Guest editorial: Artificial intelligence, remote work, digital transformation, human resource management practices: rethinking organisational effectiveness

When we launched the call for papers for this Special Issue in June 2025, the conversation about artificial intelligence (AI) and the future of work was already moving faster than most organisations, policymakers, or researchers could track. Generative AI technologies, having entered public consciousness with the release of ChatGPT in late 2022, were by 2025 reshaping task boundaries across knowledge-intensive industries, transforming recruitment, performance management and learning and development functions, and accelerating a wider reconfiguration of what organisations expected from their employees and their HR functions (Budhwar et al., 2023; Chowdhury et al., 2024; Malik et al., 2023). One year later, as we write this editorial in June 2026, the pace has not slowed. If anything, it has accelerated. What seemed like a rapidly approaching future in 2025 is now the operational reality for millions of workers and the organisations that employ them.

This context lends the papers assembled in this Special Issue an urgency that goes beyond the academic. The questions they address, how AI reshapes HRM practices, how employees build resilience in AI-mediated environments, how organisations develop the capabilities to commercialise technology without losing sight of people, how remote and hybrid work interact with career sustainability and generational expectations, are not abstract scholarly puzzles. They are the defining organisational challenges of this moment. And yet, as our call for papers noted, they have tended to be studied in disciplinary silos: AI researchers examine automation and algorithmic decision-making (Budhwar et al., 2023; Malik et al., 2023); HRM scholars study people practices and performance (Boxall and Purcell, 2022); remote work researchers focus on flexibility and wellbeing (Doargajudhur et al., 2026; Zapata et al., 2024). However, integrated research that examines how these forces act together to reshape organisational effectiveness remains limited (David et al., 2024; Jarrahi, 2018). That integration is what this Special Issue sets out to provide.

The response from the international research community exceeded our expectations. The volume and quality of submissions we received reflect the depth of scholarly appetite for exactly this kind of synthesis. Given the number of high-quality papers accepted, this Special Issue will be published in two parts. The present editorial introduces Part 1, which brings together fourteen contributions spanning bibliometric analysis, qualitative inquiry, structural equation modelling, mathematical modelling, mixed-method designs and predictive machine learning. These papers speak from twelve national contexts across Europe, Asia, Africa and the Middle East, and engage with workers and organisations across industries as varied as financial services, hospitality, IT, construction, banking, retail, public services and higher education. It is, we believe, one of the most internationally diverse and methodologically rich collections yet assembled on these themes in a single journal issue.

Collectively, the papers in this Special Issue show that organisational effectiveness in the AI era depends less on technology adoption alone and more on the alignment between human capability, HR governance, leadership practice, employee wellbeing and digital work design. The contribution of the Special Issue is therefore not simply to examine AI, remote work, digital transformation and HRM as separate developments, but to show how these forces interact to reshape work, careers, organisational capability and employee experience. In doing so, the collection moves the debate beyond questions of whether AI and digital technologies improve performance, towards a more nuanced understanding of when, how and for whom they create organisational value.

The editorial is structured as follows. Section 2 situates the Special Issue within its theoretical and empirical context. Section 3 reviews the fourteen articles published in this first part, organised into four thematic clusters. Section 4 proposes directions for future research. Section 5 offers concluding remarks.

Research at the intersection of AI, HRM and digital transformation has expanded rapidly, but it remains, in important respects, fragmented. Considerable progress has been made on questions of recruitment automation, algorithmic performance management and people analytics (Budhwar et al., 2023; Malik et al., 2023); on the effects of remote and hybrid work on communication, trust and employee wellbeing (Doargajudhur et al., 2026; Peeters et al., 2020; Reiche, 2023); and on how organisations build dynamic capabilities to sense, seize and reconfigure resources in the face of technological disruption (Teece, 2007). Yet the mechanisms through which these forces interact, and their combined effects on people, HRM practices and organisational effectiveness across different industrial and national contexts, remain far less well understood. This Special Issue responds to that gap by positioning organisational effectiveness as an outcome of socio-technical alignment: the fit between digital technologies, HRM systems, employee capabilities, leadership practices and the wider conditions under which work is organised.

