Developing a Governance and Adoption Framework for Generative AI: Ethical and Business Implications for Organizational Stakeholders

Abstract

This dissertation investigates the adoption, implementation, and impact of AI governance frameworks on organizations, focusing on their influence on business performance, stakeholder trust, and ethical concerns such as bias, privacy, and transparency. The study provides a comprehensive analysis of the current landscape of AI governance practices, identifying challenges and opportunities for improvement in organizations of varying sizes and regions.
The research begins by examining the adoption of AI governance practices. Descriptive statistics revealed that many organizations lack formalized governance frameworks, with 237 respondents indicating "Not Formalized" practices. Logistic regression analysis, with an accuracy of 42%, highlighted uneven progress and gaps in formal implementation. This reflects the need for targeted efforts to encourage governance formalization, particularly in organizations with limited resources or regulatory pressure.
To measure the impact of AI governance on ethical issues, paired t-tests demonstrated that governance consistency significantly influences bias reduction (p < 0.001), privacy protection (p = 0.002), and transparency improvements (p < 0.001). Factor analysis identified governance consistency and stakeholder trust as pivotal dimensions for achieving ethical outcomes, underscoring the role of regular audits and consistent implementation strategies.
The study also explores the relationship between AI governance and stakeholder trust. Regression analysis, with an R-squared value of 1.0, confirmed a strong positive association between governance practices and increased stakeholder trust, confidence, and engagement. Transparent communication of governance policies and active stakeholder involvement emerged as essential for fostering trust and ensuring ethical AI adoption.
The final objective assesses the impact of AI governance on business performance. ANOVA results indicated no significant differences across performance metrics (p = 0.312), though KMeans clustering revealed three distinct performance groups. Organizations in Cluster 2—characterized by high operational efficiency and innovation—achieved superior financial and risk management outcomes, showcasing the advantages of mature AI governance frameworks.