From Data to Decisions: The Role of Artificial Intelligence and Large Language Models in Organizational Decision-Making

The emergence of Artificial Intelligence (AI), and specifically Large Language Models (LLMs), represents one of the most consequential shifts in how organizations gather, process, and act on information. Traditional decision-making frameworks, long dependent on structured data and deterministic analytics, are being reshaped by systems capable of interpreting ambiguous language, synthesizing unstructured knowledge, and surfacing contextually relevant insights in real time. This paper examines how AI and LLMs are transforming organizational decision-making across three dimensions: the evolution of decision support systems, the integration of Retrieval-Augmented Generation (RAG) pipelines as an enterprise intelligence layer, and the human factors that determine whether AI-augmented decisions are better than unaided ones. Drawing on relevant literature in information systems, organizational behavior, and AI research, the paper argues that the most significant barrier to effective AI-driven decision-making is not technological capability but organizational readiness — specifically, the alignment between data infrastructure, human cognition, and institutional trust. Practical implications for managers, information systems designers, and policymakers are discussed.