The need for integration is not merely academic. As organisations simultaneously adopt generative AI, intelligent automation and digital HR platforms alongside flexible and hybrid work arrangements, they face a set of challenges that no single disciplinary lens can adequately capture. Questions of competency redefinition, human-AI task allocation, leadership in distributed teams, employee resilience under cognitive load and long-term career sustainability are simultaneously psychological, strategic and societal in nature. They connect directly to several United Nations Sustainable Development Goals, including SDG 3 (Good Health and Well-being), SDG 4 (Quality Education), SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure) and SDG 10 (Reduced Inequalities), which run as threads through many of the papers in this collection (Einola and Khoreva, 2023).

The fourteen papers assembled in Part 1 bring a wide range of theoretical lenses to these questions, drawing on the job demands-resources model, dynamic capabilities theory, socio-technical systems theory, the resource-based view, career construction theory, adult learning theory, social exchange theory and comparative advantage theory. Methodologically, the collection is equally diverse. It includes quantitative studies using structural equation modelling (Abas and Qureshi, 2026; Gunnoo et al., 2026; Jyoti et al., 2026; Malik et al., 2026; Nanjundeswaraswamy et al., 2026; Nazir et al., 2026), qualitative designs employing thematic and narrative inquiry (Atallah et al., 2026; Barbosa and Real de Oliveira, 2026; Cayrat and Boxall, 2026; Huzooree, 2026), mixed-methods approaches combining grounded theory with SEM and predictive modelling (Bhardwaj et al., 2026; Pasi et al., 2026), a conceptual model grounded in mathematical modelling and simulation (Chang, 2026) and a bibliometric and systematic review (Asemota, 2026). Geographically, empirical data are drawn from the United Kingdom, France, Canada, India, Pakistan, Mauritius, Iran, Portugal, Saudi Arabia and Taiwan, reflecting a genuinely global engagement with the themes of this Special Issue.

Figure 1 presents a keyword map drawn from the articles published in this first part of the Special Issue, offering a visual representation of the thematic landscape the collection covers.

The fourteen articles are organised into four thematic clusters: (1) mapping knowledge structures and frameworks for human-AI collaboration, (2) HR strategy and organisational capabilities in digitally transforming contexts, (3) AI, employee wellbeing, resilience and cognitive experience and (4) remote work, digital reskilling and career sustainability. Within each cluster, papers are discussed in relation to one another to draw out the conversations they sustain and the cumulative contribution they make.

Two papers open the collection by mapping existing knowledge and developing frameworks for understanding human-AI collaboration, providing intellectual anchors for the Special Issue as a whole. The first draws on bibliometric methods to chart the intellectual structure of the field; the second introduces a mathematical model for optimising human-AI task allocation. Asemota (2026) presents a rigorous bibliometric and systematic review of competency management research in the age of AI, drawing on 198 peer-reviewed articles retrieved from the Scopus database. Five overarching themes emerge: the rise of AI-focused managerial skills; AI's role in augmenting both strategic and routine capabilities; dynamic managerial capabilities for AI-enabled HRM; selective skill substitution, augmentation and resilience in the AI era; and organisational design, power and wellbeing implications. A striking finding is that competency management is shifting from static, role-defined models toward dynamic, human-centred and technology-augmented capability systems. This has profound implications for how talent development functions are designed and how organisations build learning pathways in conditions of ongoing disruption.

Chang (2026) complements this landscape mapping with a conceptual and mathematical model grounded in Ricardo's comparative advantage theory, developed to optimise the allocation of tasks between humans and AI. Validated through simulation using data from semiconductor processing technicians, the model demonstrates that strategic alignment of human and AI capabilities through comparative advantage principles yields 63% higher productivity compared to random task assignment approaches. The paper makes a novel interdisciplinary contribution by importing classical economic theory into the HRM domain, and provides practitioners with actionable tools for workforce planning, training needs assessment and human capital investment in AI-integrated work environments. These two papers establish that navigating human-AI collaboration effectively requires both a reconceptualisation of organisational competency frameworks and a principled analytical approach to allocating tasks between humans and machines.

