Robots at Work: Robotics Process Automation and the Future of Employment

EasyChair Preprint no. 11256

Abstract

This abstract provides an overview of the ongoing impact of Robotic Process Automation (RPA) on the future of employment. RPA represents a transformative technology that automates routine, rule-based tasks in various industries, raising questions about its implications for the workforce. This paper delves into the growing adoption of RPA, its effects on employment, and potential scenarios for the future of work. The rise of RPA has brought about significant changes in the workplace, with businesses seeking to enhance efficiency, reduce operational costs, and improve accuracy by automating tasks that were traditionally performed by humans. While RPA promises numerous benefits, including increased productivity, lower error rates, and 24/7 operations, it also stirs concerns about the displacement of human workers. This paper reviews the current state of RPA implementation across industries and its influence on job roles. It analyzes the impact of RPA on both blue-collar and white-collar workers, exploring the types of tasks most susceptible to automation and how companies are managing the transition. Furthermore, it discusses the potential for job displacement and the need for upskilling and reskilling to adapt to the evolving job landscape. The future of employment in the context of RPA is uncertain, but various scenarios are considered. This abstract addresses potential outcomes, including the coexistence of human and robotic workers, the creation of new roles related to RPA, and the importance of enhancing soft skills that machines cannot replicate. It also highlights policy considerations and ethical dimensions surrounding RPA adoption.

Keyphrases: Automation in the workplace, Human-Robot Collaboration, Robotic Process Automation

@Booklet{EasyChair:11256,
  author = {Lee Kasowaki and Eden Jack},
  title = {Robots at Work: Robotics Process Automation and the Future of Employment},
  howpublished = {EasyChair Preprint no. 11256},

  year = {EasyChair, 2023}}