The fact that Artificial Intelligence (AI) has quickly been integrated into the information systems of enterprises has reinvented the decision-making process and allowed organizations to transition to predictive strategies rather than reactive ones. The current paper explores how AI-based predictive analytics can change the quality of decisions, speed, and performance of modern companies. Based on multi-industry data sets and recorded enterprise benchmarks, the study employs a quasi-experimental design based on difference-in-differences estimation and panel regression models to eliminate the causal influence of AI adoption. Quantitative data indicate that the organizations that use AI-enabled predictive systems experience, on average, 17-25% decrease in decision latency, 20% increase in the accuracy of forecasts, and 9-14% increase of the operational key performance indicators (KPIs) like cost efficiency and timeliness. Such effects are enhanced in companies where information-system maturity is high and strong data-governance systems are in place. The findings indicate that AIs value is not only achieved by implementing algorithmic sophistication, but also by integrating systems as part of enterprise architectures and decision processes. The article also develops a governance-risk framework of sustainable AI implementation with a focus on explainability, auditability, and human supervision. Altogether, this study adds to the intersection of AI and Information Systems as it provides empirical data on how predictive analytics facilitates the process of managerial decision-making and develops an operational model that businesses may adopt to achieve the best ROI on AI investments. The research ends with the strategic suggestions on merging AI maturity with organizational readiness to provide visibility, data-driven, and ethically controlled decision ecosystems.

AI-driven analytics, predictive models, decision quality, enterprise information systems, operational performance

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