Harnessing Artificial Intelligence and Human Resource Management for Circular Economy and Sustainability: A Conceptual Integration

Abstract

In response to global sustainability challenges and digital transformation, this conceptual paper explores the intersection of Artificial Intelligence (AI), Human Resource Management (HRM), and Circular Economy (CE). Drawing on Resource-Based View, Stakeholder Theory, Institutional Theory, and the Socio-Technical Systems perspective, we propose an integrated framework in which AI and HRM function as complementary enablers of sustainable, circular transformation. The framework identifies enablers (e.g., green HRM, digital infrastructure), barriers (e.g., ethical concerns, skill gaps), and contextual mediators. This study contributes to sustainability and digital innovation literature and suggests practical pathways for ethically aligning workforce and AI capabilities in CE adoption.

1. Introduction

The world is witnessing a profound transformation in how economies grow, organizations operate, and societies respond to environmental and technological disruption [1,2]. Accelerated by climate change, resource scarcity, and rising societal expectations, sustainability has moved from the periphery to the core of business and policy agendas. Governments, industries, and consumers alike are demanding circular, low-carbon models that can address environmental degradation while supporting economic resilience. At the same time, the Fourth Industrial Revolution, characterized by rapid advancements in Artificial Intelligence (AI), machine learning, and automation, is reshaping the way organizations produce, consume, and interact [3]. These global shifts present both challenges and opportunities, particularly in the intersection of AI, the Circular Economy (CE), and Human Resource Management (HRM), to reimagine how businesses can create sustainable, inclusive, and technologically adaptive futures.

Among the most promising responses to environmental and economic volatility is the Circular Economy—a model that moves beyond the traditional “take-make-dispose” paradigm. Instead, CE emphasizes resource efficiency, material regeneration, waste minimization, and the design of closed-loop systems [4,5]. However, transitioning to CE models at scale is far from straightforward. It requires a rethinking of entire supply chains, value propositions, and organizational cultures. This is where AI technologies come into play [4]. AI, with its capabilities in real-time data analytics, predictive maintenance, demand forecasting, and intelligent automation, offers organizations unprecedented tools to support CE adoption [6]. For instance, AI can optimize energy use in production, identify inefficiencies in material flows, or automate circular product lifecycle decisions [7].

A practical illustration of this synergy can be seen in the case of an electronics manufacturer that integrated AI-powered sensors into its production and logistics systems to monitor product usage, predict faults, and trigger circular recovery processes such as remanufacturing and recycling [8]. These digital tools not only helped reduce material waste and energy consumption but also informed new design strategies focused on durability and reuse. Complementing this technological leap was the organization’s investment in HRM initiatives—reskilling employees in data analytics, redesigning performance metrics to include sustainability goals, and fostering a culture of innovation and responsibility. The result was a coherent, sustainable transformation enabled by the integration of AI, CE, and HRM—a combination that reflects a growing trend but is still underexplored in academic literature.

While scholarship has started to explore individual elements of this triad, there remains a significant theoretical and practical gap in understanding how these components interact holistically. Studies like [9,10] underscore the value of AI in enabling CE practices, particularly in optimizing industrial operations and intelligent waste management. Similarly, ref. [11] illustrates the role of AI in advancing sustainable energy systems, and ref. [12] presents a conceptual model linking AI functions with CE actions. These contributions collectively affirm AI’s potential in advancing CE and sustainability outcomes.

However, HRM remains largely absent from this conversation. Literature often assumes that technological adoption will naturally lead to sustainability, without considering the human systems and capabilities required to realize such outcomes. This is a critical oversight. The implementation of AI in circular strategies is not a plug-and-play solution—it requires strategic workforce planning, reskilling and upskilling, cultural alignment, and ethical governance, all of which fall within the scope of modern HRM. Only a few studies, such as [13], begin to bridge this gap by proposing the integration of Green HRM with Industry 4.0 and CE practices. Yet even these are at an early stage of development and lack comprehensive frameworks that bring together AI, CE, HRM, and sustainability as interconnected domains.

Furthermore, the current body of literature tends to adopt a siloed perspective, focusing on AI as a technological enabler [14,15] or CE as a policy and operational model, with minimal attention to the organizational and behavioral dimensions that are critical for sustainable transformation [16,17,18]. For instance, while AI can generate insights on how to optimize waste management, it is HRM that ensures employees are trained to act on these insights, leaders are aligned with sustainability goals, and ethical considerations are embedded in AI applications. The interdependence between digital and human capabilities is thus crucial but remains under-theorized.

This conceptual paper argues that to unlock the full potential of AI and CE for sustainability, we must adopt an integrated perspective that recognizes the mutually reinforcing roles of AI and HRM. In doing so, organizations can move beyond fragmented solutions toward systemic change that is technologically empowered and human-centered. The absence of such integrated frameworks in existing literature represents a theoretical and practical void that this paper seeks to address.

This paper proposes an integrative conceptual framework that examines how Artificial Intelligence (AI) and Human Resource Management (HRM) enable Circular Economy (CE) practices to support Corporate Sustainability (CS). To achieve this, the study synthesizes key literature and theoretical perspectives, identifies practical and research implications, and offers a pathway for future empirical investigation.

By advancing a multidimensional view of AI, CE, HRM, and sustainability, this paper contributes to the academic discourse on digital sustainability and organizational transformation. It also provides practitioners and policymakers with a strategic lens for designing integrated interventions that are not only environmentally sound but also technologically feasible and socially responsible.

The remainder of the paper is structured as follows. Section 2 presents the theoretical foundations and conceptual relationships underpinning the study. Section 3 offers a critical literature review, mapping the current state of research across AI, CE, HRM, and sustainability. Section 4 introduces the integrative conceptual framework, followed by a discussion of its implications in Section 5. Finally, Section 5 further outlines a research agenda and concluding thoughts to guide future studies.

