Book Review: Augmenting Human Resource Management With Artificial Intelligence: Towards an Inclusive, Sustainable and Responsible Future By Aizhan Tursunbayeva, Cham, Switzerland: Springer, 2024. pp. 151. 135,19 EUR (hardcover)

This timely and rigorous volume offers a theoretically grounded exploration of the growing—and often highly fragmented—body of research on the integration of artificial intelligence (AI) into human resource management (HRM). AI, broadly defined as the ability of machines to learn from data and perform human-like cognitive tasks (Benbya et al. 2020), is increasingly deployed across HRM practices such as recruitment, performance management, training and career development. Organizations adopt these technologies with the aim of enhancing efficiency, accuracy and personalization, given their supposed ability to potentially enable data-driven decision-making and more strategic HRM delivery. Yet, alongside these opportunities, research also highlights significant risks, including the reproduction of bias, threats to fairness and challenges for workplace inclusion (Lazazzara et al. 2025). It is this dual potential of AI—the promises and perils of its application in HRM—that the book engages with. The volume's central question: Can AI in HRM foster a more inclusive, sustainable and responsible future of work?—is both compelling and urgent. Tursunbayeva conceptualizes an inclusive future of work as one in which AI functions as a tool for empowerment rather than exclusion within and across HRM practices: it is employed to counteract bias in recruitment, improve accessibility for diverse workers and personalize learning and career development. A sustainable future of work emerges where AI is harnessed to grasp and advance social and environmental objectives (e.g., equality, well-being and decent work), while ensuring that automation and HRM decisions are assessed considering their long-term social and environmental consequences. Finally, a responsible future of work is envisioned as one where fairness, accountability, transparency and ethics guide the adoption of use of AI systems for HRM, maintaining human agency at the centre of decision-making and ensuring that this digital innovation supports equitable forms of employment. By analysing these three concepts, Tursunbayeva envisions a future development of AI in HRM that not only assists and augments efficiency but also actively contributes to more equitable workplaces. Her framework supports the creation of future workplaces where fairness is ensured and the risk of algorithmic systems reproducing historical biases is mitigated; fosters resilient organizations by aligning AI-enabled HRM practices with long-term organizational and societal goals; and promotes socially accountable employment relations by embedding ethical, transparent and human-centred principles. Throughout the book, Tursunbayeva approaches these issues from multiple angles, drawing on diverse disciplinary perspectives, methodological approaches, as well as real-life examples. Chapter 1 opens with a conceptual introduction to AI in HRM, stressing the importance of adopting a balanced perspective when examining the adoption of this transformative technology. It positions AI as a data-driven technology capable of automating cognitive and decision-making tasks, offering the potential to enhance efficiency and evidence-based management. At the same time, the chapter underscores that without the critical oversight, such systems may replicate and amplify human and institutional biases embedded in historical data, potentially leading to unintended ethical and organizational consequences. Chapter 2 traces the evolution of HRM practices, showing how key HRM practices including recruitment and selection, performance management, training and development and compensation and benefits have developed over time. It highlights the shift from personnel administration to strategic HRM and the growing influence of digitalization, which has enhanced efficiency but also deepened tensions between human-centred and data-driven approaches to managing people. The chapter further provides an overview of the environmental, organizational, technological and individual factors and trends that shape contemporary HRM, and their adoption of AI. Chapter 3 addresses the ambiguities of AI, critically engaging with both the hype and fear narratives surrounding its adoption. The chapter draws on the Future of work literature, distinguishing between automation, where AI replaces human labour, and augmentation, where AI enhances human capabilities. It then connects this debate with the configuration approach in electronic HRM (e-HRM) literature (Bondarouk et al. 2017) that conceptualize e-HRM as a system of interrelated practices and technologies that operate together to achieve distinct organizational outcomes. Therefore, this approach builds the base for exploring the operational, relational and transformational implications of AI in HRM in the following three chapters. Indeed, chapter 4 starts with the former, illustrating how algorithmic management can go beyond automating routine HRM tasks to also reconfiguring organizational control and management practices. These systems, Tursunbayeva argues, echo classic labour process concerns by increasing surveillance and reducing worker