Data Driven Management, Artificial Intelligence, and Automation

Abstract

Data driven management (DDM), artificial intelligence (AI), and automation in some ways represent the “end game” for technologies in relation to their impact on organizational and workplace practices. While new technologies have affected how work is performed since the dawn of human kind, the rate of technological development, along with the fusing of human and machine roles, sees us placed at the beginning of the era of widespread and pervasive AI-supported automation. Firms primarily invest in new workplace technologies to reduce costs and increase labor productivity (Avent, 2014). These new disruptive technologies support profound changes in how organizations organize, manage, and perform work, as they seek to reduce costs, improve their competitiveness, and develop new markets (OECD, 2015, p. 17). Countless science fiction movies have raised the specter of the “rise of the machines” and an allied AI-ruled dystopian future. The technologically advanced plutocratic world envisaged in the 1982 movie Bladerunner is also beginning to look eerily prescient. The digital economy is producing new technologies that boost industrial productivity and output, but which in turn rapidly reduce the number of people needed to produce that output (Gillies, 2015, p. 117). These changes therefore raise unsettling questions about the role of machines and automation in our workplaces. Does data analytics, for example, empower managers in relation to organizational decision-making or does it create machine-driven data management bureaucracy and control? (Pedersen & Aagaard, 2015). What is the ability of industrialized “high income” economies to create new jobs to replace those that are rapidly being lost to automation? These technologies also have the potential to replace some of the work that is currently being offshored and generating employment in developing countries (Fersht, 2016b; Hamilton, 2016, p. 95). As outlined in the introduction, the authors’ aim was not to create a future of work book per se. Given the nature of these rapidly changing technologies, however, this chapter considers and examines forecasts and labor market predictions more than other sections of the book when considering how these technologies are being used to support new and changing work practices.