2. Literature Review

2.1. Theoretical Foundations

Achieving sustainable transformation at the intersection of Artificial Intelligence (AI), Circular Economy (CE), Human Resource Management (HRM), and sustainability requires a multi-theoretical lens that captures the technological, human, institutional, and systemic dynamics involved. This section outlines four theoretical perspectives that inform the development of this study’s conceptual framework: Resource-Based View (RBV), Institutional Theory, Stakeholder Theory, and the Socio-Technical Systems (STS) perspective. Together, these frameworks provide a robust foundation for analyzing how organizations can simultaneously leverage digital tools, human capital, and institutional pressures to foster sustainable and circular outcomes.

2.1.1. Resource-Based View (RBV)

The Resource-Based View [12] posits that organizations achieve a competitive advantage by developing and deploying valuable, rare, inimitable, and non-substitutable (VRIN) resources. In the context of CE and sustainability, tangible resources (e.g., circular technologies, AI infrastructure) and intangible resources (e.g., environmental culture, employee skills, innovation capabilities) are critical to achieving long-term sustainability performance. From an AI and HRM standpoint, RBV highlights that organizations with advanced technological capabilities and sustainability-oriented human capital are better positioned to integrate CE principles into their operations and strategy.

AI, as a dynamic and knowledge-intensive resource, becomes valuable when embedded in organizational routines [19] and complemented by human expertise. HRM, in this sense, plays a critical role in shaping, nurturing, and aligning human resources with AI capabilities to achieve circularity and sustainability goals [20]. Thus, RBV justifies a firm-level focus on strategic resource alignment and the development of distinctive capabilities that enable the adoption of CE.

2.1.2. Institutional Theory

Institutional Theory offers a lens for understanding how organizational behavior is shaped by external pressures, norms, and concerns about legitimacy [21]. In the sustainability domain, organizations are increasingly responding to regulatory pressures (e.g., carbon reporting mandates), normative expectations (e.g., investor demands for ESG performance), and mimetic forces (e.g., industry best practices). Institutional Theory is particularly useful in explaining why organizations pursue CE and AI adoption, not solely for efficiency, but also to maintain legitimacy in the eyes of stakeholders.

In this context, HRM becomes a vehicle for institutional adaptation [22]. For instance, organizations may adopt green training programs, environmental codes of conduct, or sustainability KPIs to align with institutional expectations. AI, meanwhile, may be deployed not only for operational improvements but also to signal innovation and responsibility [23]. This theory thus expands the analysis from firm-level capabilities to broader societal and sectoral influences on strategic choices.

2.1.3. Stakeholder Theory

Stakeholder Theory [24] argues that organizations must create value for all stakeholders, not just shareholders, to achieve long-term success. In the context of CE and sustainability, this includes customers, employees, regulators, communities, and the environment. Stakeholder Theory supports the argument that AI and HRM must be deployed in ways that are ethically grounded, socially inclusive, and environmentally responsible.

For example, AI algorithms used to optimize waste reduction must also be transparent and unbiased [25], and HRM practices must ensure employee participation, inclusion, and well-being [26]. Circular strategies that overlook stakeholder concerns, such as labor displacement due to automation or ethical risks associated with opaque AI systems, may face backlash and reputational harm. This theory, therefore, provides a normative foundation for ensuring that the integration of AI and CE is stakeholder-responsive and socially legitimate.

2.1.4. Socio-Technical Systems (STS) Perspective

The Socio-Technical Systems (STS) perspective emphasizes the interdependence between social (people, culture, organizational processes) and technical (tools, systems, infrastructure) elements within organizations [27]. In sustainable transformations, the STS view helps explain why the adoption of technology (such as AI) must be accompanied by corresponding changes in work design, employee engagement, and organizational structures. This is particularly relevant in CE contexts, where the successful implementation of circular models often hinges on cross-functional collaboration, systemic thinking, and workforce adaptability.

STS theory provides the conceptual glue that links AI and HRM in the service of sustainability [28]. It asserts that technical systems (e.g., AI for material tracking or process optimization) cannot function effectively in isolation from the social systems (e.g., HRM policies, leadership styles, team capabilities) in which they are embedded. Therefore, the STS perspective validates the need for integrative strategies that harmonize human and digital elements in pursuing circular and sustainable business models.

2.1.5. Justification for Theoretical Integration

No single theory can fully capture the multi-level complexity of integrating AI, CE, HRM, and sustainability. Each of the selected theories offers a unique but complementary lens. According to the RBV, internal strengths and resource fit are examined. Institutional Theory explains how outside forces and recognition shape the company. Stakeholder Theory adds a value and responsibility component, emphasizing that everyone matters. STS helps unite the different systems within the organization. All of them combined let us examine firms, their relationships, and the role stakeholders play from different perspectives. It becomes especially crucial when examining how organizations must integrate their use of automated intelligence (AI), ways of working (CE), workforce management (HRM), and commitments to sustainability.

In the following sections, the paper uses these insights to outline a framework that sees AI and HRM as two key drivers of CE and sustainability together. As a result, the framework using RBV ensures the value of building AI and skilled workforces; adds Institutional Theory to show how AI and HRM build sustainability under pressures from the broader society; ensures Stakeholder Theory that the efforts are open and fair to stakeholders; and introduces STS to treat AI and human resources holistically for effectiveness across the company’s operations.

2.2. Empirical Review of the Literature

2.2.1. Artificial Intelligence in Business Sustainability

Artificial Intelligence (AI) refers to the simulation of human intelligence by machines that are programmed to think, learn, and act autonomously or semi-autonomously. In a business context, AI encompasses technologies such as machine learning, natural language processing, robotics, and computer vision, which enable intelligent decision making, predictive analytics, and automation across diverse functions.

