MIS for Industry 5.0: Governing Structures for Human-AI Collaboration

Abstract Book of the 10th International Conference on Research in Management

Year: 2026

[PDF]

MIS for Industry 5.0: Governing Structures for Human-AI Collaboration

Prof. Dr. Karikoga Norman Gorejena

ABSTRACT:

The paper consolidates existing literature on human-centred AI, socio-technical systems design, and organizational governance issues related to Human-AI Teaming (HIT) in Industry 5.0 settings. It integrates findings from recent studies examining human-cantered AI design, cognitive automation, trust mechanisms, and governance frameworks across manufacturing sectors. Its purpose is to propose a framework that distils design principles and metrics for MIS governance of HIT so that it enhances human role instead of minimizing it. To achieve this goal, the paper covers HIT from three perspectives including technical integration, organizational adaptation as well as performance optimization. Findings in literature indicate that governance of HIT requires transitioning from categorically thinking about AI governance to a dimensional approach that considers agency as a spectrum. Five Pillars that surfaced throughout research include Human-cantered design, Explainability/Transparency, Dynamic task allocation, Role delineation, and Hybrid Intelligence which should be implemented across governance layers. Such frameworks should be able to dynamically shift decision-making authority as appropriate and account for both real-time monitoring and retroactive audit trails to ensure algorithmic decisions can always be explained and overridden if necessary. Challenges to consider include the “double black box” created from machine opacity and human thought processes, risk of de-skilling workers, and that human-machine teams may inherently underperform humans or AI working alone. Furthermore, Research shows institutions with cross-functional governance are highly likely to succeed at AI implementation.

Keywords: Management Information Systems, Governance Frameworks, Human-Centric AI, Socio-Technical Systems.