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Engineering and Technology OPEN ACCESS

The Algorithmic Transformation of Organizational Governance: A Longitudinal Analysis of Predictive Risk Scoring, Innovation Integration, And the Psychosociological Dynamics of AI-Mediated Change Management

Sebastian Voggel
Department of Strategic Management, University of Melbourne, Australia
tajet 2026
VOL. 8 / NO. 01 JANUARY
FULL TEXT PP. 283-287
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VOLUME 8
ISSUE 01
YEAR 2026
PAGES 283-287
RESEARCH OVERVIEW

Abstract

This research investigates the profound shift in organizational management paradigms precipitated by the integration of artificial intelligence (AI) and machine learning technologies into decision-making infrastructures. Specifically, the study examines the convergence of classical management theory with modern algorithmic predictive modeling to manage organizational change and mitigate operational risk. By synthesizing diverse theoretical frameworks-ranging from the psychology of action and commitment to the planetary costs and superintelligent trajectories of AI-the article provides an exhaustive analysis of how modern enterprises transition from manual, intuition-based governance to data-driven, automated systems. Central to this inquiry is the role of Predictive Risk Scoring within Change Advisory Boards (CAB) and the implementation of agile, lean, and data-driven methodologies to wire innovation into the organizational DNA. The research further explores the "anticipatory" nature of modern news and information infrastructures, noting how algorithmic expectations shape professional reality. Through a detailed qualitative assessment of existing theoretical literature, patent filings, and empirical studies, this article identifies a critical gap in current management education: the reconciliation of human-centric leadership with the cold efficiency of automated risk assessment. The findings suggest that successful AI integration is not a "moon shot" but a series of incremental, psychologically grounded shifts that respect the moderating role of stakeholder commitment and the inherent human biases against automated tools. The study concludes with a strategic roadmap for "building to innovate," emphasizing that the future of the successful manager lies in the balance between superintelligent capabilities and the foundational principles of human resource management.

RESEARCH TOPICS

Keywords

Artificial Intelligence Change Management Predictive Risk Scoring Organizational Innovation

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