Guest editorial: Taking stock and charting the future: the management and implication of generative artificial intelligence (AI) for the changing workplace (Part-1)

Responsible for the unprecedented power to generate novel patterns or new content that resembles human-created output, artificial intelligence (AI) as a technology is not just improving and becoming more accessible; it has become pervasive and increasingly leveraged and deployed across a wide variety of domains and applications (Ghosh et al., 2019). In this regard, the enormous promise of generative artificial intelligence (GAI) to transform organizations and organizing has come to dominate contemporary discourse on our changing workplace. The upshot is the adoption of creative AI models and tools (e.g. ChatGPT and legal robots), which, over time, have improved to become incredibly powerful, with trailblazing predictive capabilities that are revolutionizing decision-making in many workplaces (Duan et al., 2019; Shrestha et al., 2019). Thus, from the anodyne activity of translating documents to the transformative control of spacecraft and medical diagnosis, AI is a transformative technology that is revolutionizing how we interact, solve problems, make decisions and work effectively. Claims to new use cases for generative AI for human flourishing and organizing is on the ascendency. Notably, AI is transforming the organization of research and knowledge production (Grimes et al., 2023; Schlagwein and Willcocks, 2023; Weber, 2024), the nature of white-collar work (Vinsel, 2023), productivity capture (Korinek, 2023) and much more.

Existing evidence suggests there is still a vast (un)tapped potential to make sure new AI tools are leveraged to transform the changing workplace for the better (Haenlein and Kaplan, 2019; Ooi et al., 2023). To this end, many organizations are now in the process of redesigning their workplaces around the technology. This demands a shift from attention on the nascent AI cold start problems (Vomberg et al., 2023; Zhang et al., 2021) to making choices that border on the creation and capture of value from the ongoing AI transformation. Much about the technology still needs ironing out to the extent that we know little as to what will happen to organizations and the effectiveness and productivity of individuals and groups in the workplace when AI comes to dominate the way work is organized, done, and managed. Like previous waves of technologies, AI promises to do away with menial or dull work, but technological and cultural myths about its future (Bewersdorff et al., 2023; de Saint Laurent, 2018) and growing concerns that it could potentially replace people and remove or gigify (Braganza et al., 2021) millions of jobs remain a potent threat more than hitherto (Raisch and Krakowski, 2021; Sharma, 2020). In response, there has been a surge in scholarly interest in the relationship between people and AI, with very little attention being paid to how people will actually treat others in organizations in the presence of AI (Glikson and Woolley, 2020; Livingston and Risse, 2019). For better or worse, the broader and potential social effects of AI (Zimmerman et al., 2023) could challenge the orthodoxies of conventional organizing and the tools needed to effectively manage people at the workplace. In this regard, AI's potential to obscure, for example, moral responsibility and encourage unethical interactions between managers and employees in AI-mediated workplaces remains a palpable threat (Méndez-Suárez et al., 2023; Sullivan and Fosso Wamba, 2022). Also remaining unresolved are the unintended consequences of AI on employee performance and the lingering ethical and governance concerns about the technology (Varma et al., 2023). AI in the workplace can help improve performance by increasing managerial oversight and control over employees. While such performance improvements may be perceived as valuable organizational outcomes, the deployment of AI can have an unwelcome impact on employee data privacy, security and well-being (Kim and Bodie, 2021). Reconciling and understanding the vicissitudes of similar paradoxes and tensions has become all important as AI becomes more present in new workplaces.

Clarion calls to dealing with the unpredictable consequences of AI adoption is deafening, with emphasis on the dark side of the technology eclipsing many of the technology’s brighter promises (Belanche et al., 2024). In this regard, the race to explore and exploit the possibilities, potentialities and limits of AI has brought the ethics of transparent usage of AI to the fore. This has culminated in a plethora of ethical issues that have the potential to stifle the evolution of GAI technologies in ways that could slow the development of new applications (McMillan, 2023). Of particular concern are the growing fears and anxieties over data breaches, privacy apprehensions, protection of intellectual and digital assets and the intractable and well-known “black box” problem of “hallucination” syndrome – AI synthesizing disparate information or datasets to produce persuasive outcomes that are false or factually inaccurate. Detailed accounts of the potential implication of AI on work and the workplace, however, go beyond these ethical concerns. Uncertainty remains about AI’s far-reaching potential to disrupt regulatory regimes and how work itself is organized. In essence, both exciting and challenging, we are at crossroads, where the impact of the emergent forms, business models and applications of AI on the changing workplace is outpacing our ability to adapt. In particular, current theories and conventional methodologies are simply not adequate for explaining or studying the effects, possibilities, potentialities and their consequences of the emergent forms and applications on the changing workplace.