The two papers in this cluster establish an important foundation for the Special Issue: effective human-AI collaboration requires organisations to rethink both what capabilities matter and how work should be distributed between humans and intelligent technologies. Asemota's review shows that competency management is moving away from static role-based models towards more dynamic, technology-augmented and human-centred capability systems. Chang's model then demonstrates that these capabilities must be operationalised through principled task allocation rather than ad hoc adoption of AI tools. Collectively, these papers suggest that AI-enabled organisational effectiveness begins before implementation. It depends on how organisations define human value, identify complementary machine capabilities and design work systems that preserve human judgement while leveraging technological efficiency.

Four papers examine how HRM functions and organisational capabilities are being reconfigured in response to digital transformation and AI adoption, spanning qualitative, quantitative and mixed-method approaches across four national contexts. Cayrat and Boxall (2026) present a qualitative multi-case study of five large financial services organisations in France and Canada, examining how AI adoption is reshaping the HR function in practice. Drawing on interviews with HR analytics leaders, HR professionals and line managers, the study identifies two contrasting trajectories of AI-HR interaction. The first, which the authors call the “AIHR” model, prioritises automation and cost-minimisation and carries real risks of deprofessionalising HR and compromising employee wellbeing. The second, the “HAIR” model, places human-centred values at its core, with HR specialists playing a deliberate shepherding role to ensure that AI is implemented responsibly and its unintended consequences are contained. A key practical implication of the HAIR model is the need for HR professionals to develop what the authors term “fusion skills”, combining data science literacy with deep human and organisational knowledge and to implement a formal AI-HRM strategy encompassing governance, training, communication and stakeholder engagement. This distinction has considerable practical significance: it suggests that the future of the HR profession is not determined by AI itself, but by the choices organisations make about how to deploy it.

Nazir et al. (2026) draw on the resource-based view to investigate how IT firms in Pakistan design and implement HRM practices integrating talent retention, diversity and inclusion and AI-driven tools. Through hybrid semi-structured interviews with 16 HR professionals across eight organisations of varying sizes in four Pakistani cities, the study identifies three interconnected themes: talent retention through promotional policies, personalised recognition and trust-based management; diversity and inclusion via blind recruitment, culturally aware leadership and cultural celebrations; and technological integration deploying HRIS platforms and AI tools such as IBM Watson and HireVue, while prioritising ethical governance. Organisations that integrated all three practices synergistically achieved superior employee engagement and organisational performance. The study extends the resource-based view by demonstrating that strategically aligned HRM can generate valuable, rare, inimitable and non-substitutable assets even in environments characterised by brain drain, sociocultural barriers and weak governance frameworks.

Jyoti et al. (2026) examine how hybrid HRM relates to employee performance in Indian SMEs in the IT and manufacturing sectors, focussing on the mediating role of ambidextrous learning. Using PLS-SEM with data from 136 employees and managers, the study finds that ambidextrous learning, the simultaneous development of explorative and exploitative capabilities, partially mediates the hybrid HRM-performance relationship. Age moderates this effect significantly: younger employees under 35 show a substantially stronger path coefficient (0.631) than older employees (0.317), pointing to the importance of designing HR strategies that are sensitive to workforce demographics and generational diversity.

Bhardwaj et al. (2026) extend organisational capabilities thinking into the domain of technology commercialisation. Using a sequential exploratory mixed-methods design combining grounded theory development with structural equation modelling and drawing on data from 400 Iranian professionals in technology commercialisation contexts, the study develops and validates an organisational ambidexterity model in which ambidexterity functions as a dynamic capability driven by micro-foundational factors including multifaceted management, leadership orientation and HR integration. A notable finding is that contextual conditions do not directly affect the strategies organisations employ, validating the mediating role of ambidexterity as a higher-order capability. The findings show that technology commercialisation strategies enhance value creation and facilitate paradox management, offering a governance roadmap for organisations seeking to balance operational efficiency with innovation adaptability under conditions of temporal uncertainty and network complexity.

