Artificial Intelligence, Sustainable Human Resource Management, and Organisational Sustainability: A Multi-Level Integrative Framework

Journal of Perspectives in Management

Role of Artificial Intelligence in Green Human Resource Management: An Integrated Framework for Sustainable People Management

This paper examines the rapidly developing relationship between Artificial Intelligence (AI) and Green Human Resource Management (GHRM). As organizations pursue environmental objectives while digitizing their HR systems, AI is increasingly being considered for recruitment, learning, performance assessment, employee engagement and workforce analytics. The paper argues that the sustainability contribution of AI cannot be inferred from automation alone. A critical review of foundational GHRM scholarship and recent AI-HRM, AI-GHRM and Green-AI research is used to identify the principal applications, benefits, risks and unresolved questions. The review indicates that AI can make green HR practices more targeted, continuous and evidence-informed, but outcomes depend on employee digital capability, green organizational climate and responsible governance. The paper therefore proposes an integrated framework in which AI capability strengthens GHRM practices; digital literacy, green climate and responsible AI governance influence implementation; employee green behaviour provides the behavioural pathway; and environmental and organizational sustainability form the principal outcomes. The review also highlights the environmental cost of AI itself, requiring organizations to consider both the benefits of AI-enabled sustainability and the footprint of the technology used. Eighteen recent studies are critically compared, revealing gaps in measurement, causal evidence, Indian-context research, responsible-AI integration and objective environmental indicators. Nine propositions are offered for future empirical testing.

Artificial Intelligence and Sustainability Value Creation in Industry: A Systematic Literature Review and a Mechanism-Based Framework

Artificial intelligence (AI) is increasingly recognised as an enabler of sustainability in industrial systems, yet existing research remains fragmented and strongly oriented towards technical optimisation. This systematic literature review examines how AI-enabled sustainability value creation has been conceptualised through the analysis of 75 peer-reviewed articles published between 2020 and 2025. The findings reveal a rapidly expanding field, with 54% of the reviewed studies published in 2024–2025. However, the evidence remains concentrated at process and plant levels: 69% of studies focus on operational applications, and 72% adopt technical, simulation-based, optimisation-oriented, or model-development approaches. Prediction, optimisation, monitoring, adaptive control, and decision support emerge as the dominant AI-enabled mechanisms, while social, governance, resilience, and systemic transformation dimensions remain comparatively underexplored. The review further shows that the literature is stronger in documenting operational sustainability outcomes than in explaining how sustainability value becomes organisationally embedded and sustained across industrial systems. In response, this study proposes a mechanism-based framework linking organisational antecedents, AI-enabled mechanisms, operational transformation, sustainability outcomes, and contextual contingencies. The framework conceptualises AI-enabled sustainability value creation as an organisationally embedded, contingent, and multilevel process rather than a direct outcome of technological deployment alone.

Artificial Intelligence for Sustainable Development: A Theoretical Framework Linking AI Capabilities with Environmental, Social and Economic Value Creation

The accelerating advancement of Artificial Intelligence (AI) has positioned it as a transformative force with significant implications for sustainable development. While existing research highlights the potential of AI to address environmental, social, and economic challenges, there remains a lack of integrated theoretical frameworks that systematically explain how AI capabilities contribute to sustainability outcomes. This paper develops a comprehensive theoretical framework that conceptualises AI capabilities as strategic resources driving multidimensional value creation across the environmental, social, and economic domains of sustainable development. Drawing upon the Resource-Based View, Stakeholder Theory, and the Triple Bottom Line framework, the study synthesises existing literature to explain how core AI capabilities, such as predictive analytics, automation, optimisation, and intelligent decision-making, enable organisations and institutions to enhance resource efficiency, improve social welfare, and foster economic growth. The framework further highlights the mediating role of operational and strategic transformation processes through which AI capabilities translate into sustainability outcomes. In addition to opportunities, the paper critically examines the challenges associated with AI deployment, including ethical concerns, energy consumption, data bias, and governance issues, emphasising the need for responsible and inclusive AI practices. By integrating insights from technology, sustainability, and management literature, this study contributes to the growing discourse on AI-driven sustainability by offering a structured theoretical perspective on value creation. The proposed framework provides a foundation for future empirical research and offers strategic implications for policymakers, organisations, and stakeholders seeking to leverage AI for sustainable development.

Exploring AI governance for sustainable decision-making: evidence from six case studies

Examining how artificial intelligence (AI) governance supports sustainable decision-making across organizational contexts in Europe reveals that governance increasingly aligns with formal frameworks through policies, dedicated structures, human oversight and Environmental, Social and Governance oriented indicators, enhancing transparency and reliability.

Artificial intelligence as a driver of human resource management transformation: opportunities, risks and strategic directions

The rapid development of Artificial Intelligence (AI) is fundamentally reshaping organizational processes and redefining the role of Human Resource Management (HRM). This paper aims to analyze how HRM is being redesigned in the context of AI-driven transformation, focusing on key opportunities, associated risks, and strategic directions for sustainable organizational development. The study is based on a qualitative, conceptual methodology, using secondary data derived from international reports and indices, including the World Economic Forum's Future of Jobs Report, OECD well-being frameworks, International Labour Organization analyses, and European policy documents. In addition, insights from the banking sector are considered as an illustrative context due to its advanced level of AI adoption. The findings indicate that AI enhances the efficiency of HR functions such as recruitment, performance management, and workforce analytics, while also enabling more personalized employee experiences. At the same time, the integration of AI introduces significant challenges, including algorithmic bias, ethical concerns, data privacy risks, and the potential depersonalization of workplace relationships. The paper argues that HRM must evolve towards a strategic and integrative role, ensuring a balance between technological innovation and employee well-being. A conceptual perspective is proposed to support organizations in adapting HR practices to AI-driven environments while maintaining a human-centered approach and long-term performance.