Keywords
AI Governance
Explainable AI
Organizational Governance
Stakeholder Trust
How to Cite
Plaudit
Abstract
Artificial intelligence (AI) is increasingly integrated into organizational governance, reshaping decision-making processes, accountability mechanisms, and stakeholder relationships. This study investigates the differences between AI-assisted governance systems and traditional governance approaches regarding accountability and stakeholder trust. A narrative literature review was conducted by analyzing ten scholarly publications published between 2021 and 2026 across diverse sectors, including public administration, healthcare, finance, corporate governance, and human resource management. The review findings reveal that AI-assisted governance systems generally enhance accountability through automated auditing, explainable decision-making, predictive risk assessment, and continuous compliance monitoring. Several studies reported improvements in governance performance, ethical compliance, and risk management compared with conventional governance models. In addition, stakeholder trust tends to increase when AI systems incorporate transparency, fairness, and explainability features that allow users to understand and evaluate algorithmic decisions. Despite these advantages, important challenges remain, including unclear responsibility attribution, the lack of standardized AI governance and auditing frameworks, and potential trust erosion caused by excessive dependence on automated systems. The effectiveness of AI-assisted governance is also influenced by organizational context, leadership commitment, governance maturity, and the extent of human oversight. Overall, AI-assisted governance offers substantial potential to strengthen accountability and stakeholder trust when supported by robust ethical safeguards, transparency measures, and clearly defined responsibility structures. These findings contribute to the ongoing discussion of responsible AI governance and provide practical insights for organizations pursuing governance innovation.
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Copyright (c) 2026 Ma Tin Cho Mar, Ing. Nikolai Fabian Sebastián Yucra Añazco, Delia Nieves Coaquira Pari