Integration of ESG Criteria into Management Control in the Age of Artificial Intelligence: Theoretical Foundations and Governance Implications

Abstract

The rise of artificial intelligence (AI) is profoundly reshaping organizational management and control practices by enhancing analytical, forecasting, and optimization capabilities. In a context increasingly driven by environmental, social, and governance (ESG) challenges, management control faces a dual imperative: leveraging the technological potential of AI while strengthening its contribution to sustainable performance. This paper proposes a conceptual framework that connects management control, ESG standards, and AI, with the aim of identifying the conditions for coherent and strategic integration. Drawing on stakeholder theory (Freeman, 1984), we examine how AI can support the identification, prioritization, and management of multiple sometimes conflicting expectations from internal and external stakeholders. Legitimacy theory (Suchman, 1995) sheds light on how embedding ESG data into AI-driven information systems can enhance organizational credibility and transparency vis-à-vis stakeholders. The Triple Bottom Line approach (Elkington, 1997) provides a framework for jointly assessing economic, social, and environmental performance, while the Levers of Control model (Simons, 1995) helps analyze how AI and ESG indicators can be incorporated into diagnostic, interactive, and belief control systems.