autonomy, where the efficiency gains can often come at the cost of power asymmetries within organizations. Furthermore, the chapter highlights how algorithmic management is already employed to perform tasks traditionally reserved for HRM without human intervention, illustrating this with examples from Uber. Chapter 5 explores AI's role in relational HRM, encompassing recruitment, training, performance management and compensation and benefits. Tursunbayeva provides an overview of the pragmatic capabilities of existing relevant AI-for-HRM vendor tools, as they are typically legitimized by their vendors, and illustrates the adoption of AI in each of these practices through brief case studies. Chapter 6 brings together perspectives on diversity, inclusion, sustainability and even neuroscience to argue that AI can augment HRM in ways that support fairer and more resilient organizations, contingent upon rigorous critical evaluation. The chapter considers the implications of AI for diversity and inclusion (D&I) from the perspective that all HRM practices should actively support D&I objectives. These implications are examined across individual, team and organizational levels, while also drawing on the authors' research to identify which diversity attributes AI can grasp. Tursunbayeva then explores the novel insights that AI-enabled and -related neuroscience tools can generate across various HRM practices, and examines how AI-for-HRM can be leveraged across HRM practices to help organizations align their HRM strategies with sustainability objectives. The middle part of the book moves beyond discussing AI's potential for HRM practices and their implications to critical and normative debates. Chapter 7 interrogates the ethical and legal minefields surrounding AI in HRM, highlighting tensions between efficiency-driven adoption and regulatory accountability. Indeed, Tursunbayeva argues that organizations often prioritize speed and productivity over transparency and fairness. She calls for stronger governance and ethical review processes to ensure that innovation in HRM does not come at the expense of workers' rights and trust. Chapter 8 presents a multilevel framework for responsible AI, highlighting that achieving responsible AI in HRM requires more than formal regulation; it also depends on soft regulatory mechanisms and ongoing coordination across three interconnected levels. At the macro level, responsibility involves compliance with national and supranational regulations (e.g., regulatory frameworks such as the EU AI Act) and alignment with societal values around fairness, accountability and human rights. At the meso level, it concerns how organizations translate these principles into internal governance structures, ethical guidelines and HRM policies that define how AI tools are procured, implemented and monitored. At the micro level, responsibility is enacted through individual awareness, digital literacy and the everyday choices of HRM professionals, managers and employees as they interact with AI systems. The final section of the book turns to processes of change and the future of HRM. Chapter 9 situates AI adoption within established models of organizational transformation, drawing on Lewin and Kotter to highlight the challenges of embedding technological change. Tursunbayeva argues that introducing AI requires more than technical implementation and that lasting adoption depends on addressing the human side of transformation such as overcoming resistance, building trust and aligning new technologies with organizational culture and values. The chapter further illustrates these through concrete examples of change management approaches employed/promoted by leading consulting firms. Chapter 10 engages more speculatively with the future of HRM, considering how human–technology collaboration may reshape the meaning of work, knowledge and responsibility. Tursunbayeva argues that as AI takes on cognitive and decision-making tasks, the boundaries between human and machine contributions become increasingly blurred. This collaboration reshapes work, as roles evolve from executing tasks to supervising and interpreting algorithmic outputs; knowledge, as expertise shifts from individual experience to data-driven insight; and responsibility, as accountability becomes shared between human actors and automated systems. Finally, Chapter 11 offers a reflective conclusion, synthesizing theoretical, practical and policy contributions while setting out a forward-looking research agenda. Tursunbayeva argues that understanding AI in HRM requires continuous dialogue between scholarship, practice and society. She calls for interdisciplinary research that bridges management studies, ethics and data science, and for stronger integration of AI topics into HRM education to prepare future practitioners for ethical and responsible decision-making. Tursunbayeva advocates for more participatory approaches to AI development and governance, encouraging workers, educators and policymakers to play an active role in shaping the technologies that define the future of work. Her position is ultimately optimistic but critical: AI can advance more human and sustainable workplaces only if its adoption remains transparent, inclusive and collectively governed. The book has two key merits. First, it puts AI back into the e-HRM