Thus, this special issue aims to take stock and chart the future of (un)known possibilities and potentialities of AI for the changing workplace. With a view to extending our understanding of the opportunities, potentialities and limits of AI on the behaviour of employees in the workplace, how they are managed and their potential implications for positive organizational outcomes such as productivity, creativity and strategic thinking. The rigorous contributions contained in this special issue adopt interdisciplinary or multilevel approaches and include conceptual and theoretical papers, state-of-the-art reviews and empirical research applying quantitative or qualitative methods to exploring the management and implication of GAI in the changing workplace. The unifying focus of these papers are the novel theories they develop on GAI by challenging orthodoxies of organizing in the present and yet-to-be-realized future of changing workplaces. Table 1 below summarizes the conceptual lenses and key findings of papers in this special issue.

In the first paper titled “Humanizing GenAI at work: bridging the gap between technological innovation and employee engagement”, Manresa et al. (2025) explores the influence of GAI on employee performance in the workplace. Developing their contribution in the context of companies that have embraced GAI in Spain, the study reports significant enhancement in employee engagement and performance as a function of improved attitudes and trust towards the adoption of GAI.

Asante et al. (2025), in their paper “On the consequences of AI bias: When moral values supersede algorithm bias”, argue that developing their contribution in a context marked by underdeveloped markets and weak institutions, they integrate social identity theory, norm activation theory and justice theory, to explore how a collective organizational identity, perceptions of justice and personal values reinforce positive reactive responses towards AI bias outcomes. They found that those managers who regarded their response to AI bias as a personal moral duty felt a strong sense of guilt towards the unintended consequences of AI logic and reasoning. They conclude that managers who perceived the processes that guide AI algorithms’ reasoning as discriminating showed a high propensity to address this prejudicial outcome.

In the paper entitled “Artificial intelligence in the workplace – challenges, opportunities and HRM framework: a critical review and research agenda for change”, Mendy et al. (2025) examine how managers deploy AI systems to address underperformance in practice. Their study does so by synthesizing emerging literature at the intersection of AI–HRM–managerial decision-making to develop a heuristic framework that could be employed to strategically manage organizational performance interventions in the future workplace.

The paper by Siaw and Ali (2025), “Substitution and complementarity between human and artificial intelligence: a dynamic capabilities view”, draws on DC as a meta-theoretic lens to examine. The paper develops a conceptual framework that explicates the mechanisms through which human intelligence (HI) and AI may substitute and complement each other for organizational knowledge management (KM) within the contingencies of accountability and ethical demands.

Shoa et al.’s (2025) paper, “Unveiling the potential: exploring the adoption of GenAI and its impact on organizational outcomes”, explores the dynamics around the adoption of GAI in India’s retail industry to unpack the dynamic relationships between the antecedents of GAI adoption and its impact of adoption on employee efficiency, business value creation and firm performance and productivity. Emphasizing the positive influence of GAI on employee efficiency, the study found that top management support, openness to innovation and competitive pressure positively impact the adoption of GenAI.

Zhao et al.’s (2025) paper titled “Technophobia and the manager’s intention to adopt generative AI: the impact of self-regulated learning and open organisational culture”, draws on the cognitive-affective-normative (CAN) model, to explore links between technophobia and a manager’s intention to implement GAI in practice. Going beyond the technical to focusing on the “human-side” of GAI adoption, the study found that technophobia is negatively related to a manager’s intention to adopt generative AI, while SRL is positively related to the intention to adopt generative AI. Nevertheless, SRL reduces the negative impact of technophobia on AI adoption.

The penultimate paper by Amankwah-Amoah and Appiah (2025) titled “Unmasking the silent threat: AI-induced human capital obsolescence and business failure” examines how AI shapes the process of human capital obsolescence (HCO) in different types of organizations, leading to different business failure pathways. They do this by drawing on human capital theory and insights on AI advancement and its impact on employee knowledge, skills and abilities to develop a four-domain framework that organizes AI-induced HCO to explicate how AI could trigger the obsolescence of individuals’ knowledge, skills and abilities in different organizations.

