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Nearly two‑thirds of surveyed Indonesian commercial banks report using AI, concentrated in digital operations and customer analytics; adoption and implementation maturity are markedly higher among the largest, best‑capitalized institutions, driven by organizational readiness and ownership.

Exploring the Impact of Artificial Intelligence Integration on Indonesia Banking Sector
Boy Tjahyono, Muhtosim Arief, Willy Gunadi, Diena Dwidienawati · August 30, 2026 · Aptisi Transactions On Technopreneurship (ATT)
openalex correlational low evidence 7/10 relevance Summary only summary available; pdf_status=paywall DOI Source PDF

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A survey of Indonesian commercial banks finds 64.6% report AI implementation concentrated in digital operations, customer analytics, and risk management, with larger, better‑capitalized banks showing higher adoption intensity and maturity and organizational readiness, capital strength, and ownership structure positively associated with AI integration.

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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.

Summary

Main Finding

AI is already widely adopted among Indonesian commercial banks (64.6% have implemented AI), with implementation concentrated in digital operations (65.4%), customer analytics (51.6%), and risk management (23.9%). Larger, better‑capitalized banks (particularly KBMI IV) show higher adoption intensity and greater implementation maturity. Organizational readiness, capital strength, and ownership structure are significant positive predictors of AI integration.

Key Points

  • Adoption rate: 64.6% of sampled banks report AI implementation.
  • Primary application areas:
    • Digital operations: 65.4%
    • Customer analytics: 51.6%
    • Risk management: 23.9%
  • Heterogeneity by bank size: KBMI IV banks (largest in sample) have significantly higher adoption intensity and maturity than KBMI II/III banks.
  • Predictors of AI integration: organizational readiness, capital strength, and ownership structure positively influence adoption levels (PLS-SEM results).
  • Contribution to development goals: authors link AI adoption to SDG 8 (decent work & economic growth) and SDG 9 (industry, innovation & infrastructure) via productivity, innovation, and financial resilience gains.
  • Recommendation in paper: banks should use differentiated AI strategies aligned with capital capacity, digital maturity, and strategic priorities.

Data & Methods

  • Design: Cross‑sectional descriptive and verificative survey study.
  • Sample: 181 senior banking executives representing 30 Indonesian commercial banks (KBMI II to KBMI IV).
  • Measurement: Self‑reported measures of AI implementation status, application domains, implementation maturity, and organizational characteristics (readiness, capital strength, ownership).
  • Analysis:
    • Descriptive statistics to report adoption rates and domain shares.
    • SmartPLS 4 (PLS‑SEM) to estimate relationships between bank characteristics and AI integration; model shows organizational readiness, capital strength, and ownership structure positively associated with adoption and explains a substantial portion of variance (R^2 not reported in summary).
  • Limitations (implicit from design): cross‑sectional self‑reported data, limited to KBMI II–IV banks in Indonesia, no direct measurement of firm-level productivity or causal effects.

Implications for AI Economics

  • Returns to scale and concentration: Higher AI adoption and greater implementation maturity in larger, better‑capitalized banks point to scale advantages — AI investment may reinforce concentration, increasing market power for large incumbents.
  • Complementarity of capital and technology: Capital strength emerges as a key enabler of AI integration, suggesting adoption is capital‑intensive and that financial capacity and digital investment are complements.
  • Heterogeneous adoption and diffusion barriers: Organizational readiness and ownership structure drive heterogeneity; smaller banks may lag without targeted support, which could widen productivity and service gaps across the banking sector.
  • Productivity and innovation: By improving digital operations, customer analytics, and risk management, AI adoption can raise operational efficiency and innovation in financial services—supporting macro productivity growth (SDG 8 & 9), but empirical measurement of firm‑level output gains remains needed.
  • Financial stability and resilience: AI in risk management can enhance resilience, but systemic effects are ambiguous—widespread use of similar models could create correlated risks; regulators should monitor model risk and systemic concentration.
  • Policy and market design implications:
    • Support mechanisms (capacity building, subsidized experimentation, sandboxes) could help smaller banks adopt AI and reduce concentration pressures.
    • Encourage data sharing standards, open APIs, and partnerships with fintechs to lower adoption costs for smaller institutions.
    • Regulatory focus on model governance, transparency, and third‑party vendor risk to mitigate systemic vulnerabilities.
  • Research gaps: Need for longitudinal or causal studies linking bank‑level AI adoption to measurable outcomes (productivity, profitability, employment), assessment of distributional effects within the sector, and analysis of how ownership types (e.g., state vs private, domestic vs foreign) shape adoption dynamics.

