Impact of Agentic AI on Managerial Decision-Making and Organizational Performance
Keywords:
agentic AI, managerial decision-making, organizational performance, explainability, algorithmic trust, human–AI collaborationAbstract
Agentic artificial intelligence systems that plan, execute and adapt multi-step tasks with limited human supervision are entering the managerial workflow, yet empirical evidence on their organizational consequences remains scarce. This study examines how task autonomy, data integration capability, explainability and managerial trust influence the quality of managerial decision-making, and whether improved decision quality translates into organizational performance. Primary data were gathered from 120 managers in information technology, manufacturing, banking and retail organizations using a structured questionnaire on a five-point Likert scale. Analysis in IBM SPSS Statistics (Version 27) used reliability testing, descriptive statistics, one-way ANOVA, Pearson correlation and regression. The model explained 61.7 per cent of the variance in decision-making quality, with explainability the strongest predictor and task autonomy the weakest. Decision-making quality in turn predicted organizational performance, indicating that value is realised through better judgement rather than automation alone.
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