Agentic artificial intelligence (AI) is a consequential technological frontier in banking because it shifts AI from passive assistance and generative interaction toward goal-directed workflow execution. Responsible and sustainable banking transformation therefore depends not simply on whether banks experiment with agentic AI, but on the readiness conditions under which selected agentic capabilities can move from pilots to governed production and responsible value realization. This study develops a configurational forecasting framework for agentic AI deployment readiness in banking. Because comparable initiative-level evidence remains scarce and commercially sensitive, the paper adopts a transparent, case-informed synthetic configurational simulation. The analysis should therefore be read as a theory-development and foresight exercise, not as empirical evidence of actual bank projects or banking-sector prevalence. Drawing on public banking AI cases, technology-diffusion and foresight literature, AI governance research, and role-based stakeholder archetypes, we construct a synthetic dataset of 90 banking-related agentic AI initiatives and apply fuzzy-set Qualitative Comparative Analysis (fsQCA). Within this bounded simulation, production maturity is internally consistent with the conjunction of data readiness, leadership commitment, governance maturity, workflow redesign capability, human–agent collaboration maturity, and low legacy-system complexity. Supplementary analyses indicate that deployment alone is insufficient for value realization in the simulated design: value requires deployment to be combined with redesigned workflows, governed data use, and human–agent collaboration. The results are not causal estimates; rather, they specify falsifiable readiness expectations that future empirical research can test with real initiative-level data. The study offers a reproducible readiness logic for responsible value realization, customer protection, workforce capability, and financial-system resilience.

Agentic artificial intelligence (AI) is a consequential technological frontier in banking because it shifts AI from passive assistance and generative interaction toward goal-directed workflow execution. Responsible and sustainable banking transformation thus depends on the readiness conditions under which agentic AI can move from pilots to governed production and value realization. This study develops a configurational forecasting framework for agentic AI deployment readiness in banking. Because comparable initiative-level evidence remains scarce and commercially sensitive, the paper adopts a transparent, case-informed synthetic configurational simulation rather than claiming to analyze actual bank projects. Drawing on public banking AI cases, technology-diffusion and foresight literature, AI governance research, and role-based stakeholder archetypes, we construct a synthetic dataset of 90 banking-related agentic AI initiatives and apply fuzzy-set Qualitative Comparative Analysis (fsQCA). The bounded simulation indicates that production maturity is associated with the conjunction of data readiness, leadership commitment, governance maturity, workflow redesign capability, human-agent collaboration maturity, and low legacy-system complexity. A supplementary analysis shows that deployment alone is insufficient: value is realized only when deployment is combined with redesigned workflows, governed data use, and human-agent collaboration, whereas non-deployment arises from distinct failure configurations rather than the mere inverse of success. These patterns reframe governance as an enabler of bounded autonomy rather than a constraint. The study offers a reproducible readiness logic for responsible value realization, customer protection, workforce capability, and financial-system resilience.

Investors value sustainability information that is financially material to a firm’s industry, but little is known about how much of it firms outside the United States and Europe report. We examine 7,278 statutory business reports of 808 non-financial KOSPI firms for fiscal years 2016–2025. A classifier trained on large language model annotations sorts every narrative sentence by environmental, social and governance (ESG) issue and specificity, and the industry standards of the Sustainability Accounting Standards Board separate material from immaterial content. The classifier outperforms dictionary and zero-shot methods; blind human coding confirms this advantage but shows that the classifier understates material content. Material ESG information is a small, slowly growing, mostly qualitative part of the reports. After Korea’s 2021 roadmap for mandatory sustainability disclosure, the largest firms expanded their ESG content more than other firms. They added specific material information but also generic and, less robustly, immaterial statements, so their disclosure did not shift significantly towards material issues; the estimates are sensitive to flexible trend controls. Material disclosure is not robustly related to firm value, and its weak association with better liquidity is shared by ESG content generally. Anticipated mandates can expand disclosure without clearly making it more useful to investors.