Background: Artificial intelligence (AI) is increasingly reshaping administrative systems in higher education; however, evidence regarding the differential effects of specific AI technologies on institutional performance remains limited. Understanding whether Optical Character Recognition (OCR), Chatbots and Virtual Assistants (CVA), and Robotic Process Automation (RPA) contribute differently to administrative outcomes is essential for evidence-informed digital transformation. Objectives: This study aimed to comparatively assess the effects of OCR, CVA, and RPA on administrative efficiency and effectiveness (AEE), decision-making (DM), and job redesign (JR), and to determine whether organizational culture moderates these relationships in Nigerian tertiary institutions. Methods: A cross-sectional quantitative study was conducted among 323 respondents from Nigerian tertiary institutions. Composite measures of OCR, CVA, RPA, AEE, DM, JR, and organizational culture were analysed using descriptive statistics, Pearson correlation, multiple regression, relative importance analysis, dominance analysis, and hierarchical moderation regression. Results: OCR demonstrated the highest mean AI score (mean 3.882, SD 0.831), followed by CVA (mean 3.849, SD 0.873) and RPA (mean 3.811, SD 0.905). Correlation analysis showed generally weak associations, with the strongest relationship observed between RPA and AEE (r = 0.161). The regression model predicting AEE was statistically significant (R² = 0.031; F = 3.410; p = 0.018), with RPA emerging as the only significant predictor (β = 0.148, p = 0.004). Models predicting DM (R² = 0.007; p = 0.524) and JR (R² = 0.002; p = 0.920) were not significant. Relative importance analysis showed that RPA contributed 83.1% of explained variance in AEE, while CVA contributed 85.1% in DM. Dominance analysis identified RPA as dominant for AEE (incremental R² = 0.0258) and JR (0.0009), and CVA for DM (0.0059). Organizational culture did not significantly moderate AI relationships across AEE (β = −0.109, p = 0.376), DM (β = 0.120, p = 0.323), or JR (β = 0.018, p = 0.891). Conclusion: AI technologies demonstrate outcome-specific rather than uniform effects within tertiary administrative systems. RPA showed the strongest empirical association with operational efficiency, whereas CVA demonstrated greater relative contribution to decision-support functions. Broader effects on decision-making, job redesign, and culture-dependent transformation remain limited. Recommendation: Tertiary institutions should adopt differentiated AI implementation strategies that align specific technologies with targeted administrative objectives, while strengthening infrastructure, workforce capacity, and governance frameworks to support sustainable digital transformation. Thus, this study provides empirical evidence that AI adoption in tertiary institutions should move beyond technology availability towards function-specific deployment. Identifying which AI applications contribute to particular administrative outcomes can support more effective, equitable, and context-responsive digital transformation policies.