Artificial Intelligence-Assisted Management Decision-Making in the Context of Digital Transformation

Abstract

Artificial intelligence changes managerial decision-making by redistributing prediction, evaluation, and authorization between algorithms and organizational actors. This conceptual paper develops a governance-oriented framework for AI-assisted decisions under digital transformation. A structured integrative review links machine learning, knowledge-based systems, and predictive analytics to three mechanisms: reducing information asymmetry, augmenting bounded managerial cognition, and accelerating feedback-based organizational learning. The framework distinguishes decision support, sequential hybrid decision-making, and constrained delegation, and maps each mode to suitable task conditions and controls. Four propositions explain how task structure, explainability, data quality, and accountability influence decision quality. The study rejects claims of automatic performance improvement and specifies an implementation cycle covering problem definition, baseline comparison, human review, monitoring, and audit. Its principal contribution is a practical and testable model of responsible human–AI decision architecture for service- and information-intensive organizations