AI-Driven Decision Intelligence in Managerial Economics: Leveraging Predictive Analytics and Intelligent Technologies for Strategic Decision-Making

Abstract

Managerial economics applies economic reasoning to decisions concerning demand, pricing, cost, production, resource allocation, investment, risk and competition. The growth of digital business has expanded the information available for these decisions, while artificial intelligence (AI), predictive analytics and business intelligence have increased the ability to process that information. This paper develops a conceptual framework for AI-driven decision intelligence in managerial economics. It connects established economic tools with predictive and intelligent technologies so that managers can move from historical information to forecasts, alternatives and economically justified actions. The paper reviews literature on data-driven decision-making, business intelligence, predictive analytics, AI-enabled strategy, digital transformation, big data capabilities, risk management and responsible AI. It then considers applications in demand forecasting, pricing, production, resource allocation, investment, marketing, supply chains and competitive strategy. The paper argues that the value of AI depends not simply on prediction accuracy but on the quality of the economic objective, data, managerial interpretation and implementation. It proposes an integrated decision cycle in which managerial economics defines the decision problem, analytics estimates future conditions, intelligent technologies support evaluation of alternatives, and managerial judgment remains responsible for the final choice. Data quality, organizational capability, explainability and ethical governance are identified as important conditions for sustained value.