AI-Augmented Strategic Decision-Making in Business Organizations

Abstract

This paper explores the complex interplay between human judgment and AI-driven machine learning (ML) algorithms, highlighting both the opportunities and challenges this relationship presents. On the one hand, AI enhances decision-making through predictive accuracy, data-driven insights, and resource optimisation. On the other, issues such as algorithm aversion, automation bias, and a lack of trust hinder its full potential. The study critically examines how over-reliance on AI may lead to automation bias, while skepticism arising from AI errors contributes to algorithm aversion. To address these concerns, the paper advocates for the use of explainable AI (XAI), open systems, and user-centric design to improve transparency and build trust. Ethical considerations such as accountability, equity, and the prevention of bias are also discussed, with proposed solutions including ethical audits, legal compliance, and aligning AI systems with organisational values.