Across these four papers, a shared message emerges: digital transformation becomes organisationally effective only when it is embedded within coherent HR strategy, governance and capability development. The papers move the discussion beyond AI as a tool for automation and show how HRM practices shape whether digital technologies strengthen or weaken organisational capability. Cayrat and Boxall's distinction between automation-first and human-centred AI-HRM is especially important here, as it frames digital HR transformation as a strategic and ethical choice rather than an inevitable technical pathway. The studies by Nazir et al. (2026), Jyoti et al. (2026) and Bhardwaj et al. (2026) further show that retention, inclusion, learning and ambidexterity are not separate HR concerns but interdependent mechanisms through which organisations build resilience and adaptability. Collectively, this cluster positions HRM as a central actor in governing digital transformation, rather than a function merely being transformed by it.

The third and largest cluster engages with how AI adoption shapes the psychological experience of employees: their cognitive load, resilience, wellbeing and vulnerability to burnout. These papers collectively push back against any straightforward equation of AI adoption with performance improvement, revealing instead a more conditional and contingent picture in which outcomes depend critically on individual dispositions, managerial support and organisational culture. Nanjundeswaraswamy et al. (2026) investigate the mediating role of employee wellbeing in the relationship between AI tool usage and employee performance in hybrid work environments, drawing on data from 465 IT employees in Bengaluru, India. Using exploratory factor analysis, confirmatory factor analysis and Hayes' PROCESS macro for mediation analysis, the study demonstrates that AI tool usage has a strong positive effect on both employee wellbeing and performance, with wellbeing in turn significantly predicting performance and partially mediating the AI-performance relationship. The practical implication is clear: organisations that deploy AI tools without corresponding investment in employee wellbeing capture only a fraction of the performance benefits that AI can deliver.

Abas and Qureshi (2026) examine the same relationship from a dispositional perspective, exploring how the perceived value of AI shapes cognitive load reduction and task performance efficiency among hotel industry employees, with employee resilience as a mediator. Grounded in Conservation of Resources theory, and using PLS-SEM, the study finds that when employees perceive AI as genuinely valuable, it reduces cognitive demands and enhances task performance efficiency, with resilience partially mediating both relationships. This finding contributes an important dispositional perspective: the capacity to benefit from AI in the workplace is not automatic but depends in part on the psychological resources employees bring to their interactions with AI systems, a finding with direct implications for how organisations approach AI training and onboarding.

Atallah et al. (2026) take a qualitative approach, drawing on thematic analysis of 27 semi-structured interviews with managers across banking, IT and higher education in France and Lebanon. Extending the job demands-resources model, the study explores how individual adaptability shapes the link between perceptions of technology demands and resources and burnout. The key finding is a new dimension of adaptability, the ability to handle physical and mental workspaces simultaneously, that has become newly salient in hybrid and AI-mediated environments. The study proposes a revised JD-R matrix of burnout in terms of adaptability, offering managers a practical diagnostic tool for identifying burnout risk in technology-intensive settings.

Malik et al. (2026) contribute an important perspective that is often overlooked in the AI-and-work literature: the role of supervisory behaviour in digitally saturated environments. Examining boss phubbing, the practice of supervisors ignoring employees in favour of their smartphones, across 552 faculty members in Pakistani technology-driven universities and drawing on social exchange theory and PLS-SEM, the study finds that workload, stress and lack of empathy all drive boss phubbing, which in turn produces office clutter, employee silence and deteriorating mental health among staff. The paper makes a novel contribution by connecting boss phubbing to the physical environment of work through the office clutter link, a relationship not previously examined in the literature and by situating the analysis within higher education settings where supervisory communication is foundational to organisational effectiveness.

The papers in this cluster collectively challenge technologically deterministic accounts of AI adoption. Rather than assuming that AI improves performance by default, they show that its effects are mediated by employee wellbeing, resilience, perceived value, adaptability and supervisory behaviour. This is an important contribution because it shifts the focus from technology implementation to the lived experience of employees working in AI-mediated environments. AI tools may reduce cognitive load and support performance, but only when employees perceive them as valuable, have sufficient psychological resources and work within supportive organisational climates. Conversely, digitally saturated workplaces can intensify stress, silence, burnout and relational disconnection when managerial behaviour and work design are poorly aligned. These studies show that AI-enabled organisational effectiveness is not only a technical or productivity issue; it is also a question of psychological safety, employee agency, leadership quality and human sustainability.

