ESG and Artificial Intelligence Integration in Digital Banking Transformation: A Qualitative Case Study of Bank Rakyat Indonesia

Abstract

Sustainability commitments and technological capabilities are increasingly expected to be integrated into a coherent strategic configuration rather than managed as parallel initiatives, particularly within digitally transforming financial institutions, yet limited evidence explains how this occurs in state-owned banks in emerging markets. This study examines how Bank Rakyat Indonesia (BRI), Indonesia’s largest state-owned bank, appears to integrate Environmental, Social, and Governance (ESG) principles with Artificial Intelligence (AI) capabilities within its digital transformation strategy. Using a qualitative single case study design, the study analyses 47 secondary documents — annual reports, sustainability reports, and regulatory publications issued between 2021 and 2023 — through content and thematic analysis guided by the Resource-Based View, Dynamic Capabilities Theory, and Institutional Theory. Because the analysis relies entirely on corporate and regulatory disclosures, the findings are presented as interpretations of these public narratives rather than verified organizational facts. Four interconnected themes emerge: AI-driven data infrastructure strengthens ESG governance through monitoring and reporting; AI-assisted credit assessment for micro, small, and medium enterprises links ESG social objectives with AI implementation; BRI’s proprietary AI ecosystem appears to generate competitive advantage through cumulative data assets and network effects; and ESG–AI narratives function as a legitimacy strategy toward regulators, investors, and development institutions. The study proposes the ESG–AI Strategic Convergence Model (EASCM), suggesting that competitive advantage may emerge when ESG and AI capabilities are co-designed as mutually reinforcing resources. The single-case, document-based design limits generalizability; future research should verify these propositions using primary data and cross-case comparison.