AI-Driven Sustainability Initiatives and Customer Perception: Mechanisms, Moderators, and Ethical Risks; A Systematic Review

The growing integration of artificial intelligence (AI) with corporate sustainability initiatives is reshaping contemporary marketing practice, yet the mechanisms through which these initiatives influence customer perception remain insufficiently examined across diverse geographic, demographic, and industry contexts. This study examines how AI-driven sustainability initiatives influence customer perception and identifies the individual, contextual, and ethical factors that moderate this relationship. A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 (PRISMA 2020) protocol, synthesising twenty peer-reviewed studies and conference proceedings published primarily between 2020 and 2026. Sources were retrieved from Scopus, Web of Science, Google Scholar, and EBSCO Business Source Complete, supplemented by manual reference list searching. Thematic synthesis across five analytical dimensions reveals three primary mechanisms through which AI shapes customer perception personalised sustainability engagement, predictive analytics & targeting, and real-time environmental transparency moderated by institutional trust, digital literacy, geographic context, and generational orientation across retail, banking, and cosmetics sectors in India, China, Indonesia, and the Gulf Cooperation Council region. AI-enabled greenwashing emerges as the most consequential threat to perceived organisational authenticity, while data privacy violations are identified as a critical determinant of consumer trust. Five research gaps are identified, including the absence of longitudinal evidence and insufficient measurement of actual environmental outcomes. The study concludes that AI advances sustainable marketing only when grounded in genuine environmental commitment and transparent ethical governance, with distinct implications for marketing managers seeking contextually sensitive deployment strategies, policymakers developing AI-specific regulatory frameworks, and researchers designing longitudinal and cross-cultural studies.