The rapid evolution of artificial intelligence (AI) technologies has fundamentally transformed decision-making processes in data-intensive service industries. This mixed-methods study investigates how AI-enabled decision support systems (AI-DSS) impact managerial performance across multiple service sectors. Drawing on knowledge-based theory and technology acceptance frameworks, we employ a sequential explanatory design combining quantitative survey data (n=350 managers) with qualitative case studies (6 organizations). Results demonstrate that AI-DSS significantly enhances decision-making speed (β=0.42, p<0.001) and accuracy (β=0.38, p<0.001), with perceived usefulness and trust serving as critical mediators. Data quality moderates these relationships, strengthening performance outcomes in high-quality contexts. Qualitative findings reveal implementation challenges, including organizational resistance, technical integration complexities, and the need for explainable AI systems. The study contributes a comprehensive framework integrating AI-DSS features, organizational factors, and performance outcomes, offering evidence-based guidelines for practitioners. Findings indicate that successful implementation requires robust data infrastructure, transparent AI systems, comprehensive training programs, and adaptive change management strategies tailored to industry contexts.