Artificial Intelligence (AI) is increasingly transforming the landscape of corporate financial strategy, particularly in the domain of long-term investment and capital budgeting. Traditional evaluation methods—such as Net Present Value (NPV), Internal Rate of Return (IRR), scenario analysis, and real options valuation—are essential tools for strategic decision-making but are constrained by rigid assumptions, historical data limitations, and the inability to adapt quickly to uncertainty. AI-powered models, by contrast, offer dynamic, data-driven approaches that enhance forecasting accuracy, risk assessment, and strategic flexibility. Through machine learning, natural language processing, and predictive analytics, corporations can analyze vast amounts of financial and non-financial data, identify hidden patterns, and generate real-time insights to guide investment decisions. This paper examines how AI augments the capital budgeting process by refining cash flow estimation, enabling advanced scenario simulations, and integrating external factors such as macroeconomic volatility, market sentiment, and ESG considerations into corporate financial planning. The study further investigates the role of AI in real options valuation, where adaptive models provide managers with enhanced flexibility in responding to evolving market conditions, technological disruptions, and competitive pressures. In addition, ethical and governance implications are discussed, particularly regarding the transparency and interpretability of AI-driven financial decisions. By bridging traditional corporate finance theories with advanced AI applications, this research highlights the potential for firms to optimize capital allocation, reduce uncertainty in investment planning, and maximize long-term shareholder value. Ultimately, the study argues that AI is not merely a supportive tool but a transformative force capable of redefining corporate financial strategy and positioning organizations for sustainable growth in increasingly complex global markets.
Roy et al. (Sun,) studied this question.