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

Abstract

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.