Utilization Of Artificial Intelligence In Demand Forecasting: A Qualitative Study Of Business Actors

Abstract

Market uncertainty in the era of volatility, uncertainty, complexity, and ambiguity (VUCA) demands that businesses possess high predictive acumen. This study explores the use of Artificial Intelligence (AI) in demand forecasting through a descriptive qualitative approach. The study focuses on a deeper understanding of how the integration of AI technology transforms managerial decision-making processes and the dynamics of its adaptation within the digital business ecosystem in Indonesia. In-depth interviews were conducted with ten informants holding managerial and operational analyst positions in the e-commerce and technology retail sectors in Indonesia. The results show that the application of AI, particularly based on Machine Learning and Deep Learning, is able to reduce subjective human bias and capture non-linear data patterns that conventional statistical methods fail to identify. This implementation significantly improves inventory estimation accuracy, minimizes holding costs, and prevents loss of sales momentum (stockouts). However, the transition to an AI-based forecasting system faces significant structural challenges, including data quality issues (data silos), limited local talent with multidisciplinary competencies, and cultural resistance within the organization. From the perspective of managerial decision-making theory, AI acts as a cognitive amplification tool, shifting the paradigm from pure intuition to data-driven decision-making. This study concludes that the success of AI implementation is determined not only by the sophistication of the algorithm, but also by the readiness of data governance and the alignment of inclusive organizational strategies