AI-Powered Information Governance: Balancing Automation and Human Oversight for Optimal Organization Productivity

Abstract

This study employs a mixed-methods approach to examine the optimal balance between AI-powered automation and human oversight in information governance frameworks, aiming to enhance organizational productivity, efficiency, and compliance. Quantitative data collected from 384 respondents were analyzed using Pearson correlation, regression models, and Structural Equation Modeling (SEM). The results reveal strong positive correlations between AI automation levels and both organization size (r = 0.55, p < .01) and AI adoption duration (r = 0.62, p < .01). Regression analysis indicates that higher levels of AI automation significantly improve error reduction (β = 1.12, p < .001) and compliance (β = 1.05, p < .001), especially in larger organizations with longer AI adoption periods. SEM findings highlight that human oversight positively impacts error reduction (β = 0.65, p < .001) and compliance improvement (β = 0.72, p < .001), and the interaction between human oversight and AI automation further enhances these outcomes (error reduction: β = 0.32, p < .001; compliance improvement: β = 0.35, p < .001). The qualitative analysis, involving thematic extraction from industry reports, reveals ethical challenges such as data quality issues, algorithmic bias, and privacy concerns. Hence, it is necessary to integrate human oversight to ensure ethical standards and build stakeholder trust in AI-driven systems. The study concludes with practical recommendations for organizations: establishing transparent AI governance frameworks, investing in continuous training for employees, and regularly auditing AI processes to mitigate risks. By addressing both the technological and ethical dimensions, organizations can implement AI-powered information governance that not only boosts productivity and efficiency but also ensures compliance and ethical integrity.