Enhancing Strategic Decision Performance Through AI: The Task-Technology Fit and Managerial Behaviour Link - Othman Boulitama; Brahim Sabiri; Nissrine Mouchtakir; Driss Rahli; Karim Sabri (Ital Publication, 2026)
This study examines how artificial intelligence improves strategic decision-making by focusing on the alignment between managerial task requirements and AI capabilities through the lens of Task Technology Fit. The objective is to explain whether the performance benefits associated with AI emerge from mere adoption or from a more precise fit between task demands, AI characteristics, and actual patterns of use. Methodologically, the study relies on a quantitative design based on survey data collected from 360 managers working in medium and large enterprises in Morocco. The proposed research model was tested using Partial Least Squares Structural Equation Modeling to assess the direct and indirect relationships among task characteristics, AI characteristics, Task AI Technology Fit, effective AI use, and strategic decision-making performance. The findings show that both task characteristics and AI characteristics positively and significantly influence Task AI Technology Fit. In turn, this fit strongly enhances effective AI use and strategic decision-making benefits, while effective AI use partially mediates the relationship between fit and performance. These results indicate that organizational value does not arise from symbolic or superficial AI adoption but from purposeful integration aligned with strategic requirements. The study’s novelty lies in extending Task Technology Fit theory to AI enabled strategic contexts and in demonstrating that AI should be understood not as a substitute for managerial judgment but as a mechanism for cognitive augmentation and performance enhancement.