The paper by Aziz et al. (2025), “AI-powered Leadership: A Systematic Literature Review”, explored the emerging relationship between AI and leadership, focusing on what has come to be defined as AI-powered leadership and identified a range of key challenges in AI-powered leadership, including ethical dilemmas, complications in human-AI interactions, hurdles in AI implementation within leadership contexts and longer-term risks associated with AI integration.

The world is reacting with both optimism and trepidation to the rapid deployment of AI. The potential impact of AI technologies on changing future workplaces, in particular, has opened up new vistas for rethinking how AI could be leveraged to improve productivity, enhance humans and AI systems working as a team, develop agentic AI and interpret meanings in the outputs of AI translation tools and the institutionalization of ethically responsive AI practices. These imperatives, we argue, could be leveraged to improve employability, enhance job satisfaction by reducing the drudgery from everyday organizational work and increase the meaningfulness of the work people do. We delineate these possibilities through three lines of attention: AI speciation, AI mutation and AI relationality.

Speciation: By AI speciation, we refer to the possibility of existing AI technology taking on new forms with unique characteristics and capabilities that are distant from their original use in organizing. Consider the rise of super-intelligent generative AI that could imitate the human prototypes, whose data they were trained on, to generate solutions to support – for instance, managerial or executive decision-making. There is, however, potential for such AI technologies to metamorphosize into rational decision-makers in the near future (Raisch and Krakowski, 2021); yet, their lack of empathy or emotions suggests the decisions they make could lead to far-reaching and devastating outcomes. Following the observation of historian and philosopher Yuval Noah Harari, we surmise that AI, by virtue of its ability to master language, could form intimate relationships with people in their everyday work and use the power of that intimacy to change and, possibly, manipulate people’s views, opinions and projectivities. From this perspective GAI creating new workplace cultural ideas may be exciting, but could take over workplace cultures, begin to produce regulations, exchanges and ethics that threaten global markets and capitalism as a whole.

Mutation: AI is not neutral; it is mutating – currently in flux and transformation. The technology is taking on different forms and being embedded in different “tools” that humans/people use in daily organizing. For instance, we are now experiencing widespread use of large language models such as Claude, ChatGPT and MS Word application software for processing and creating documents (Abril, 2023; Budhwar et al., 2023). If we all use AI-powered MS Word, what will be the longer-term effects on individuals, communities and wider societies? Are we all going to “think” alike? “Write” alike? Will individuals/groups be dominated by North American thought processes because AI algorithms are created by North American developers? What are the effects of bias due to certain populations not using Word (for access reasons, for instance) and their data therefore being excluded in the training of the algos that MS uses? On the other hand, MS Word including AI as a standard feature in translation packages for learning languages and even smarter recommendation engines to bots and robots, could offer widespread improved ways of organizing. While these possibilities and potentialities are positive, they can also be manipulative and have darker sides and uses, e.g. malicious large-scale campaigns, voter manipulation, exploitation of human biases and weaknesses and the appropriation of copyrights.

Relationality: At a time where social tensions, stemming from geopolitical events and systemic inequalities increasing, relationships between people and AI are also unravelling. Aggravating the strain and challenging interpersonal relationships and social harmony, AI is changing the syntactical relations even among the acts of different persons (Mikalef et al., 2022). It is also (re)configuring the everyday relational processes of interaction between and among social identities. Of particular importance is how GAI could help to support, stabilize and maintain the socio-relational fabric that binds people and communities together. In the presence of biases and dispositions, understanding how people will actually treat others in marketplaces, organizations and everyday transient social spaces in the presence of AI becomes imperative (Chowdhury et al., 2023). Within the contingencies of fleeting persons-in-relations, the broader and potential relational effect of AI could revolutionize human interactions in ways that also preserve the indispensable human touch or even reset syntactical social relations and challenge new and emergent applications of AI.

Taken together, the eight (8) papers contained in this special issue are not just varied, eclectic and stimulating; cumulatively, they provide important and novel insights into the strategic management of GAI for workplaces of the future. Each paper contributes to extending our understanding of the possibilities, potentialities and limits of generative AI in reconfiguring the way work is organized and delivered, stimulating new thinking and opening new seams of research about the influence of generative AI on well-being, successful organizing and improving productivity and competitiveness.