مجلة إقتصاد المال والأعمال
Volume 11, Numéro 1, Pages 1061-1086
2026-05-31

The Impact Of Intelligent Systems Adoption On The Quality Of Accounting Information Within Organizations A Comprehensive Empirical Analysis



Authors : Lounis Naim .

Abstract

This comprehensive academic article investigates the multifaceted impact of intelligent systems adoption on accounting information quality within organizations through integrated literature review, conceptual framework development, and empirical analysis. Drawing from extensive research across 410 organizations over 36-month implementation periods, the study demonstrates that intelligent systems adoption—encompassing cloud-based accounting information systems (AIS), machine learning, robotic process automation (RPA), natural language processing (NLP), and predictive analytics—drives statistically significant improvements across six critical dimensions of accounting information quality: accuracy (30.6% improvement), timeliness (41.5% improvement), completeness (30.9% improvement), and analytical capability (60.0% improvement). The research identifies the mechanisms through which information quality improvements translate to organizational performance benefits, including enhanced decision-making (34% speed improvement), operational efficiency gains (31% improvement), and strengthened risk management (26% improvement). Implementation success depends critically on organizational factors including executive sponsorship (18% weight in success equation), change management capability (16% weight), and data governance frameworks (15% weight). The average financial return on intelligent systems investment reaches 268% over three years, with payback periods averaging 14.2 months. This article presents six comprehensive graphical analyses and multiple quantitative data tables supporting the conceptual and empirical findings, providing both academic rigor and practical guidance for organizational decision-makers navigating intelligent systems adoption in accounting functions.

Keywords

Intelligent systems ; artificial intelligence ; machine learning ; accounting information systems ; information quality ; digital transformation ; organizational performance ; empirical analysis

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