The role of AI in advancing sustainability has gained growing academic and industrial attention. As seen in recent research (e.g., [29,30,31,32]), AI is being deployed to address complex sustainability challenges—from reducing carbon emissions and enhancing energy efficiency to optimizing supply chains and enabling circular business models. Its relevance extends across various sectors, including manufacturing, tourism, agriculture, healthcare, and logistics, marking it as a cross-cutting enabler of the United Nations’ Sustainable Development Goals (SDGs).

2.2.2. Circular Economy Principles and Business Transformation

The Circular Economy (CE) represents a fundamental shift from the traditional linear model of “take, make, dispose” to a regenerative system designed to minimize waste, retain resource value, and restore natural systems. Rooted in sustainability, CE is underpinned by four core principles: Reduce—Minimizing the use of raw materials and energy in production and consumption processes. Reuse—Extending the life of products, parts, and components through repair, refurbishment, and re-manufacturing. Recycle—Recovering materials from end-of-life products to reintroduce them into production loops. Regenerate—Restoring ecosystems and enhancing biodiversity by designing industrial systems that align with natural cycles.

These principles not only aim to decouple economic growth from resource consumption but also promote long-term value creation for businesses and society [33]. The transformation from linear to circular systems is increasingly being viewed as essential for achieving the Sustainable Development Goals (SDGs) and responding to planetary boundaries.

Circular business models challenge traditional notions of value creation. Rather than focusing solely on product sales, CE emphasizes models such as product-as-a-service, reverse logistics, and closed-loop supply chains. For instance, ref. [34] highlights how the transition to Industry 5.0 enables personalized and sustainable production systems through CE and human-centric design. Meanwhile, ref. [35] identifies the critical factors that affect CE adoption in manufacturing, including organizational readiness, supply chain integration, and technology enablers. The CE transition compels organizations to rethink strategy, reconfigure operations, and invest in innovative ecosystems [36]. Key transformation levers include Design for circularity (e.g., modularity, upgradability), Circular supply chains (e.g., local sourcing, regenerative agriculture), and new performance metrics (e.g., material circularity indicators, environmental return on investment).

Technology, particularly digital innovation, is central to enabling CE on a scale and is currently considered a smart circular economy [37]. Emerging tools such as Artificial Intelligence (AI), Internet of Things (IoT), Blockchain, and Big Data Analytics serve as enablers for transparency, traceability, and resource efficiency. Key technological roles include Data-driven decision making, where real-time data on material flows, energy consumption, and lifecycle impacts can inform better design and resource planning [38]. Smart product lifecycle management—Leveraging IoT and AI enables continuous monitoring and predictive maintenance, thereby extending product life and reducing downtime [39]. Reverse logistics optimization—Digital platforms can match supply and demand for used materials, improving efficiency and reducing waste [40]. Simulation and modeling—Digital twins and system dynamics models help evaluate circular scenarios and assess their economic and environmental trade-offs. Notably, ref. [41] examines how CE initiatives must balance environmental regeneration with social equity and profit motives. Their work underscores the importance of aligning CE technology deployment with inclusive, ethical, and equitable strategies. Furthermore, cross-sectoral integration, e.g., combining CE principles with sustainable tourism, energy, or construction, can amplify systemic change. In agriculture, for example, CE principles are transforming supply chains to reduce water usage and enhance soil regeneration, as evidenced by research in Technological Forecasting and Social Change [38].

Circular Economy principles offer a transformative pathway for businesses seeking long-term resilience, sustainability, and innovation. Even so, this transition involves more than just operations; it depends on leaders, culture, and new technology. Currently, research indicates that digital innovations, such as AI and IoT, are crucial to making CE models effective, providing means to control processes in real time, plan, and manage interconnected systems. Overall, CE-driven change requires more than recycling; it also means rethinking worth, restructuring processes, and restoring the planet. The following section examines how HRM strategies can be integrated into the transition, enabling organizations to become more digital, circular, and sustainable.

2.2.3. Human Resource Management and Sustainable Development

Human Resource Management (HRM) plays a pivotal role in steering organizations toward sustainability and circular economy (CE) goals [42]. As businesses grapple with climate change, digital disruption, and stakeholder pressure for responsible governance, strategic HRM practices—particularly Green HRM (GHRM) and Sustainable HRM (SHRM)—are gaining momentum.

Green HRM refers to HR policies and practices that promote environmental awareness, minimize ecological footprints, and embed sustainability across the employee lifecycle [43,44]. Examples include green recruitment, training for environmental responsibility, green performance management, and employee involvement in sustainability initiatives. As [45] argues, GHRM is instrumental in cultivating a sustainability-oriented organizational culture, especially when paired with Industry 4.0 and CE frameworks.

Sustainable HRM, on the other hand, adopts a broader perspective by balancing economic, environmental, and social sustainability in workforce practices [43]. It emphasizes long-term employability, ethical leadership, and employee well-being—key elements in achieving sustainability beyond mere compliance. As evidenced by studies such as [46,47,48], Sustainable Human Resource Management (SHRM) has been effectively integrated across various sectors—including energy, agriculture, and manufacturing—highlighting its broad cross-sectoral relevance.

In practice, organizations adopting sustainable HRM often implement inclusive hiring policies aligned with SDG principles [49], long-term learning and development programs focused on CE, green technology, and digital competencies, and ethical leadership development to drive sustainability strategy from the top down. Metrics and KPIs are tied to sustainability indicators (e.g., carbon footprint per employee, diversity indices).

