Artificial Intelligence–Enabled Products and Their Effects on Managerial Judgment, Strategy, and Organizational Performance

Despite growing organizational investment in artificial intelligence (AI), the mechanisms through which AI-enabled tools influence managerial judgment and translate into measurable performance outcomes remain theoretically underdeveloped. This study addresses this gap by examining the relationships between AI adoption and usage, AI-influenced decision-making quality, and organizational performance across managerial levels and industry sectors. A quantitative cross-sectional survey was conducted among 100 managers from six industries: technology, manufacturing, financial services, healthcare, education, and retail. Three latent constructs—AI usage and perception, AI influence on decision-making, and organizational performance—were operationalized using multi-item Likert-scale instruments and tested for reliability. Hypotheses were evaluated using ordinary least squares (OLS) regression, complemented by correlation and group-level comparative analyses across managerial hierarchies and AI adoption tenures. The findings support all proposed relationships. AI usage exhibits a strong positive association with decision-making quality, which in turn significantly predicts organizational performance. Additionally, AI usage has a direct effect on performance, indicating that decision-making quality partially mediates this relationship. Group-level analysis reveals variation in AI engagement and perceived impact across managerial levels and industries, with longer AI adoption tenure generally linked to stronger performance outcomes. From a practical standpoint, the results suggest that organizations must go beyond mere AI deployment and focus on enhancing managerial capability for AI-informed decision-making. Decision-making quality emerges as a critical pathway through which AI adoption drives performance gains, highlighting the importance of targeted training, integration infrastructure, and governance frameworks. This study contributes to the literature by empirically validating a model that links AI usage to organizational performance through the mediating role of managerial decision-making. By incorporating contextual variables such as managerial level, industry sector, and adoption tenure, the research provides a more nuanced understanding of how AI’s organizational impact varies across different settings.