Purpose – This research investigates the potential impacts of AI-automated corporate administration on the efficiency of decision-making, transparency in governance, the effectiveness of risk control, and accountability in organizations. It also analyzes the role of transparency in mediating the accountability relationship. Design/methodology/approach - The research employed a transparent synthetic demonstration dataset that contained 300 managers, governance and compliance professionals, risk and audit staff, and medium and large corporate IT managers and employees. The research utilized a quantitative, cross-sectional, and explanatory design. Five constructs were assessed using six items each and a five-point Likert scale. Factor reliability was assessed using reliability measures and factor adequacy. These scales were used for correlation analysis and multiple regression, which included organizational controls. A bootstrap mediation test was run with 5,000 resamples. Findings - The proposed scales were reliable (Cronbach's alpha = .878 to .894). AI-automated administration positively impacted the efficiency of decision-making (beta = .536), the transparency of governance (beta = .582), the effectiveness of risk control (beta = .495), and accountability in organizations (beta = .469), all p < .001. Transparency was a partial mediating factor in the accountability relationship (indirect effect = .227, 95% bootstrap CI [.154, .304]).