and Human Resource Information Systems (HRIS) fields. This is a significant move given the tendency of most contemporary AI research to develop independently of, and often with little reference to, these extensive literature streams. Indeed, research on technology and HRM spans more than 60 years (see Kim et al. 2021), yet much AI-in-HRM research treats AI as a novel phenomenon, rarely embedding it within established theoretical frameworks such as socio-technical systems theory or sociomateriality. This neglect exacerbates a long-standing critique in e-HRM research: that research in this domain often lacks theoretical grounding and overlooks the embeddedness of technology in organizational life. By bridging this gap, the book repositions AI not as a black box or neutral infrastructure, but as a socio-technical artefact, a technology whose meaning and effects emerge from the interplay between its technical design and the social practices, organizational routines and institutional contexts in which it is enacted. An illustrative example of this reorientation is the book's use of the classical e-HRM outcome configurations, namely operational, relational and transformational e-HRM outcomes (see Bondarouk et al. 2017). Thus, AI's operational effects are examined through its capacity to enhance efficiency and automate routine tasks in HRM processes such as candidate screening or scheduling (see Chapter 4). Its relational impact is analysed in terms of how they can shape communication, trust and employee experience, including implications for fairness and transparency in decision-making (see Chapter 5). Finally, AI transformational and augmentation potential is considered through how intelligent systems may enable strategic HRM, redefining roles, workflows and power relations within organizations (see Chapter 6). For example, AI may redistribute decision-making authority and control, shifting influence from line managers to algorithmic systems, or from employees to people analytics teams, thus reshaping whose voices count in organizational governance. This structured typology provides a coherent analytical lens through which the multifaceted impacts of AI are evaluated, lending conceptual clarity to an otherwise fragmented research field. In so doing, the volume offers a commendably organic and theoretically grounded overview of AI integration in HRM. A second major strength of the book lies in its capacity to approach the question of whether AI adoption in HRM can foster inclusive, sustainable and responsible workplaces through what might be termed an ensemble perspective (see Kim et al. 2021). Rather than reducing AI to a set of discrete tools or treating it as an external force acting upon organizations, the volume captures—albeit often implicitly—the interwoven dynamics between technologies, human actors and institutional contexts. In this sense, the book's implicit ensemble sensibility is a notable strength, as it highlights Tursunbayeva's recognition that AI outcomes are not technologically determined but emerge from the dynamic interaction of algorithms with organizational routines, regulatory frameworks and workplace cultures. While this ensemble perspective is woven throughout the chapters rather than explicitly framed as a conceptual model, making it more visible or employing it as an overarching framework could have further enhanced the book's theoretical contribution. Nonetheless, this does not detract from the book's analytical value. The ensemble view challenges linear models of technological impact by attending to the relational, situated and co-constitutive nature of human–AI interactions. For instance, whether recruitment tools reduce or reinforce bias, whether performance systems enhance well-being or intensify surveillance and whether governance structures ensure fairness and accountability depend on how these technologies are designed, the purposes for which they are deployed, and the ways in which managers and employees interpret and engage with them (Lazazzara et al. 2025). Several chapters in Tursunbayeva's book reflect this orientation. Chapter 6, for example, situates debates on diversity, inclusion and sustainability within the wider sociotechnical assemblages of HRM, showing how AI's potential to foster fairer and more resilient workplaces depends on the interplay between technological tools, organizational practices and cultural norms. Chapter 7 extends this by examining the ethical tensions of algorithmic decision-making, not as abstract dilemmas but as outcomes shaped by specific institutional logics and managerial choices. Chapter 8 further reinforces the ensemble perspective by proposing a multilevel framework of responsible AI, highlighting how adoption is conditioned simultaneously by macro-level regulations, meso-level organizational policies and micro-level worker experiences. These chapters collectively suggest inclusion, sustainability and responsibility in AI-enabled workplaces are not determined by AI in isolation but emerge from the sociotechnical assemblages, namely the intertwined networks of technologies, human actors, organizational routines and institutional norms that collectively shape how AI is designed, implemented and experienced. Moreover, Chapter 5 brings the ensemble logic into sharper relief by foregrounding the question of