MJAAR

From Technical Tool to Leadership Imperative: Managing Artificial Intelligence in Contemporary Organizations and Implications for Advanced Business Education

Dr. Arinaitwe Julius, Asiimwe Isaac Kazaara


Abstract

This study examined the transition of artificial intelligence (AI) from a peripheral technical utility to a core strategic and leadership imperative within contemporary organizations, and explored the corresponding implications for advanced business education curricula. Employing a quantitative cross-sectional survey design, data were collected from 320 organizational leaders, middle managers, and business educators drawn from six industrial sectors finance, healthcare, manufacturing, retail, education, and logistics across Uganda and the broader East African region. Respondents were selected through stratified random sampling to ensure adequate sectoral and hierarchical representation. The survey instrument comprised 48 Likert-scaled items measuring AI integration levels, leadership readiness, organizational transformation indicators, and business education curriculum adequacy. Descriptive univariate statistics characterized the central tendencies and dispersions of AI adoption across sectors. Bivariate Pearson correlation analysis identified statistically significant relationships among AI strategic alignment, leadership competency, and organizational performance outcomes. Exploratory factor analysis (EFA) with principal axis factoring revealed the latent constructs undergirding AI leadership capabilities, while principal component analysis (PCA) confirmed a parsimonious two-component structure explaining 45.7% of the total variance in AI competency constructs. Results revealed that the finance sector demonstrated the highest AI integration score (M = 78.4%, SD = 8.2), while educational institutions lagged significantly (M = 52.3%, SD = 11.6). Strong positive correlations were observed between AI strategic alignment and technological investment readiness (r = 0.77, p < 0.001). Factor analysis extracted three interpretable factors—Strategic AI Leadership, Operational AI Readiness, and Ethical AI Governance—together explaining 63.4% of common variance. The study concluded that advanced business education programs must urgently integrate AI management competencies, data governance frameworks, and ethical AI leadership modules to adequately prepare future organizational leaders for the demands of the fourth industrial revolution.

Keywords

Artificial Intelligence Leadership Organizational Management Business Education Factor Analysis Principal Component Analysis AI Governance
Metropolitan Journal of Academic and Applied Research

Cite This Article

Dr. Arinaitwe Julius & Asiimwe Isaac Kazaara (2026). From Technical Tool to Leadership Imperative: Managing Artificial Intelligence in Contemporary Organizations and Implications for Advanced Business Education. Metropolitan Journal of Academic and Applied Research, 5(4). https://journals.miu.ac.ug/pages/article.php?article_id=368

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