The fourth cluster addresses the intersection of remote and hybrid work, digital skills development, generational experience and long-term career sustainability. These papers engage the most expansive temporal and social canvas of the Special Issue, moving from the immediate experience of work to questions about how careers are built and sustained in AI-mediated environments over time. Pasi et al. (2026) develop and empirically operationalise an AI-remote work career sustainability model (ARW-CSM) using data from 350 Indian professionals across multiple industries, combining descriptive analysis, two-way ANOVA, multiple regression, moderated regression and Random Forest predictive modelling. A striking finding is that group-level differences across AI usage and work modes were minimal, indicating broad normalisation of AI-supported work environments. Furthermore, none of the determinant clusters significantly predicted AI adoption behaviour, suggesting that adoption is shaped more by job design and organisational mandates than by individual or contextual readiness. Productivity outcomes varied significantly by career stage, with mid- and senior-career employees deriving greater benefits from AI-augmented remote work. Feature importance analysis identifies AI engagement, training intensity and human capital variables as the strongest predictors of long-term career sustainability, pointing to the value of targeted, career-stage-sensitive upskilling strategies.

Barbosa and Real de Oliveira (2026) approach digital reskilling through qualitative narrative inquiry, drawing on life-history interviews, reflective journals and HR documentation from 30 employees across finance, retail and public service sectors in Portugal participating in AI-driven reskilling programmes. Grounded in adult learning theory, career construction theory and dynamic capabilities theory, the study introduces the concept of “reskilling identity work” to capture how employees reconcile existing skill repertoires and professional identities with new digital expectations. Three patterned responses emerge: preservation, bridging and transformation, each reflecting a distinct way in which employees protect, translate, or reconfigure their professional selves. A key finding is that career adaptability and a supportive, power-sensitive learning culture are critical in determining whether reskilling initiatives foster genuine identity renewal or defencive resistance. The study challenges organisations that treat reskilling as a purely technical matter, demonstrating that genuine capability renewal requires attending to identity transitions and psychological empowerment alongside skill acquisition.

Gunnoo et al. (2026) examine how work-life balance, perceived organisational support for remote work, digital readiness and job security perception shape job satisfaction among 330 Generation Z employees in Mauritius. Drawing on JD-R theory and PLS-SEM, the study finds that work-life balance significantly improves job satisfaction, with perceived organisational support emerging as a strong predictor of both work-life balance and job satisfaction. Job security perception significantly impacts both variables, underscoring the importance of psychological safety in digital workplaces. Digital readiness has a positive but comparatively weaker effect on work-life balance, suggesting that technical competence alone does not resolve the structural and relational dimensions of satisfying work. By positioning JD-R theory within a small-island developing economy and foregrounding generational perspective, the paper makes a contextually distinctive contribution to a literature that has drawn its conclusions disproportionately from large Western economies.

Huzooree (2026) completes this cluster with a qualitative study of e-leadership and human-AI collaboration in project-based teams, drawing on 34 semi-structured interviews with project managers across five UK industries. Data were analysed using a Gioia-informed thematic approach to identify how e-leadership practices interact with varying orientations of AI integration. The study identifies a curvilinear pattern of bounded augmentation, in which team effectiveness peaks in a zone of balanced AI use but declines under both under-use and over-reliance. This trajectory is shaped by e-leadership practices: proactive engagement combined with creation-oriented AI use generates the highest effectiveness, while reactive approaches produce breakdowns in performance, trust and accountability. The resulting 2x2 e-leadership-AI orientation matrix maps how social subsystems, including leadership engagement, trust, ownership, mediation and alignment, and technical subsystems, including automation, creation, reliability and integration, combine to enable or erode team effectiveness. The study offers a practical tool for leaders and organisations navigating the socio-technical complexity of AI-integrated project work.

The papers in this cluster extend the Special Issue's focus from immediate work outcomes to the longer-term sustainability of careers and capabilities in AI-mediated workplaces. They show that remote work, digital reskilling and human-AI collaboration are not simply operational adjustments but career-shaping processes. Employees experience these changes differently depending on career stage, generational position, identity, organisational support and leadership quality. The cluster therefore highlights the need to understand digital transformation as a temporal process: one that affects how workers build confidence, maintain employability, reconstruct professional identity and sustain meaningful careers over time. Collectively, these papers show that organisational effectiveness in the AI era depends not only on adopting new technologies, but also on creating work systems that enable people to adapt, develop and remain employable across changing digital conditions.