If you want, I can convert this into a one‑page slide, extract policy recommendations for Indonesian regulators, or outline a follow‑up empirical design to measure AI’s causal impact on bank productivity.

Assessment

Paper Typecorrelational Evidence Strengthlow — Cross-sectional, self-reported survey data from executives and PLS-SEM associations cannot support causal claims; potential common-method and reporting biases and limited sample of 30 banks weaken inference about real-world impacts or causal mechanisms. Methods Rigormedium — The study uses standard descriptive statistics and PLS-SEM to estimate relationships, which is appropriate for exploratory correlational work, but key rigor concerns remain: reliance on self-reported measures, cross-sectional design, limited sample of banks, unclear construct validation and measurement quality, and no robustness checks or causal identification strategies reported in the summary. SampleCross-sectional survey of 181 senior banking executives representing 30 Indonesian commercial banks (KBMI II to KBMI IV); measures are self-reported implementation status, application domains, implementation maturity, and organizational characteristics (readiness, capital strength, ownership). Themesadoption productivity org_design innovation GeneralizabilityLimited to Indonesian commercial banks (KBMI II–IV) — may not generalize to smaller local banks, rural cooperatives, or banks in other countries., Sample is executive self-reports, introducing potential reporting and common-method bias., Cross-sectional snapshot — findings may not hold over time or capture adoption dynamics., Does not measure firm-level productivity or market outcomes, so inference about economic impacts is indirect.

Claims (10)

ClaimDirectionOutcomeConfidence & EvidenceDetails
64.6% of sampled Indonesian commercial banks report that they have implemented AI. Adoption Rate positive AI implementation/adoption rate
Reading fidelity high
Study strength medium
n=181
64.6%
0.3
Among the reported AI application areas, digital operations are the most common, accounting for 65.4% of implementations. Organizational Efficiency positive AI use in digital operations
Reading fidelity high
Study strength medium
n=181
65.4%
0.3
Customer analytics is a major reported application area for AI, accounting for 51.6% of implementations. Decision Quality positive AI use in customer analytics
Reading fidelity high
Study strength medium
n=181
51.6%
0.3
Risk management is a reported AI application area, accounting for 23.9% of implementations. Organizational Efficiency positive AI use in risk management
Reading fidelity high
Study strength medium
n=181
23.9%
0.3
KBMI IV banks have significantly higher AI adoption intensity and implementation maturity than KBMI II and KBMI III banks. Adoption Rate positive AI adoption intensity and implementation maturity
Reading fidelity high
Study strength medium
n=181
0.3
Organizational readiness is positively associated with the level of AI integration among Indonesian commercial banks. Adoption Rate positive Level of AI integration
Reading fidelity high
Study strength medium
n=181
0.3
Capital strength is positively associated with the level of AI integration among Indonesian commercial banks. Adoption Rate positive Level of AI integration
Reading fidelity high
Study strength medium
n=181
0.3
Ownership structure is positively associated with the level of AI integration among Indonesian commercial banks. Adoption Rate positive Level of AI integration
Reading fidelity high
Study strength medium
n=181
0.3
The study does not directly measure firm-level productivity or establish causal effects of AI adoption. Firm Productivity null_result Firm-level productivity and causal effects of AI adoption
Reading fidelity high
Study strength high
n=181
0.5
The study links AI adoption to SDG 8 and SDG 9 through proposed productivity, innovation, and financial-resilience gains, but these gains are not directly measured in the study. Firm Productivity positive Proposed productivity, innovation, and financial-resilience gains associated with AI adoption
Reading fidelity high
Study strength low
n=181
0.15

Notes