The growing integration of artificial intelligence (AI) into sustainability initiatives has intensified interest in its potential to influence behaviour and advance progress toward the Sustainable Development Goals (SDGs). However, a persistent gap between sustainability intentions and actual behaviours continues to constrain meaningful outcomes. This study conducts a systematic literature review to examine the extent to which AI reduces or reinforces this intention–behaviour gap across multiple levels of influence. Following PRISMA guidelines, 48 studies were analysed, capturing AI interventions across environmental, social, and economic sustainability domains and spanning internal, strategic, value chain, and system-level contexts. The findings show that AI operates as a conditional behavioural mechanism rather than a uniformly positive solution. At the internal level, AI-enabled interventions, including nudges, feedback systems, and decision support tools, are associated with improved behavioural alignment, particularly in domains linked to SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action). However, at organisational and system levels, AI frequently reinforces sustainability decoupling by enabling optimisation, reporting, and symbolic compliance without corresponding behavioural change. The study proposes a multi-level conceptual model of AI-mediated behavioural sustainability, demonstrating that AI’s effectiveness depends on contextual, organisational, and institutional conditions that determine whether it supports substantive or symbolic sustainability outcomes.
Cedric Marvin Nkiko (Sat,) studied this question.
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