Identifying Influential Factors on the Efficiency of AI-Based Budgeting Algorithms

Abstract

This study aims to identify the key factors influencing the efficiency of AI-based budgeting algorithms in financial organizations. This qualitative study employed a phenomenological approach to explore the experiences of financial and IT professionals. Data were collected through semi-structured interviews with 19 financial managers, budgeting experts, and AI specialists in Tehran. Interviews continued until theoretical saturation was reached, and data were analyzed using thematic analysis with NVivo software. The results indicated that the efficiency of AI-based budgeting is influenced by four main categories of factors: organizational, technological, human, and environmental. In the organizational dimension, senior management support and flexible organizational structures played a crucial role. Technological factors included data quality, system integration, and information security. Human factors such as managerial attitudes and employee skills were found to be highly significant. Finally, economic fluctuations and regulatory frameworks were identified as key environmental factors affecting AI-based budgeting. The findings suggest that the successful implementation of AI-based budgeting requires improvements in technological infrastructure, employee training, increased managerial support, and the development of appropriate policies to facilitate AI adoption. This study provides valuable insights for financial managers and policymakers in developing effective strategies for AI-driven budgeting.