The AIMx framework: integrating marketing mix modeling, attribution, and AI-driven analytics for adaptive decision systems

Abstract Artificial intelligence (AI) is transforming marketing from a retrospective reporting function into a predictive decision system. However, research and policy discussions often treat marketing analytics and economic competitiveness as separate domains. This study introduces the AIMx framework (AI-integrated marketing analytics ) , which integrates marketing mix modeling (MMM), multi-touch attribution (MTA), and incrementality testing (IT) within a unified AI-driven feedback architecture. Using a design-science methodology combined with exploratory system dynamics simulation, the study examines how integrated marketing analytics influence forecasting accuracy, resource allocation, and organizational responsiveness under volatile market conditions. The results suggest that coordinated AI-driven measurement reduces decision fragmentation and improves adaptive decision making. At a broader level, the framework highlights how aggregated marketing intelligence may contribute to competitive resilience within digital ecosystems. The study contributes a clear conceptual architecture linking AI-enabled marketing analytics with adaptive competitiveness and data-driven strategic learning. Purpose This study develops the AIMx Framework to show how AI-driven marketing analytics can move beyond standalone measurement methods toward an integrated decision system. The paper addresses the gap between firm-level marketing optimization and broader adaptive competitiveness by specifying how predictive analytics systems may generate scalable learning effects. Design/methodology The study adopts a design-science approach to develop and refine the AIMx framework. The architecture is specified as a recursive feedback system linking predictive measurement, resource allocation, organizational learning, and market volatility. Its structural behavior is examined using exploratory system dynamics simulation to analyze adaptive patterns under varying market conditions. The simulation is intended to test the internal logic of the framework rather than to generate empirical economic forecasts. Findings The results suggest that integrating marketing measurement models within an AI-driven system reduces decision fragmentation and improves responsiveness to market fluctuations. Firms using the integrated framework demonstrate more stable budget adjustments and faster adaptation compared to fragmented approaches. At a broader level, widespread adoption may generate stabilizing feedback effects, although these system-level implications remain conceptual. Practical implications For managers, AIMx provides a structured approach to improving forecasting accuracy and making clearer decisions about how much to spend, where to allocate budgets, and when to adjust investments under changing market conditions. For policymakers, the framework offers a conceptual perspective on how national adoption of AI-enabled marketing analytics may influence broader competitive dynamics, while emphasizing the importance of governance and responsible implementation. Originality The paper contributes a unified framework that integrates major marketing measurement models within a single AI-driven feedback system. By linking predictive marketing optimization with competitive adaptation, the study extends research on digital transformation and dynamic capabilities into a clearer and empirically testable systems model.