Ai And Machine Learning In Strategic Business Decision-Making

Abstract

This study explores the impact of machine learning
on strategic business decision-making through a
robust methodology. Data was gathered from over
500 companies via online surveys, 15 industry
experts through in-depth interviews, and public
discussions analyzed using advanced text mining
techniques. Rigorous data preprocessing, including
cleansing, transformation, integration, feature
selection, and discretization, ensured high-quality
analysis. Multiple linear regression was used to
quantify the influence of AI input, enterprise size,
and the number of AI projects on business
performance, resulting in a predictive equation.
Enterprises were categorized into three clusters—
Start-ups, Mid-Sized Tech Businesses, and Large
Industry Leaders—through K-means clustering,
highlighting differences in AI usage and
performance. A decision tree model, developed
with the C4.5 algorithm and validated through
cross-validation, demonstrated strong predictive
capabilities with high accuracy, recall, and F1
scores. This model identified scenarios where AI
application significantly affects business outcomes,
offering actionable insights for optimizing AI
strategies. The findings corroborate existing
literature and emphasize the positive impact of AI
on enhancing strategic decision-making across
various types of enterprises.