Research on the Impact of Generative AI on the Quality of Management Accounting Decisions: Evidence from Manufacturing Firms

Abstract

Generative artificial intelligence (GenAI) is moving from experimentation to organizational infrastructure in accounting and finance. Yet firm-level evidence on how it shapes management accounting decision quality in manufacturing remains limited, especially in contexts where financial, operational, engineering, and service information are tightly intertwined. This paper revises the question in a more analytically cautious way: rather than claiming direct observation of internal decision processes, it examines what can reasonably be inferred from public corporate disclosures about the ways GenAI-related capabilities may influence management accounting decision quality. The study adopts an interpretive multiple-case design and analyzes three major Chinese manufacturing firms - Midea Group, Haier Smart Home, and Dongfang Electric - using official annual and semi-annual reports, corporate disclosures, and recent AI-and-accounting literature. The findings suggest that GenAI does not improve management accounting decision quality primarily by replacing managerial judgment. Instead, its potential effects appear to operate through three linked mechanisms: information enrichment, analytical augmentation, and organizational embedding. First, GenAI-related capabilities broaden the informational basis of management accounting by making operational, service, quality, and ecosystem data more usable in planning and control. Second, they enhance analysis by translating complex data into more interpretable, scenario-sensitive, and action-oriented outputs. Third, these benefits are likely to materialize only when AI capabilities are embedded in standardized routines, integrated data infrastructures, and cross-functional governance arrangements. At the same time, the documentary evidence also indicates important boundary conditions, including data maturity, process integration, governance discipline, and the degree of functional trust between finance and operating units. The paper contributes by sharpening the concept of management accounting decision quality, distinguishing GenAI from broader digital transformation, and offering a cautious process model grounded in documentary case evidence from leading Chinese manufacturers. Because the evidence is drawn primarily from external disclosures rather than direct internal observation, the claims should be read as interpretive analytical inferences rather than as definitive causal proof.