AI-Driven Capital Budgeting and Managerial Decision Quality in Global Firms

Abstract

Purpose: This study investigates the effect of AI-Driven Capital Budgeting on Managerial Decision Quality in multinational corporations and examines whether Organizational Readiness strengthens this relationship. The study evaluates how Predictive Analytics Capability, Intelligent Investment Evaluation, AI-Based Risk Management, and Automated Decision Support Systems jointly improve strategic investment decisions under varying organizational conditions.

Methodology: The study employs a balanced longitudinal panel comprising 41 Fortune Global 500 multinational corporations observed between 2015 and 2025, generating 451 firm-year observations. Secondary data were integrated from corporate financial and governance reports, the Stanford Artificial Intelligence Index, the World Bank, the OECD, and the McKinsey State of AI database. A moderated two-way fixed-effects panel regression model was estimated following comprehensive construct validation, panel unit root tests, diagnostic testing, Hausman specification analysis, and robustness assessments to establish reliable causal inference.

Results/Analysis: The findings demonstrate that Predictive Analytics Capability, Intelligent Investment Evaluation, AI-Based Risk Management, and Automated Decision Support Systems each exert positive and statistically significant effects on Managerial Decision Quality. These effects operate through improved investment forecasting, enhanced project evaluation, stronger risk intelligence, real-time decision support, and more effective strategic resource allocation. Organizational Readiness significantly strengthens these relationships by enhancing digital infrastructure quality, employee AI competence, top management support, data governance effectiveness, and innovation-oriented culture. Robustness and diagnostic analyses confirm the stability, validity, and consistency of the estimated relationships across alternative model specifications.

Originality/Value: This study extends Dynamic Capabilities Theory and Socio-Technical Systems Theory by conceptualizing AI-Driven Capital Budgeting as an integrated higher-order organizational capability whose effectiveness depends on organizational readiness rather than technology adoption alone. It introduces a multidimensional international framework linking complementary AI capabilities, organizational readiness, and managerial decision quality using longitudinal evidence from globally operating firms. The findings provide practical guidance for corporate executives, investors, and policymakers seeking to improve strategic investment performance through integrated AI-enabled capital budgeting systems.

Type of Paper: Empirical Research paper.