power. It discusses AI-powered recruitment systems, performance monitoring tools and algorithmic decision-making illustrating how meaning, agency and power are distributed across technological artefacts and organizational routines. In practice, this redistribution often favours management and the HRM function by enhancing control, streamlining decision-making and providing data-driven legitimacy to managerial choices. For workers, however, the implications are more ambivalent: while AI may promise better employee experience, it can also intensify surveillance, reduce autonomy and embed existing biases in new forms. For trade unions, these developments raise pressing questions about collective bargaining and worker voice, as algorithmic decisions may obscure accountability and limit opportunities for contestation. A more critical engagement with the resulting power asymmetries, particularly their implications for worker autonomy, surveillance and collective bargaining, would have deepened the book's contribution. By leaving these questions underexplored, the book opens an important yet underdeveloped avenue for future research to interrogate how algorithmic systems consolidate managerial dominance and reconfigure workers' agency. In conclusion, the ensemble perspective represents both a theoretical opportunity and a missed occasion for conceptual deepening. While Tursunbayeva's analysis successfully mobilizes the logic of sociotechnical entanglement to move beyond simplistic narratives of technological optimism or determinism, a more explicit theorization of the ensemble framework would have amplified the book's critical edge and aligned more directly with current debates in the literature that foreground the constitutive interplay between AI and the workplace (Kim et al. 2021; Lazazzara et al. 2025). That said, this volume makes a valuable contribution to the expanding literature on AI and HRM, while also opening important avenues for future research that takes seriously the distributed, negotiated and contested nature of AI's role in organizational life. By foregrounding how AI systems are embedded within broader managerial routines, discourses and socio-technical infrastructures, the book invites scholars to move beyond deterministic accounts of technological change. This means rejecting the idea that technology produces fixed or inevitable outcomes and instead recognizing that its effects depend on how it is interpreted, implemented and contested within specific organizational and institutional contexts. In practice, this perspective draws attention to the ways in which organizational actors engage with AI. HRM specialists may adapt AI systems to fit existing processes or to enhance the HRM function's strategic influence. Managers may embrace algorithmic tools as a means of legitimizing their decisions, but resist them when they threaten their authority. Employees, for their part, may comply with, reinterpret or contest AI applications they perceive as opaque, unfair or intrusive. Taken together, these dynamics underscore that AI in HRM is not simply a technical system but a site of negotiation and struggle, whose outcomes remain contingent and open to contestation. This perspective underscores the situated and contingent nature of AI implementation, highlighting the processes through which AI systems acquire meaning, legitimacy and influence in the workplace. In doing so, the volume provides a rich platform for advancing ongoing debates around whether AI can be harnessed to promote more inclusive and responsible forms of work, or whether it risks reinforcing existing exclusions under the guise of neutrality. As recent research demonstrates, AI-enabled recruitment tools may, on the one hand, help reduce bias by standardizing initial candidate screenings, but on the other hand, risk perpetuating historical inequalities when trained on biased datasets (Kelan 2024; Lazazzara et al. 2025). Similarly, AI-powered performance management systems can generate seemingly objective productivity scores that shape promotion decisions, yet these may systematically undervalue collaborative or context-specific contributions, thereby disadvantaging certain groups. Even chatbots designed to handle routine HRM queries can inadvertently create new forms of exclusion when employees with limited digital literacy or non-native language skills are less able to use them effectively. These examples underscore that algorithmic neutrality is often an illusion: what appears as technical objectivity can reproduce or even amplify existing exclusions unless carefully scrutinized and mitigated (Lazazzara et al. 2025). In sum, Augmenting Human Resource Management with Artificial Intelligence provides a thought-provoking entry point into the complex dynamics shaping the future of work and employment in the age of AI. It encourages both scholars and practitioners to reconsider how such technologies are conceptualized, implemented and evaluated within contemporary workplaces, as well as their implications. In doing so, the volume speaks directly to academics interested in the dynamic interplay between technology, labour and the future of work, but also to teachers seeking to integrate these debates into policy in AI in the workplace and practitioners with the ethical and organizational challenges of technological