understand a free flow of sentences. Machine learning allows the robot to identify and learn patterns, contexts, through repetitive exposure to a series of inputs and outputs (e.g., P.O means “purchase order” when presented on an invoice, but can mean “post office” in the context of an address).

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