Navigating the Algorithmic Boardroom: How AI-Driven Predictive Analytics Alter Executive Risk Appetite and Strategic Capital Allocation Decisions

Author

Dr. Mazaharunnisa, Chenchu Swaraj Kumar

Abstract

 AI-driven predictive analytics now sit inside many of the routines senior executives use to weigh uncertainty, rank investment options, and commit capital. This study asks whether heavier use of these tools goes along with a higher executive risk appetite and a bigger share of capital in high-uncertainty strategic projects. Building on upper echelons theory, the behavioral theory of the firm, and prospect theory, it tests four hypotheses on a cross-sectional dataset of 300 senior executives, using a chi-square test of independence, one-way ANOVA with Tukey post-hoc comparisons, and two simple linear regressions. The dataset is simulated for illustration (Section 4.1), so the results show how the analytical pipeline works and should not be read as empirical findings about real executives. Within this dataset, AI adoption level and risk-appetite category are significantly associated, χ²(4, N = 300) = 79.17, p < .001, Cramér's V = .36. Mean risk appetite climbs from low to medium to high adopters (4.15, 4.75, 5.36), F(2, 297) = 44.75, p < .001, η² = .23. AI analytics usage predicts risk appetite (b = 0.58, R² = .36), and risk appetite in turn predicts the percentage of capital placed in high-risk projects (b = 3.67, R² = .35). Supplementary analyses suggest these relationships hold after controlling for firm size, sector and executive role, and that the link between usage and capital allocation runs through risk appetite. The pattern fits a view of predictive analytics as an input that reduces uncertainty and so widens, rather than narrows, the risks executives will accept. It also raises governance questions about over-reliance and model risk.

Keywords

predictive analytics; artificial intelligence; risk appetite; capital allocation; strategic decision-making; corporate governance; executive behavior

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