THE DUAL EFFECT OF ARTIFICIAL INTELLIGENCE APPLICATION IN THE MANAGEMENT OF SUSTAINABLE OUTSOURCING OF MULTINATIONAL ENTERPRISES

Abstract

The article is devoted to the conceptualization of the dual effect of artificial intelligence (AI) in managing sustainable outsourcing of multinational enterprises (MNEs). The dual effect is defined as a synergistic interaction between two inseparably linked dimensions: the socio-ethical dimension, legitimation of business activities through compliance with societal and regulatory expectations, and the practical dimension, optimization of operational efficiency and resource intensity. This duality is not a static property of technology but is dynamically configured depending on the strategic priorities of the enterprise, industry-specific context, and the nature of institutional pressures. Three core mechanisms through which the dual effect is realized have been identified: intelligent supplier selection, predictive emission analytics, and automated monitoring of ESG commitments. Each mechanism demonstrates how AI-driven technological solutions simultaneously generate ethical and economic value, overcoming the traditional trade-off between profitability and responsibility. Based on a cross-case analysis of eight leading MNEs, IBM, OpenSC, Microsoft, Adidas, UPS, H&M, Unilever, and FrieslandCampina, an original typology of the dual effect has been developed, comprising four qualitatively distinct types: Symmetric Synergy, where ethical transparency directly converts into economic efficiency; Ethically-Dominant Convergence, where socio-ethical goals act as the primary driver generating unexpected economic gains; Economically-Dominant Transformation, where operational optimization produces positive environmental externalities; and Cascading Amplification, where progress in one dimension systematically catalyzes advancement in the other. The type of dual effect realization is determined by the strategic priorities of the MNE, industry specificity, and the character of institutional pressure. The study identifies key challenges to scaling AI solutions, significant initial investments, cybersecurity risks, shortage of qualified personnel, and the threat of algorithmic greenwashing, and formulates practical recommendations, including the establishment of internal AI competence centers and independent auditing of AI-generated ESG reports.