These strategic HRM approaches ensure that human capital evolves in step with organizational sustainability goals.

As businesses transition to CE models and deploy AI technologies, a major challenge lies in preparing the workforce for this dual transformation [50]. Agility, adaptability, and a commitment to lifelong learning are essential traits for employees in this dynamic context.

Workforce agility refers to the ability of employees to respond rapidly to changes, acquire new skills, and transition into cross-functional roles. Organizations must foster an environment of continuous learning and digital fluency, especially when implementing AI for tasks ranging from manufacturing automation to HR analytics.

Recent studies highlight that capability-building initiatives—such as modular training, micro-credentialing, and experiential learning—are essential for equipping employees to thrive in smart, sustainable systems [51,52,53]. In sectors such as energy, agriculture, and logistics, upskilling in data analysis, AI applications, and CE principles is increasingly viewed as a strategic necessity.

Key strategies for building a circular economy (CE)- and AI-ready workforce include cross-disciplinary training that combines environmental science, data literacy, and operational skills. Leadership development programs focused on sustainability and technological innovation are essential. Job redesign efforts are introducing new roles such as “circularity analyst” and “sustainability data officer.” In addition, digital inclusion initiatives are being implemented to ensure that employees at all organizational levels can effectively engage with AI tools and CE practices [54].

Moreover, the integration of AI in HRM itself (e.g., using machine learning for performance appraisal or predictive attrition analysis) demands ethical and psychological preparedness among HR professionals. AI-driven decision making must be transparent, unbiased, and human-centered to uphold the principles of sustainable development.

HRM has evolved into a strategic function at the heart of sustainability transformation. By integrating Green and Sustainable HRM practices, organizations can align employee behavior, culture, and systems with broader environmental and social goals. Furthermore, preparing the workforce for CE and AI adoption through agility building and capability development ensures resilience and competitiveness in a volatile business landscape.

Table 1. Review of literature.

Domain	Key Contributions in Literature	Identified Gaps	How the Proposed Framework Adds Value
Artificial Intelligence (AI)	- Enables predictive analytics, automation, and process optimization (Rebahi et al., 2023 [6]; Tutore et al., 2024 [12])	- Overemphasis on technical functions	- Positions AI as a tool that must align with human capabilities and ethical principles
	- Applied in waste reduction, energy efficiency, lifecycle monitoring (Li et al., 2025 [41]; Anozie et al., 2024 [8])	- Limited discussion on ethical risks, employee integration, or socio-organizational readiness	- Integrates AI into organizational strategy alongside HRM
Human Resource Management (HRM)	- Supports green behavior through Green HRM practices (Renwick et al., 2013 [43]; Singh et al., 2025 [45])	- Often treated as a support function, not a strategic driver	- Elevates HRM as a core enabler of CE and AI integration
	- Fosters sustainability-oriented training, leadership, and engagement (Jabbour et al., 2010 [45]; Ehnert et al., 2016 [46])	- Disconnected from technological transformation literature	- Identifies HRM’s role in capability building, ethics, and alignment
Circular Economy (CE)	- Emphasizes resource regeneration, closed-loop systems, and eco-design (Bocken et al., 2019 [55]; Khan et al., 2024 [5])	- Lacks holistic integration with workforce and digital strategies	- Places CE at the core of transformation, supported by AI and HRM
	- Requires systemic change across operations and value chains	- Often presented independently from HRM and AI	- Links CE implementation with culture, leadership, and digital infrastructure

synthesizes existing literature on AI, HRM, and CE, outlining key contributions, gaps, and how this study addresses them through an integrated model.

The next frontier for HR lies in fostering innovative mindsets and embedding sustainability into the DNA of an organization’s identity. As Section 4 will elaborate, integrating these HR strategies with AI and CE initiatives requires cohesive frameworks and cross-functional leadership to realize truly sustainable business models.

3. Proposed Conceptual Framework

3.1. Integrating AI, HRM, and Circular Economy (CE) for Sustainability

In the face of global environmental degradation, digital disruption, and the urgent need for sustainable development, organizations are increasingly recognizing the importance of integrated approaches. Among the most promising intersections lies the convergence of Artificial Intelligence (AI), Human Resource Management (HRM), and Circular Economy (CE) principles. While each of these domains has contributed to sustainability individually, their integration presents a novel opportunity to catalyze transformational change across industries. Together, they form a powerful triad. AI serves as the enabler of smart, real-time decision making; HRM cultivates the necessary human capabilities and cultural alignment; and CE provides the structural framework for resource regeneration and waste minimization.

The interplay between AI and the circular economy is becoming particularly prominent. AI technologies enable the automation and optimization of circular processes through capabilities such as predictive analytics, machine learning, and digital twins. These tools enable businesses to monitor product life cycles, forecast material wear and tear, and streamline their reverse logistics systems. For example, companies can use AI to classify waste streams with high precision, improve recycling rates, and enhance supply chain transparency. As research by [11,12] suggests, AI plays a central role in scaling CE practices, particularly in industries that rely on complex supply chains and material-intensive operations.

Simultaneously, AI is transforming HRM functions by streamlining recruitment, personalizing employee learning, and enhancing performance management systems. Algorithms can identify emerging skills needs and match them with internal talent pools, thereby fostering agility and proactive capability building. AI tools also facilitate the development of personalized training programs aligned with sustainability and CE objectives, enabling employees to acquire competencies in environmental management, eco-innovation, and data-driven decision making. As HRM leverages AI to become more strategic and forward-looking, it also gains the potential to embed sustainability values into the digital transformation journey of organizations.

