When Hannah Arendt (1958/1998) wrote in the aftermath of Sputnik’s launch, she warned that a world premised on the promise of technological liberation from labour risked producing “a society of laborers … [that] does no longer know of those other higher and more meaningful activities for the sake of which this freedom would deserve to be won” (p.5). For Arendt, human emancipation lay not in the relentless optimisation of labour through technology, but in world-changing work and collective, purposeful action. Seventy years on, her warning resonates with renewed urgency. Artificial intelligence (AI), and Generative AI (GenAI) and Agentic AI in particular, is reshaping how labour and work are organised, evaluated, and experienced at a pace and scale that few previous technologies have matched, and it is doing so in ways that raise fundamental questions about what work means for the human condition. Indeed, if we follow Arendt’s line of thinking, we can already see how AI can automate certain labour tasks, but simultaneously it can degrade the conditions for genuine work, action, and creativity in the workplace.
Public discourse on AI and work oscillates between euphoria and alarm. The prospect of a “post-work world” has captured popular imagination, epitomised by Sam Altman’s vision of “a billion-dollar company staffed by just one person” (Sawers, 2025). On the dystopian end, Yuval Noah Harari (2017) predicts that “99 percent of human qualities and abilities are simply redundant for the performance of most modern jobs.” Yet beneath these headline narratives, the empirical picture is considerably more ambiguous.
As a discipline that has long championed sociotechnical approaches to understanding the interplay (or intraplay) of technology and people, Information Systems (IS) is advantageously placed to move beyond technological determinism and to offer nuanced, evidence-based insights into the dynamics of AI use in the workplace. The IS tradition asks not merely what AI can do, but what kinds of organisational governance, power structures, worker identity, and worker voice AI makes possible or forecloses. In the spirit of Arendt’s call for engaged, critical action, this special issue invites the IS community to contribute clear-headed, theoretically informed perspectives to the evolving discourse on AI and the future of work.
Despite emerging evidence of AI-induced productivity gains, micro-level analysis of how AI technologies are powered, experienced and used by workers and organisations remains limited. For example, Gray and Suri (2019) have drawn our attention to ‘ghost work’, and the data labellers, content moderators and annotators (many in Global South countries) whose cognitive and emotional — but also invisible — labour make AI systems function. IS research to date has not sufficiently engaged with how AI externalises and outsources such labour onto Global South populations. In addition, AI continuously penetrates healthcare, social work, and education, which are historically sectors that are undervalued, and often gendered, with women engaged in care labour, whereas IS research tends to privilege knowledge work and professional settings significantly different from this form of labour. Here, we see an opportunity for IS research to engage more deeply with care and invisible labour, in a way that is genuinely concerned with the human condition, and the relational dynamics that underpin our technological futures (Zamani et al., 2026).
The AI’s impact on workplace inequality is also contested in the extant literature: some research suggests that AI differentially affects categories of tasks and occupational roles in ways that could exacerbate existing disparities (Karunakaran et al., 2025). Others contend that AI lowers barriers for lower-skilled workers, opening pathways for upskilling and professional growth that may ultimately narrow inequality (Brynjolfsson et al., 2025). Resolving such debates requires precisely the kind of theoretically grounded, empirically rigorous enquiry that the IS discipline is positioned to provide.
IS scholars have consistently brought critical and contextual lenses to bear on transformative technologies. From pioneering studies of the power and politics of MIS implementation (Markus, 1983) to more recent scholarship on algorithmic management of platform work (Tarafdar et al., 2023; Zheng & Wu, 2022), the IS discipline has accumulated a rich understanding of how “technology in use” continuously reconfigures work practice. However, few technologies in recent decades have posed such fundamental challenges to work as we know it. Unlike earlier generations of rule-based information systems, contemporary AI technologies built on large language models (and, imminently, “world models”) are not static artefacts: they learn, adapt, and evolve with minimal human intervention. This introduces profound theoretical and empirical challenges for IS researchers concerned with material and human agency (Andraschko & Weritz 2026), how power dynamics play out “at the human-machine-institution interface” (Leonardi, 2023).
This special issue invites submissions that build on the IS discipline’s strong sociotechnical tradition and that engage critically with AI practices as they emerge in workplaces. We welcome theoretical and empirical work that focuses on how AI is transforming the human-machine-institution interface, employees’ lived experience of working with AI tools, the new sociomaterial relations that arise, as well as the voice, subjectivity, and agentic action of the workers within these contexts.