AI-augmented decision-making and sustainability performance in SMEs: a hybrid PLS-SEM, ANN, and multi-group analysis

Purpose This study examines how artificial intelligence-augmented decision-making (AI-ADM) capability influences triple bottom line (TBL) sustainability performance in small and medium-sized enterprises (SMEs), investigating technological, human, and organisational antecedents, the moderating role of environmental volatility, and heterogeneous effects across firm sizes. Design/methodology/approach Grounded in dynamic capabilities theory, organisational learning theory, and the resource-based view, we develop a comprehensive theoretical framework tested using a hybrid approach integrating Partial Least Squares Structural Equation Modelling (PLS-SEM) with Artificial Neural Networks (ANN). Data were collected from 304 SME managers and IT professionals. Multi-group analysis (MGA) examines firm size heterogeneity. Findings PLS-SEM results reveal that all seven antecedents significantly influence AI-ADM capability, with technology infrastructure (β = 0.342) and managerial skills (β = 0.324) demonstrating the strongest effects. AI-ADM capability significantly enhances environmental (β = 0.467), economic (β = 0.524), and social performance (β = 0.398). Environmental volatility positively moderates these relationships. ANN analysis confirms technology infrastructure and managerial skills as the most critical predictors. MGA reveals that medium-sized enterprises derive significantly greater sustainability benefits from AI-ADM than small enterprises. Research limitations/implications This study is based on cross-sectional data from SMEs in BRICS economies, which limits causal inference and may not fully capture the dynamic evolution of AI-enabled decision-making capabilities. Although the hybrid PLS-SEM, PLSpredict, ANN and fsQCA approach enhances analytical robustness, the findings may not generalise to large firms or non-emerging markets. Self-reported measures may introduce perceptual bias, and future studies could incorporate longitudinal data, objective performance indicators and multi-respondent designs. Expanding the model to include generative AI, supply-chain dependencies or digital ecosystem interactions would further strengthen theoretical development and broaden the study's empirical relevance. Practical implications This study provides SME managers with a clear roadmap for leveraging AI-enabled decision-making capabilities to improve sustainability performance in turbulent environments. By demonstrating how digital orientation, data governance and AI adoption jointly strengthen decision quality, the findings offer managers practical guidance on prioritising investments in data infrastructure, organisational learning and digital culture. The hybrid PLS-SEM, PLSpredict, ANN and fsQCA results reveal multiple capability pathways that SMEs can follow depending on their resource constraints. Policymakers can also use these insights to design targeted capacity-building programmes that accelerate digital transformation across emerging-economy SMEs. Social implications The study shows that AI-enabled decision-making can enhance the social and environmental performance of SMEs, particularly in resource-constrained emerging economies. By improving transparency, data quality and evidence-driven choices, AI capabilities support fairer labour practices, community engagement and responsible resource use. The results underscore the potential of digital technologies to reduce inequality between digitally mature and digitally marginalised firms, enabling more inclusive participation in global value chains. Strengthening SME digital capabilities, therefore has broader societal benefits, including job preservation, skills development, improved environmental stewardship and greater resilience of local economies facing volatility and uncertainty. Originality/value This study extends dynamic capabilities theory to the AI-sustainability nexus, identifies resource configurations enabling AI-ADM in SMEs, demonstrates contingent effects of environmental volatility, and reveals firm size heterogeneity in AI-driven sustainability outcomes. The hybrid PLS-SEM-ANN methodology advances analytical rigour by capturing both linear and non-linear relationships.