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The increasing workplace use of artificially intelligent (AI) technologies has implications for the experience of meaningful human work. Meaningful work refers to the perception that one’s work has worth, significance, or a higher purpose. The development and organisational deployment of AI is accelerating, but the ways in which this will support or diminish opportunities for meaningful work and the ethical implications of these changes remain under-explored. This conceptual paper is positioned at the intersection of the meaningful work and ethical AI literatures and offers a detailed assessment of the ways in which the deployment of AI can enhance or diminish employees’ experiences of meaningful work. We first outline the nature of meaningful work and draw on philosophical and business ethics accounts to establish its ethical importance. We then explore the impacts of three paths of AI deployment (replacing some tasks, ‘tending the machine’, and amplifying human skills) across five dimensions constituting a holistic account of meaningful work, and finally assess the ethical implications. In doing so we help to contextualise the meaningful work literature for the era of AI, extend the ethical AI literature into the workplace, and conclude with a range of practical implications and future research directions.

This article addresses educational challenges posed by the future of work, examining “21st century skills”, their conception, assessment, and valorization. It focuses in particular on key soft skill competencies known as the “4Cs”: creativity, critical thinking, collaboration, and communication. In a section on each C, we provide an overview of assessment at the level of individual performance, before focusing on the less common assessment of systemic support for the development of the 4Cs that can be measured at the institutional level (i.e., in schools, universities, professional training programs, etc.). We then present the process of official assessment and certification known as “labelization”, suggesting it as a solution both for establishing a publicly trusted assessment of the 4Cs and for promoting their cultural valorization. Next, two variations of the “International Institute for Competency Development’s 21st Century Skills Framework” are presented. The first of these comprehensive systems allows for the assessment and labelization of the extent to which development of the 4Cs is supported by a formal educational program or institution. The second assesses informal educational or training experiences, such as playing a game. We discuss the overlap between the 4Cs and the challenges of teaching and institutionalizing them, both of which may be assisted by adopting a dynamic interactionist model of the 4Cs—playfully entitled “Crea-Critical-Collab-ication”—for pedagogical and policy-promotion purposes. We conclude by briefly discussing opportunities presented by future research and new technologies such as artificial intelligence and virtual reality.

Amazon’s projects for future automation contribute to anxieties about the marginalization of living labor in warehousing. Yet, a systematic analysis of patents owned by Amazon suggests that workers are not about to disappear from the warehouse floor. Many patents portray machines that increase worker surveillance and work rhythms. Others aim at incorporating workers’ activities into machinery to rationalize the labor process in an ever more pervasive form of digital Taylorism. Patents materialize the company’s desire for a technological future in which workers act and sense on behalf of machinery, becoming its living and sensing appendages. In this new relationship, humans extend machinery and its reach. Through the work-in-progress process of reaching increasing levels of automation, Amazon develops new technical foundations that consolidate its power in the digital workplace.

Artificial intelligence (AI) systems in the workplace increasingly substitute for employees’ tasks, responsibilities, and decision-making. Consequently, employees must relinquish core activities of their work processes without the ability to interact with the AI system (e.g., to influence decision-making processes or adapt or overrule decision-making outcomes). To deepen our understanding of how substitutive decision-making AI systems affect employees’ professional role identity and how employees adapt their identity in response to the system, we conducted an in-depth case study of a company in the area of loan consulting. We qualitatively analyzed more than 60 interviews with employees and managers. Our research contributes to the literature on IS and identity by disclosing mechanisms through which employees strengthen and protect their professional role identity despite being unable to directly interact with the AI system. Further, we highlight the boundary conditions for introducing an AI system and contribute to the body of empirical research on the potential downsides of AI.

Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets.While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists frommeasuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill substitution and human–machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data, as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally, given the fundamental uncertainty in predicting technological change, we recommend developing a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior.

Hybrid work models have rapidly become the most common work arrangement for many knowledge workers, affording them with improved work–life balance and greater levels of job satisfaction, but little research has been conducted to identify the different hybrid work models that are emerging, and the appropriate supports needed to drive sustainable improvement. This paper utilises primary data from a series of semi-structured interviews with senior Australian human resource (HR) managers, to identify a range of different approaches to hybrid work design, applying the Conservation of Resources (COR) theory. Analyses of these findings have resulted in five key contributions: one being the identification of the most popular current hybrid work arrangements; the second being the key supporting pillars that are required to support successful hybrid work; the third identifies the infrastructure required to support these pillars; the fourth being a theoretic contribution that extends the existing academic literature in this field; and with the final contribution being an interpretation of the findings via COR theory. These contributions have significant implications for both scholars and human resource professionals, as organisations and academics strive to learn from the recent period of turbulence and develop sustainable improvements in performance and working conditions (SDG8), with improved support for employee health and wellbeing (SDG3), and gender equality (SDG5).

Work organization practices, including work flexibility, are changing and can affect worker well-being. Common work flexibility types include working at home, taking time off when needed, and changing one’s work schedule. Given the changes in and the importance of work flexibility, the study assesses its prevalence and association with worker well-being in the United States. We used 2002–2018 General Social Survey—Quality of Worklife (GSS-QWL) data, descriptive statistics, and regression analyses to assess the reported likelihood of job stress, job satisfaction, healthy days, and days with activity limitations among workers reporting work flexibility. The prevalence of work flexibility remained relatively stable during the period examined. Working at home increased the likelihood of job stress by 22% and job satisfaction by 65%. Taking time off decreased the likelihood of job stress by 56% and days with activity limitations by 24%, and more than doubled the likelihood of job satisfaction. Changing one’s schedule decreased the likelihood of job stress by 20% and increased the likelihood of job satisfaction by 62%. This study used all the available data from GSS-QWL and demonstrated the ongoing importance of work flexibility for well-being.

Both shaping and shaped by technological, economic and social facets, the world of work has witnessed a wide array of changes. This review article sets out to provide a synthesis of some of the main directions and insights of existing research connected to the new world of work. In particular, we approached the topic of new work practices through four key dimensions: (1) Conceptual and methodological dimensions in the study of new work practices; (2) Spatial and temporal manifestations of new work practices in the collaborative economy; (3) Individuals, organizations and new work configurations; (4) Power and control. The review article critically discusses the future of work and argues that the ‘new' world of work simply repeats asymmetrical power relations and inequalities that characterise work activities, with the potential of exacerbating even further disparities, inequalities and precarity.

The rapid development of machine-learning algorithms, which underpin contemporary artificial intelligence systems, has created new opportunities for the automation of work processes and management functions. While algorithmic management has been observed primarily within the platform-mediated gig economy, its transformative reach and consequences are also spreading to more standard work settings. Exploring algorithmic management as a sociotechnical concept, which reflects both technological infrastructures and organizational choices, we discuss how algorithmic management may influence existing power and social structures within organizations. We identify three key issues. First, we explore how algorithmic management shapes pre-existing power dynamics between workers and managers. Second, we discuss how algorithmic management demands new roles and competencies while also fostering oppositional attitudes toward algorithms. Third, we explain how algorithmic management impacts knowledge and information exchange within an organization, unpacking the concept of opacity on both a technical and organizational level. We conclude by situating this piece in broader discussions on the future of work, accountability, and identifying future research steps.