Artificial intelligence at work: The problem of managerial control from call centers to transport platforms

Abstract

There has been much recent research on the topic of artificial intelligence at work, which is increasingly featuring in more types of work and across the labor process. Much research takes the application of artificial intelligence, in its various forms, as a break from the previous methods of organizing work. Less is known about how these applications of artificial intelligence build upon previous forms of managerial control or are adapted in practice. This paper aims to situate the use of artificial intelligence by management within a longer history of control at work. In doing so, it seeks to draw out the novelty of the technology, while also critically appraising the impact of artificial intelligence as a managerial tool. The aim is to understand the contest at work over the introduction of these tools, taking call centers and transport platforms as case studies. Call centers are important because they have been a site of struggle over previous forms of electronic surveillance and computation control, providing important lessons for how artificial intelligence is, or may, be used in practice. In particular, this paper will draw out moments and tactics in algorithmic management has been challenged at work, using this as a discussion point for considering the possible future of artificial intelligence at work.

Introduction: Artificial Intelligence in Management

“…A second important challenge is to understand how these technologies are actually used at work. As I have argued elsewhere (Woodcock, 2022), "algorithmic systems of control at work have both strengths and weaknesses" (p. 5). They can be effective in some cases, or more limited elsewhere.…”

The Cyber-Industrialization of Catfishing and Romance Fraud

“…Centers have employed a variety of strategies to improve efficiency and maximize profits, including 24-hour staffing, just-intime performance evaluations, incentive programs for rapid or high-quality output, and optimized cueing (see, Ta, et al, 2021;Borst, Mandelbaum & Reiman, 2004). More recently, call center performance has been enhanced and expanded via machine learning, AI, and deep learning processes (see, e.g., Woodcock, 2022;Deschamps-Berger, Lamel & Devillers, 2021).…”