The link between HRM and the circular economy is equally crucial. Green HRM and Sustainable HRM practices foster a work culture that values environmental responsibility, ethical behavior, and long-term thinking. HRM initiatives, such as green recruitment, sustainability-focused training, and environmental performance metrics, encourage employees to actively contribute to circularity goals. In particular, HRM plays a foundational role in workforce engagement, change management, and the institutionalization of CE principles within business processes. As [16] highlights, strategic HRM is not merely a support function but a driver of organizational resilience in the face of ecological and digital challenges.

When these three domains intersect, several significant synergies emerge. AI-powered data analytics can support HRM in identifying workforce sustainability champions, mapping circular competencies, and tracking carbon footprints across departments. HRM, in turn, can facilitate the cultural and behavioral shifts required to implement CE models effectively, ensuring employee buy-in and skill readiness. Additionally, the integration of CE and AI allows for the creation of new sustainability performance indicators, such as material circularity scores or energy usage per employee, that HR can incorporate into appraisal systems. As a result of these synergies, organizations can become digital and green, helping them succeed when resources are scarce.

Still, there are some difficulties when integrating these technologies. HR professionals must be mindful of the ethical risks associated with using AI in their tasks. Trust can be ruined when AI is used unfairly, and this can occur if there are no clear algorithms and unbiased strategies. If AI controls HR functions, there is a risk that both privacy issues and reduced worker autonomy may result if not handled properly. In addition, when AI and CE automate ordinary tasks and utilize resources more efficiently, the risk of job displacement primarily exists for individuals with only basic skills. HRM should take proactive steps to support and upskill employees during their transition to maintain fairness.

Another issue arises when organizational goals are not well aligned, particularly between sustainability and digital efficiency objectives. A further issue arises if organizational goals are not properly aligned. Because sustainability is about the future, many AI projects focus primarily on reducing expenses or enhancing efficiency in the present. Similarly, many companies continue to rely solely on traditional metrics, such as turnover and productivity, in their HRM metrics, overlooking social and environmental performance. When organizations lack a clear strategy for integrating all aspects, they may end up running separate activities, such as AI and CE, outside of the HR area.

Furthermore, failing to implement these factors effectively and not collaborating across disciplines can prevent the benefits from being fully realized. For integration to succeed, top managers must be committed, everyone must share the same digital tools, and all functions, including IT, HR, operations, and sustainability, must work together. It also encourages businesses to transform their culture, think broadly, include diverse groups, and act ethically.

Overall, linking AI, HRM, and CE supports the development of new approaches for sustainability. The strengths from every side can be used together to turn enterprises into resilient, intelligent, and circular ones. To make this potential a reality, we need to invest in technology, support leaders who think innovatively, encourage a diverse mix of people to work together, and align the way decisions are made. Next, we propose a model that unites these concepts, laying the groundwork for further studies and practical applications.

3.2. Conceptual Framework

As sustainability emerges as a key goal for the economy, companies must consider new approaches to their methods and models [2]. The adoption of a Circular Economy necessitates new approaches to managing waste, reusing materials, and enhancing the recovery of natural resources, as well as a focus on workforce training. In such situations, Artificial Intelligence (AI) and Human Resource Management (HRM) act as effective supports. To illustrate how these domains interact in sustainable transformation, we have developed a framework that highlights their dynamic connections, their functions, and the main factors that affect their ability to support change.

The proposed model is based on the idea that AI and HRM are complementary drivers of CE and sustainability. AI acts as a digital catalyst, providing data-driven insights, automation, and predictive capabilities that enhance efficiency, optimize resource utilization, and reduce environmental footprints. HRM functions as the organizational engine that mobilizes talent, fosters a culture of sustainability, and builds workforce capabilities aligned with the transition to circular and digital economies. At the heart of the model is the Circular Economy and Sustainability, which represents the ultimate goal. The interconnections between AI and HRM flow directly into CE, each contributing unique yet interdependent capabilities.

AI’s role in enabling CE is primarily technological [55]. It enables intelligent decision-making across the product lifecycle—from design to disposal. For example, AI can facilitate eco-design by analyzing environmental impacts during product development, support predictive maintenance to extend asset life, and optimize supply chains for reverse logistics. In waste management, AI algorithms enable the efficient sorting and classification of waste, resulting in higher recovery and recycling rates. In energy systems, AI helps balance renewable energy generation with consumption through smart grids and forecasting tools. These applications demonstrate that AI is essential not only for operational efficiency but also for environmental stewardship.

In contrast, HRM’s role is more strategic and behavioral. Sustainable and green HRM practices ensure that the workforce is prepared, motivated, and engaged to support CE initiatives [43]. HRM facilitates reskilling and upskilling programs tailored to the competencies needed for AI and CE adoption. It embeds sustainability values into recruitment, performance management, and organizational culture. Moreover, HRM drives change management strategies that reduce resistance and foster buy-in across hierarchical levels. Without HRM’s support, the technological advancements offered by AI may remain underutilized or misaligned with human capabilities and values.

Enablers of this integrated system include robust digital infrastructure, organizational agility, and strategic leadership. The implementation of AI requires the availability of data, interoperability among digital systems, and skilled technical teams [55]. On the HRM side, enablers include leadership commitment to sustainability, green organizational culture, and well-designed learning ecosystems. Importantly, alignment between HR and IT departments is critical; siloed functions can derail the system-wide transformation required for circularity.

However, the way forward is often hindered by certain challenges. Ethics is a key issue for AI, particularly given concerns about data privacy breaches, biased algorithm results, and a lack of understanding [56]. Such problems may lead stakeholders to mistrust one another and hinder the adoption process. Employees fearing that automation might cost them their jobs can make it difficult for an organization to adapt to change, from a human resources viewpoint. Having employees who lack digital or sustainability skills hinders further improvement. Moreover, when corporate strategies are not aligned or when companies focus solely on the short term, long-term success may be limited.

