TY - JOUR AU - Ramdani, Rama Putra AU - Mulyana, Rahmat AU - Adi, Taufik Nur PY - 2025/08/18 Y2 - 2025/11/14 TI - Ambidextrous AI Governance Model for Advancing State-Owned Bank in Indonesia Digital Transformation Through COBIT 2019 Traditional and DevOps JF - Jurnal Teknik Informatika (Jutif) JA - J. Tek. Inform. (JUTIF) VL - 6 IS - 4 SE - Articles DO - 10.52436/1.jutif.2025.6.4.4835 UR - https://jutif.if.unsoed.ac.id/index.php/jurnal/article/view/4835 SP - 1745-1768 AB - <p class="ABSTRAKTITLE" style="margin: 0in; text-align: justify;"><span lang="EN-US" style="font-weight: normal;">Integrating artificial intelligence into the banking sector accelerates digital transformation, but it also presents governance challenges, particularly in striking a balance between innovation and regulatory compliance, risk management, and operational control. This research proposes an ambidextrous AI governance model by combining two distinct yet complementary mechanisms from COBIT 2019: the structured, control-oriented Traditional framework and the agile, adaptive DevOps Focus Area. This dual approach enables organizations to pursue innovation and maintain governance stability simultaneously. The study investigates BankCo’s, a state-owned bank in Indonesia that is undergoing a systemic digital transformation and applies the Design Science Research (DSR) methodology with a case study approach. Collecting data through five semi-structured interviews with key IT Governance, Risk, and Compliance stakeholders and triangulated with internal policy documents, annual reports, and audit trails. The analysis identified two prioritized Governance and Management Objectives (GMOs), MEA03 (Managed Compliance with External Requirements) and APO12 (Managed Risk), based on design factors, regulatory alignment (POJK No. 11/2022 and SOE Minister Regulation No. PER-2/MBU/03/2023), and agile governance needs. A maturity gap analysis revealed areas for improvement across people, process, and technology dimensions, with the proposed model raising governance capability from 3.55 to 3.95. The proposed model applies multidimensional prioritization through Resource-Risk-Value (RRV) analysis. This study presents a practical and auditable approach to ethical AI governance that strikes a balance between innovation and accountability. The model supports digital transformation in banks and contributes to information systems governance by linking the ethical use of AI with agile yet compliant practices in regulated environments.</span></p> ER -