Management Accounting Transformation in the Era of Artificial Intelligence: A Structured Literature Review on Efficiency, Cost Control, and Decision-Making
DOI:
https://doi.org/10.58764/j.im.2026.7.245Keywords:
management accounting, artificial intelligence, machine learning, budgeting, management control, decision-making, structured literature reviewAbstract
Aim: This article examines the role of artificial intelligence (AI) in four core management accounting functions: cost analysis, budgeting, management control, and decision-making. Method: A structured literature review synthesizes findings from systematic reviews, case studies, experiments, a dissertation, and professional reports to distinguish demonstrated predictive benefits from broader claims about organizational performance. The review is limited by the absence of a complete database export and a study-specific PRISMA flow; it should therefore be read as a structured synthesis of accessible literature rather than a fully reproducible systematic review. Results: Machine-learning models can match or exceed human estimates in selected cost-estimation tasks when adequate historical data are available. In budgeting, machine learning can improve prediction accuracy, but predictive accuracy alone does not establish causal effects or guarantee appropriate resource allocation. In management control, digital tools support more timely and anticipatory monitoring, although the available evidence does not always isolate the effect of AI itself. For decision-making, the usefulness of AI depends on data quality, model explainability, user trust, professional competence, and the preservation of human accountability. Novelty/Implications: The novelty lies in organizing the evidence by management accounting function and strength of evidence, separating technical outcomes (prediction accuracy) from organizational outcomes. AI is best positioned as support for professional judgment rather than a replacement for management accountants. Organizations need data governance, model evaluation, and training to ensure responsible adoption, and the article proposes an empirical research agenda, particularly in Indonesian organizations, on AI use, efficiency, cost control, and decision quality.
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