Exploring the Impact of Artificial Intelligence Integration on Indonesia Banking Sector
TL;DR
The study demonstrates that AI adoption contributes to SDG 8 and SDG 9 by strengthening productivity, innovation, and financial resilience and suggests that banks should adopt differentiated implementation strategies based on their capital capacity, digital maturity, and strategic priorities.
Abstract
Artificial Intelligence (AI) has emerged as a strategic enabler of digital transformation in the banking industry, improving operational efficiency, customer experience, and risk management. This study examines the extent of AI adoption in Indonesian commercial banks and analyzes how organizational characteristics influence implementation patterns. Using a descriptive and verificative research design, survey data were collected from 181 senior banking executives representing 30 commercial banks classified as KBMI II to KBMI IV. The data were analyzed using descriptive statistics and SmartPLS 4 to evaluate relationships between bank characteristics and AI integration. The findings indicate that 64.6% of banks have implemented AI, with adoption concentrated in digital operations (65.4%), customer analytics (51.6%), and risk management (23.9%). Larger banks, particularly KBMI IV institutions, exhibit significantly higher adoption intensity and implementation maturity than smaller banks. The structural model shows that organizational readiness, capital strength, and ownership structure positively influence AI integration, explaining a substantial proportion of variance in adoption levels. The study extends global research on AI in banking by providing empirical evidence from an emerging economy and demonstrates that AI adoption contributes to SDG 8 and SDG 9 by strengthening productivity, innovation, and financial resilience. The results suggest that banks should adopt differentiated implementation strategies based on their capital capacity, digital maturity, and strategic priorities.