The contributions in this Special Issue advance our collective understanding considerably, and in doing so they also illuminate the questions that remain open. Rather than treating the four themes of the collection as separate research agendas, we propose five cross-cutting questions that should guide future research on AI, remote work, digital transformation, HRM and organisational effectiveness.

First, how should organisations govern AI-enabled HRM responsibly? The papers in this Special Issue show that AI adoption in HRM is not simply a technical matter, but a question of values, accountability and organisational choice. Future research should examine how organisations design governance systems that ensure transparency, reduce bias, protect employee wellbeing and maintain meaningful human oversight in AI-supported people management. More work is also needed on the institutional, professional and cultural conditions that enable human-centred AI-HRM to be sustained over time.

Second, how do employees experience AI over time? Much existing research, including several studies in this collection, relies on cross-sectional evidence. Future studies should use longitudinal, diary and experience-sampling designs to examine how employee perceptions, cognitive load, trust, resilience, wellbeing and burnout risk evolve as AI tools become embedded in everyday work routines. This would help move the field beyond initial adoption effects towards a deeper understanding of adaptation, habituation, resistance and long-term human sustainability.

Third, what capabilities do HR professionals and line managers need in human-centred AI systems? The Special Issue points to the growing importance of fusion skills, digital literacy, ethical judgement, data-informed decision-making and socio-technical leadership. Future research should examine how these capabilities are developed, how they differ across organisational levels and sectors and how HR professionals can retain strategic influence rather than being displaced by automation-first approaches to digital transformation.

Fourth, how do AI and remote work reshape inequality across career stage, age, geography and occupation? The papers in this collection show that employees do not experience AI-enabled and remote work in the same way. Future research should examine who benefits from these changes, who is disadvantaged and how inequalities are reproduced or reduced through digital work design. Greater attention should be given to early-career workers, older workers, workers in emerging economies, small-island developing states and occupations where AI adoption is driven more by organisational mandate than by individual readiness.

Fifth, what forms of leadership sustain effective human-AI collaboration? The findings in this Special Issue suggest that human-AI collaboration is most effective when leadership supports trust, accountability, learning and balanced technology use. Future research should therefore examine the leadership behaviours, team processes and organisational conditions that prevent both under-use and over-reliance on AI. Particular attention should be paid to project-based, hybrid and distributed teams, where digital coordination, human judgement and technological dependence intersect most visibly.

Together, these questions suggest that the next stage of research should move beyond asking whether AI, remote work and digital transformation improve organisational effectiveness. The more important question is when, how and for whom these changes create sustainable value. Addressing this question will require research designs that are longitudinal, comparative, multi-level and attentive to both technological systems and human experience.

We launched this Special Issue in June 2025 with a conviction that the scholarly community needed a platform to examine how AI, remote work, digital transformation and HRM practices are jointly reshaping organisational effectiveness. One year later, the fourteen papers assembled in Part 1 vindicate that conviction. Taken together, they tell a story that is more nuanced, more contextually contingent and more human than either the optimistic narratives of technological progress or the pessimistic narratives of displacement and control tend to allow. AI can reduce cognitive load and enhance performance, but only when employees have the resilience and perceived value to engage with it productively. Reskilling programmes can build new capabilities, but they work best when they attend to identity as much as to skill. Human-AI collaboration can generate team effectiveness, but only in a bounded zone of balanced use that requires active and thoughtful leadership to sustain. These are not just research findings. They are practical lessons for every organisation navigating this moment of profound transformation.

We are deeply grateful to the authors for the intellectual ambition, rigour and patience they have brought to this collection. We thank the many reviewers whose constructive and sustained engagement raised the quality of every paper included here. Our sincere gratitude goes also to the Editorial Board and staff at JOEPP and to Emerald Publishing for their professionalism and support throughout. We hope that readers, whether scholars, practitioners, or policymakers, will find in these pages both new knowledge and new questions worth pursuing and we look forward to presenting the second part of this Special Issue in due course.