Artificial Intelligence, ESG Performance, and Corporate Financial Resilience: Evidence from Listed Companies in Bangladesh

his study investigates the joint effect of Artificial Intelligence (AI) adoption and Environmental, Social, and Governance (ESG) performance on corporate financial resilience among listed firms in Bangladesh. Drawing on the dynamic capabilities view, stakeholder theory, and resource-based theory, we develop an integrated framework that positions AI adoption as a technological capability and ESG performance as a legitimacy-enhancing resource that together strengthen firms' capacity to absorb, adapt to, and recover from financial shocks. Using a panel dataset of 142 non-financial firms listed on the Dhaka Stock Exchange (DSE) over 2015–2024 (1,420 firm-year observations), we construct an AI adoption index from annual-report textual analysis, an ESG composite from Refinitiv and hand-collected disclosures, and a multidimensional Financial Resilience Index (FRI) combining liquidity, leverage, profitability stability, and Altman Z-score components. We estimate two-way fixed-effects and system-GMM models to address endogeneity, complemented by machine-learning (XGBoost with SHAP) analysis to capture non-linear interactions. Results show that both AI adoption (β = 0.184, p < 0.01) and ESG performance (β = 0.221, p < 0.01) positively affect financial resilience, and their interaction is significant and positive (β = 0.096, p < 0.05), indicating a complementary relationship. The effect is stronger for firms in export-oriented and high-competition sectors, and during the COVID-19 shock period. The findings extend ESG-AI research to a frontier South Asian market and offer actionable implications for managers, regulators (BSEC, Bangladesh Bank), and policymakers advancing the National AI Strategy and ESG disclosure roadmap.