REVIEW article
Front. Artif. Intell.
Sec. AI in Business
Volume 8 - 2025 | doi: 10.3389/frai.2025.1675231
This article is part of the Research TopicSmart Forecasting: Deep Learning and Explainable AI for Real-World Time Series PredictionView all articles
How Generative AI Empowers Production Management and Sustainable Performance in Manufacturing Enterprises in the Era of Cognitive Automation
Provisionally accepted
Mintian He*
Shuili Yang- School of Economics and Management, Xi'an University of Technology, Xi'an, China
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In the context of accelerating cognitive automation, Generative Artificial Intelligence (Generative AI) is emerging as a transformative cognitive technology reshaping production management practices in manufacturing enterprises. Drawing on a panel dataset of 617 publicly listed manufacturing firms in China from 2020 to 2024, this study develops a theoretical framework and empirically investigates how the adoption intensity of Generative AI influences firms' operational capability and sustainable performance. The results reveal that: (1) Generative AI significantly enhances operational capabilities, particularly in resource allocation efficiency, process responsiveness, quality control, and energy management; (2) Operational capability partially mediates the effect of Generative AI on sustainable performance, covering economic, environmental, and social dimensions; and (3) The enabling effect of Generative AI is more pronounced among firms with higher automation maturity and those located in digitally developed eastern regions. This research extends the application of cognitive automation theory and the resource-based view (RBV) by unveiling the AI–capability– performance transmission mechanism. It also offers practical implications for advancing smart manufacturing and sustainability transformation in industrial contexts.
Keywords: Generative AI1, cognitive automation2, operational capability3, sustainable performance4, manufacturing industry5, Mediation analysis6
Received: 30 Jul 2025; Accepted: 08 Oct 2025.
Copyright: © 2025 He and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mintian He, 3326017237@qq.com
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