The functioning of the AI–HRM–CE nexus is influenced by a range of mediating and moderating variables. The degree to which leadership and top management are committed and supportive helps determine the extent to which AI and HR change actions truly influence an organization. A learning-oriented culture and employee engagement mediate the relationship between HRM and sustainability outcomes. External pressures, such as regulatory mandates, investor demands, and shifting consumer expectations, also moderate organizational behavior and strategic alignment. Firms that respond proactively to these signals are more likely to integrate AI and HRM effectively into their sustainability agenda.

To illustrate this framework, a visual model has been developed (refer to Figure 1). The diagram positions CE and sustainability at the center, supported on one side by AI (providing tools such as predictive analytics, intelligent systems, and automation), and on the other by HRM (offering green competencies, strategic alignment, and workforce engagement). Arrows illustrate the mutual reinforcement between AI and HRM, as well as their joint influence on CE. Surrounding this core are the enablers, including digital infrastructure, green culture, and strategic leadership, as well as the barriers, such as skill gaps and ethical concerns. Mediating/moderating elements such as leadership, engagement, and external pressures are positioned as dynamic influences that affect the strength of the core relationships.

In summary, the proposed conceptual framework in Figure 1 provides an integrated lens through which organizations can understand and operationalize sustainability. By linking technological capabilities (AI), human capital strategies (HRM), and circular practices (CE), the framework offers a pathway for systemic transformation. The inclusion of enablers, barriers, and contextual variables ensures that the model remains adaptable to different organizational contexts and sectors. This integrative approach transcends siloed thinking and fosters a socio-technical understanding of sustainability transitions, which is crucial for achieving long-term environmental and economic resilience.

The conceptual framework (Figure 1) positions AI and HRM as complementary enablers, CE as the mediator, and CS as the ultimate outcome. This structure is underpinned by a synthesis of four theoretical lenses (See

Table 2. Components of the conceptual framework.

Framework Component	Role	Theoretical Support	Representative Literature
Artificial Intelligence (AI)	Enabler	Resource-Based View (RBV), STS	Brynjolfsson & McAfee (2017) [14]; Singh et al. (2025) [16]
Human Resource Management	Enabler	RBV, Stakeholder Theory	Renwick et al. (2013) [43]; Ehnert et al. (2016) [46]
Circular Economy	Mediator	Socio-Technical Systems, Systems Thinking	Bocken et al. (2019) [54]; Geissdoerfer et al. (2017) [55]
Corporate Sustainability	Outcome	Institutional Theory, RBV, Stakeholder Theory	Elkington (1997) [37]; Freeman (1983) [24]

).

AI enables circular economy practices through data-driven insights, automation, and intelligent decision making, optimizing processes for resource efficiency. HRM ensures the workforce is equipped and aligned with sustainability goals through green HRM, reskilling, and change management. The Circular Economy serves as the operational model, linking enablers (such as AI and HRM) to sustainability by implementing closed-loop systems and regenerative design. Corporate Sustainability is the outcome of effectively integrating AI and HRM via CE pathways, leading to long-term environmental, social, and economic value.

4. Discussions

Working with Artificial Intelligence, Human Resource Management, and Circular Economy presents companies with new and diverse opportunities to transform themselves in a sustainable manner. By combining theories, this synthesis highlights both useful aspects and important subjects that require further research, policy planning, and development.

This conceptual paper explores the integration of Artificial Intelligence (AI), Human Resource Management (HRM), and Circular Economy (CE) to support organizational sustainability. One of its main objectives was to analyze and synthesize the literature on the interconnections among these domains. The findings reveal that while AI and CE have been widely studied individually, with AI contributing through optimization, automation, and data-driven decision making [6,12,27], and CE focusing on resource efficiency and closed-loop systems [4,5,21], the role of HRM remains underrepresented. The literature review highlights that HRM, particularly Green HRM and Sustainable HRM practices, is crucial for building the workforce capabilities and organizational culture required to enable the adoption of CE and AI [30,32,35].

The second objective aimed to propose a conceptual framework that positions AI and HRM as complementary enablers of circular and sustainable transformation. The study’s results successfully present a multidimensional model, where AI contributes through technological capabilities, such as predictive maintenance and lifecycle analytics [26,42]. At the same time, HRM drives change through reskilling, employee engagement, and sustainability-oriented policies [29,31]. At the core of this framework is the recognition that CE implementation depends not only on digital tools but also on human behavior and systemic coordination. The framework further identifies critical enablers (e.g., digital infrastructure, strategic alignment), barriers (e.g., ethical risks, skill gaps), and mediating factors (e.g., leadership support, cultural readiness) that determine the success of this integration.

Finally, in line with the paper’s fourth objective, the discussion outlines a research agenda that emphasizes the need for empirical validation of the proposed framework across diverse sectors and organizational contexts. It suggests that future studies should explore how specific combinations of AI technologies and HRM practices influence CE outcomes, such as material circularity and waste reduction [9,18,28]. Additionally, the paper calls for interdisciplinary approaches and mixed methods to investigate how these domains co-evolve and impact organizational resilience and sustainability. This focus on empirical inquiry and collaboration responds to a growing need to bridge conceptual insights with real-world applications in the journey toward digital circular transformation.

• Future Workforce Development and AI Capability Building

As organizations embrace digital circular models, the nature of work and required competencies are rapidly evolving [33]. There is an urgent need for future-oriented workforce development strategies that focus on digital literacy, green competencies, and AI-human collaboration skills. Universities, training institutions, and corporate learning departments should integrate interdisciplinary curricula that blend data science, environmental management, systems thinking, and behavioral change. Moreover, AI systems themselves must be designed to augment, not replace, human capabilities. This means designing tools with intuitive interfaces, embedded learning pathways, and transparency features that help users understand and trust AI decisions.

