Abstract
This study investigates the transformative implications of Generative Artificial General Intelligence (GAI) for managerial accounting, highlighting its technical, institutional, ethical, and cognitive dimensions. While narrow AI has already enhanced accounting functions through the automation of repetitive tasks and improved accuracy, GAI introduces autonomous reasoning, continuous learning, and adaptability to dynamic environments. The paper demonstrates how GAI integration can reshape processes such as costing, budgeting, and performance evaluation, turning managerial accounting into a strategic real-time decision-support mechanism. At the same time, challenges of transparency, accountability, and data protection are addressed, as GAI systems often function as “black boxes,” raising concerns over trust, regulatory compliance, and ethical alignment. The study contributes by: (a) proposing a theoretical framework for understanding GAI in managerial accounting, (b) identifying new professional roles and required skillsets for accountants within Industry 4.0 environments, and (c) analyzing the ethical and regulatory challenges emerging from the adoption of autonomous algorithmic systems. Furthermore, the paper identifies important research gaps, including the need for empirical evidence on GAI’s practical adoption, the study of human–machine collaboration, and the exploration of education and training for future accountants in AI and big data competencies. Overall, the findings underscore that GAI is not merely a technological innovation but a paradigm shift that will shape the future of managerial accounting. By combining technological advancement, strategic adaptation, and ethical alignment, GAI emerges as a transformative force, redefining decision-making, organizational control, and professional competencies in accounting.
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Atsalakis, G. S., Atsalaki, I., Lemonakis, C., & Zopounidis, C. (2026). Generative Artificial Intelligence and Managerial Accounting. Information Systems Frontiers. https://doi.org/10.1007/s10796-026-10763-x
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