AI-Enabled Financial Strategy and Strategic Decision-Making

Abstract

-Artificial Intelligence (AI) is increasingly influencing how financial decisions are analyzed, evaluated, and executed. Traditionally, financial strategy has relied on managerial judgment, financial models, historical information, and established frameworks for decisions relating to capital allocation, investment, financing, liquidity, and risk. The emergence of machine learning, predictive analytics, natural language processing, and generative AI is expanding these capabilities by enabling financial professionals to process large volumes of information, identify patterns, generate forecasts, and evaluate alternative scenarios. This paper examines the transformation of financial strategy through AI-enabled financial strategy, defined as the integration of AI-driven analytical capabilities into strategic financial decision-making while retaining human judgment and oversight. Rather than examining AI solely as an automation tool, the paper positions it as an emerging strategic capability that can influence forecasting, capital allocation, investment analysis, risk management, liquidity management, and financial intelligence. Existing research documents AI applications across individual financial functions, but these applications are often examined separately. Limited attention has been given to how AI-enabled capabilities collectively influence strategic financial decision-making and under what organizational conditions they translate into measurable outcomes. The paper therefore develops a conceptual framework connecting AI capabilities, financial intelligence, strategic decision-making, and financial outcomes. The paper further proposes AI Absorptive Capacity as an organizational capability that influences the extent to which AI-generated insights are assimilated and applied to financial decisions. Data quality, human expertise, AI literacy and trust, and governance are also identified as important conditions affecting value realization. The study concludes by identifying avenues for empirical research into the relationship between AI adoption, decision quality, organizational capability, and financial performance.