HRM must champion continuous learning cultures, establish strategic partnerships with educational institutions, and promote job redesign processes that elevate human–AI collaboration. For example, as routine tasks are automated, employees can be redeployed into roles involving problem solving, innovation, and stakeholder engagement—areas where human judgment remains superior. Organizations should also establish internal governance frameworks that monitor AI-driven workforce changes and ensure alignment with environmental and social responsibility goals.

• Potential Risks and Ethical Considerations

While utilizing AI, HRM, and CE can significantly enhance a company’s sustainability, it also presents various ethical and practical risks. AI used in support of sustainability could sometimes have results we did not anticipate, such as perpetuating biased decisions in hiring or causing overconsumption that is wrongly justified. Making ethical AI governance a priority and defining how data should be used, as well as being fair, responsible, and open, ought to be high on the company’s agenda. AI is involved in circular tests and must also account for its energy use and the associated risks of increased consumption as efficiency improves.

Unless upskilling is available to all workers, the focus on digital and green change in HR may lead to more workforce inequality. Laborers with little skill or experience may face an increased risk of job loss. Should HRM not support equal treatment and the right labor policies, the shift to CE could see socio-economic gaps grow wider. Furthermore, consistently using algorithms to oversee workers may erode their sense of autonomy, strain trust, and undermine their involvement, which is essential for a sustainable business.

In general, to make the best use of AI, HRM, and CE, we also need a robust ethical framework and a commitment to social sustainability. It is essential that organizations opt for human-centered designs, clarify AI decision-making processes, and ensure that digital initiatives are both inclusive and participatory. Both types of governance systems should respond flexibly through participation.

5. Conclusions

This conceptual paper has explored the dynamic and interrelated roles of Artificial Intelligence (AI), Human Resource Management (HRM), and Circular Economy (CE) in advancing organizational sustainability. Against the backdrop of accelerating global environmental, economic, and digital challenges, the paper addresses an urgent need to reimagine how businesses operate, innovate, and manage both human and technological resources. By integrating theoretical perspectives from the Resource-Based View, Stakeholder Theory, Institutional Theory, and Socio-Technical Systems, we have built a multidimensional understanding of how AI and HRM can serve as critical enablers of circular and sustainable business transformation.

Our discussion reveals that AI has significant potential to improve resource efficiency, reduce waste, and optimize decision making in alignment with CE principles. At the same time, HRM plays a pivotal role in fostering the workforce capabilities, culture, and leadership necessary to support such technological transitions. When strategically aligned, AI and HRM not only coexist but also synergistically reinforce each other to create resilient, adaptive, and sustainable organizational systems. The conceptual framework proposed herein illustrates these interrelationships, highlighting key enablers, barriers, and mediating or moderating variables that influence outcomes.

From a theoretical standpoint, this paper contributes to an emerging body of literature at the nexus of digital transformation and sustainable development. It extends existing models of sustainability by embedding AI and HRM into the operationalization of CE principles—an area that remains underexplored in current scholarship. Our synthesis adds value by highlighting the need to move beyond isolated approaches and toward integrated frameworks that reflect the complexity of real-world sustainability transitions. The integration of socio-technical and institutional logic also enhances our understanding of how structures, agency, and technology interact to shape sustainable outcomes.

In terms of practical contributions, the paper offers actionable insights for managers, policymakers, and HR professionals. For organizational leaders, it emphasizes the importance of aligning AI investments with human-centered change strategies and long-term sustainability goals. For HR practitioners, it advocates for the adoption of green HRM practices and continuous reskilling initiatives that prepare employees for digital and circular transitions. For policymakers, the paper emphasizes the importance of establishing ethical and governance frameworks that ensure the responsible deployment of AI, equitable workforce transitions, and inclusive innovation.

Nevertheless, several limitations must be acknowledged. As a conceptual paper, the arguments presented are based on theoretical synthesis and secondary data rather than primary empirical investigation. While the proposed framework is grounded in literature and illustrative logic, its real-world applicability requires empirical testing through case studies, surveys, or experimental designs. Furthermore, although the paper attempts to be interdisciplinary, it is constrained by the scope of the existing literature, which remains fragmented across various fields. Cultural, regional, and industry-specific nuances are not fully addressed and warrant further exploration.

In light of these limitations, this paper calls on scholars to build upon and empirically test the proposed model using diverse methodological approaches and cross-sectoral analyses. Future research should investigate the contextual conditions that enable or hinder the successful integration of AI, HRM, and CE practices. Practitioners are urged to consider the strategic alignment of digital, human, and environmental priorities as they design organizational systems for long-term resilience. Without such integrated thinking and collaboration, the transition to a sustainable future may remain fragmented and incomplete.

Ultimately, the fusion of technological innovation, strategic human resource management, and circular economic thinking holds transformative potential. To unlock this potential, both academic inquiry and organizational action must evolve in tandem, grounded in ethics, inspired by sustainability, and driven by systems thinking.

5.1. Theoretical Implications

Theoretically, this paper contributes to the growth of interdisciplinary sustainability research by bridging unconnected fields of study. Using the Resource-Based View (RBV) and Stakeholder Theory in conjunction with Socio-Technical Systems Theory, we can examine how organizations develop strengths that others cannot replicate and that are also accepted by all stakeholders, contributing to lasting change in the system. This framework enhances digital and sustainability capabilities in a manner consistent with the conventional way RBV approaches human capital. It also invites society, companies, and individuals to consider the social and moral implications of AI use in the workplace. By examining socio-technical systems, we can observe how structure, people, and technology interact to support circular changes. As a result, additional research can be conducted to prove propositions and address new issues in explaining how organizations sustain themselves.

