AI-Driven Decision-Making in Organizations: Enhancing Executive Strategy, Mitigating Bias, and Navigating the Trust Deficit

Today, AI plays an ever-growing role in organizationally supported decision-making in fields ranging from executive forecasting to risk analysis, personnel selection to customer analytics, and planning to operations. This literature review explores the ways in which AI may be able to improve decisions through augmented cognition, but simultaneously carries risks linked to issues of opacity, bias, automation bias, and overreliance. In particular, the thesis presented here is that the value of AI in management involves augmenting executive decision-making rather than entirely replacing it: algorithms increase scope, speed, uniformity, prediction, and pattern recognition, while executives supply the contextual understanding, ethical responsibility, and strategic judgment. The present review distinguishes between cognitive biases that originate from bounded rationality and algorithmic biases based on data sources, algorithm design, contexts of deployment, and reinforcement from feedback cycles. It further highlights the importance of explainability, auditability, domain suitability, governance, education, and even challenge ability as factors in managerial trust. A framework for hybrid decision governance is described, which includes the design of decision rights, validation of models, explainable AI, bias audits, escalation with human oversight, and AI training for executives. In conclusion, it is argued that AI achieves its greatest strategic utility as a transparent collaborator in decision-making rather than as a unilateral arbiter.