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

Asian Journal of Research in Computer Science, volume 17, issue 10, 2024[10.9734/ajrcos/2024/v17i10513]

22 Pages Posted: 20 Dec 2024

See all articles by Sunday Joseph

Sunday Joseph

Ashland University

Titilayo Modupe Kolade

Federal Government of Nigeria - Ministry of Foreign Affairs, Nigeria

Onyinye Obioha Val

University of the District of Columbia

Olubukola Omolara Adebiyi

Ulster University - Centre City House

Olumide Samuel Ogungbemi

Centennial College

Oluwaseun Oladeji Olaniyi

University of the Cumberlands

Date Written: October 19, 2024

Abstract

This study employs a mixed-methods approach to examine the optimal balance between AIpowered 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.

Keywords: Mixed methods, AI automation, human oversight, information governance, organizational productivity

Suggested Citation

Joseph, Sunday Abayomi and Kolade, Titilayo Modupe and Obioha Val, Onyinye and Adebiyi, Olubukola Omolara and Ogungbemi, Olumide Samuel and Olaniyi, Oluwaseun Oladeji, AI-Powered Information Governance: Balancing Automation and Human Oversight for Optimal Organization Productivity (October 19, 2024). Asian Journal of Research in Computer Science, volume 17, issue 10, 2024[10.9734/ajrcos/2024/v17i10513], Available at SSRN: https://ssrn.com/abstract=4995930 or http://dx.doi.org/10.9734/ajrcos/2024/v17i10513

Sunday Abayomi Joseph

Ashland University ( email )

401 College Avenue
Ashland, OH Ashland 44805
United States

Titilayo Modupe Kolade

Federal Government of Nigeria - Ministry of Foreign Affairs, Nigeria ( email )

Onyinye Obioha Val

University of the District of Columbia ( email )

Olubukola Omolara Adebiyi

Ulster University - Centre City House ( email )

Olumide Samuel Ogungbemi

Centennial College ( email )

Oluwaseun Oladeji Olaniyi (Contact Author)

University of the Cumberlands ( email )

6178 College Station Drive
Williamsburg, KY 40769
United States

HOME PAGE: http://www.ucumberlands.edu

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