5.2. Managerial and Policy Implications

For practitioners, this framework offers actionable insights into how businesses can strategically align digital transformation and sustainability goals. Managers must recognize that deploying AI for CE objectives cannot succeed in isolation and requires coordinated HRM interventions to develop the necessary mindsets and skillsets within the workforce. For instance, predictive analytics for supply chain circularity will only be effective if employees understand how to interpret and act on such insights. HRM departments should embed sustainability into recruitment, training, and performance evaluation systems to institutionalize green behavior across all levels of the organization. Leadership plays a critical role in fostering cross-functional collaboration between IT, operations, and HR, ensuring that AI implementation aligns with CE goals rather than merely driving efficiency.

Policy makers, in turn, must ensure that regulatory environments promote the ethical adoption of AI while incentivizing circular business models. Policies encouraging open data sharing, digital upskilling, and responsible innovation can strengthen the enabling environment. At the same time, governments must address potential labor displacement due to automation by introducing proactive workforce transition programs, tax incentives for green reskilling, and inclusion-focused policies that ensure vulnerable groups are not left behind in the transition to sustainability.

5.3. Research Agenda and Future Directions

The growing convergence of Artificial Intelligence (AI), Human Resource Management (HRM), and Circular Economy (CE) within sustainability research and practice presents a rich landscape for scholarly inquiry. While conceptual models help establish foundational understanding, there remains a pressing need for empirical studies that validate, contextualize, and extend these frameworks. Future research should aim to deepen our understanding of how these three domains interact and evolve, as well as the organizational, societal, and environmental outcomes that result.

A critical area for investigation is the impact of AI technologies on the practical realization of CE principles, such as reducing, reusing, and regenerating resources. Although the literature suggests promising applications, such as smart waste management, predictive maintenance, and energy optimization, there is limited knowledge about the long-term effectiveness and unintended consequences of these tools. Researchers should examine which types of AI (e.g., machine learning, computer vision, natural language processing) are most aligned with specific CE goals, and in what contexts they yield the most benefit. Simultaneously, empirical studies should explore how HRM practices can best support the human capital needs of such transformations, including reskilling, fostering a circular mindset, and maintaining employee well-being amid digital disruption.

Several key research questions emerge from this intersectional space. How do AI and HRM together facilitate or hinder the successful adoption of circular models in different industries? What competencies and organizational cultures are most conducive to sustaining these transformations? Are certain HRM strategies, such as green recruitment, sustainability-oriented training, or performance incentives, more effective than others in embedding circular values into organizational routines? Furthermore, researchers should investigate the extent to which AI and HRM interventions influence sustainability outcomes at multiple levels: organizational, sectoral, and societal. Ethical questions are also ripe for exploration, such as whether AI systems used for sustainability exacerbate digital divides or undermine worker autonomy.

Addressing such questions requires robust methodological approaches. Qualitative methods, including interviews, ethnographies, and case studies, are well-suited for uncovering organizational dynamics and understanding how individuals experience and make sense of AI-driven sustainability initiatives. These methods are particularly useful in capturing the nuanced role of leadership, culture, and employee engagement. On the other hand, quantitative research, including survey studies, regression analysis, and structural equation modeling, can test the strength and direction of relationships among AI implementation, HRM practices, and CE performance indicators. Mixed-methods research, which integrates the depth of qualitative insights with the generalizability of quantitative analysis, is especially promising for this interdisciplinary field. Additionally, simulation models and systems thinking approaches could be used to map feedback loops and predict long-term outcomes of various AI-HRM-CE configurations.

The research agenda must also be interdisciplinary in scope. Scholars from management, information systems, environmental studies, organizational psychology, and public policy must collaborate to produce holistic insights. For example, AI developers working with HR professionals and sustainability experts can create ethically aligned technologies that promote CE goals while protecting worker rights and autonomy. Legal scholars and ethics can inform governance structures that ensure fair data practices and accountability in AI deployment. Engineers and behavioral scientists can co-design user-centric AI systems that are both technically effective and socially acceptable.

Cross-sectoral comparisons represent another important direction. The challenges and opportunities of implementing AI-enabled CE models vary widely across industries such as manufacturing, logistics, energy, retail, and services. Each of these contexts brings distinct operational realities, labor dynamics, and regulatory environments that influence the feasibility and impact of AI-HRM integration. Research comparing best practices and barriers across sectors can provide valuable insights for managers and policymakers seeking to scale sustainable innovations. Additionally, studies in emerging economies can yield critical knowledge about inclusive green transitions and leapfrogging opportunities in regions with limited digital infrastructure but high sustainability potential.

Finally, the global push for climate neutrality and digital competitiveness underscores the need for research that supports future workforce development. As organizations deploy AI to achieve circularity, workers will need a new blend of technical, cognitive, and emotional skills. Scholars should investigate the design and delivery of educational interventions that prepare individuals to thrive in this digital green economy. This includes understanding what motivates learning, how to foster lifelong skill acquisition, and how to reduce digital skill inequalities. Evaluating the efficacy of these interventions will help ensure that the sustainability transition is not only technologically feasible but also socially just and inclusive.

To extend the relevance of this work, we propose that the framework be applied in practice as a diagnostic or strategic planning tool for organizations aiming to align their AI initiatives and HR practices with CE principles. Furthermore, future research could empirically test the framework through case studies, industry-specific surveys, or longitudinal assessments to evaluate the causal relationships among the proposed elements and to refine the model across different sectors and organizational contexts.

By connecting theory, practice, and future research avenues, this study offers a foundation for further inquiry and actionable insights for managers, policymakers, and scholars working at the intersection of technology, sustainability, and organizational change.