From Artificial Intelligence Adoption to Trustworthy Governance: Evidence from Artificial Intelligence-Enabled Enterprise Resource Planning Systems

The rapid integration of artificial intelligence into enterprise resource planning systems has expanded organizational capabilities in automation, prediction, and decision support while simultaneously creating new governance challenges involving transparency, accountability, regulatory compliance, cybersecurity, and continuous system assurance. This study develops and empirically evaluates a multi-layer framework for trustworthy Artificial Intelligence governance in Artificial intelligence-enabled enterprise resource planning systems. Drawing on Socio-Technical Systems Theory, the study conceptualizes trustworthy governance through five dimensions: explainability and transparency, human oversight, regulatory compliance, security controls, and artificial intelligence lifecycle assurance. A quantitative cross-sectional research design is employed using organizational-level survey data from respondents involved in artificial intelligence-enabled enterprise resource planning processes and governance. The constructs are operationalized through multi-item measures using a five-point Likert scale, and descriptive statistics, correlation analysis, regression estimation, and Granger causality testing are applied to examine the proposed relationships. The empirical results demonstrate positive relationships between all five governance dimensions and overall trustworthy artificial intelligence governance. Regression findings confirm that explainability and transparency, human oversight, regulatory compliance, security controls, and lifecycle assurance significantly contribute to trustworthy governance. The Granger causality analysis further identifies unidirectional predictive relationships from each governance dimension toward trustworthy artificial intelligence governance, providing additional evidence of their foundational role. The study contributes to artificial intelligence governance research by integrating technical, organizational, regulatory, and lifecycle dimensions within an enterprise resource planning-specific framework and provides organizations with a structured basis for strengthening trustworthy artificial intelligence deployment.