Strategic Integration of Artificial Intelligence in Manufacturing Processes: Enhancing Sustainability and Efficiency

Andi Gofur; Eko  Satrio; Fajar Adji  Wicaksono

doi:10.51903/e5v7qt70

Authors

Andi Gofur Universitas Wijaya Kusuma, Banyumas, Jawa Tengah, Indonesia
Eko Satrio Universitas Wijaya Kusuma, Banyumas, Jawa Tengah, Indonesia
Fajar Adji Wicaksono Universitas Wijaya Kusuma, Banyumas, Jawa Tengah, Indonesia

DOI:

https://doi.org/10.51903/e5v7qt70

Keywords:

Artificial Intelligence, Manufacturing, Strategic Integration, Sustainability, Operational Efficiency

Abstract

Digital transformation drives manufacturing companies to adopt artificial intelligence (AI) as a strategy to improve operational efficiency and competitiveness. However, the effectiveness of AI integration is influenced not only by technological readiness but also by internal organizational capabilities and external environmental pressures. This study aims to analyze how technological, organizational, and environmental factors contribute to the successful implementation of AI in the manufacturing sector, emphasizing the unique dynamics in developing countries such as Indonesia. This study uses a mixed methods approach with a sequential explanatory design. Quantitative data were collected through surveys of 85 respondents from various manufacturing companies in Indonesia, including operational managers, information technology managers, and professionals related to AI implementation. Analysis was conducted using PLS-SEM to test the relationships between variables. Subsequently, qualitative data were obtained through in-depth interviews with 10 key informants who have direct experience in AI planning and integration in their companies. The results show that organizational factors, especially managerial support and human resource competence, play the most significant role in driving the success of AI integration. Aspects of transformational leadership and work culture readiness also emerge as determining factors for the sustainability of adoption. This study expands the application of TOE and RBV theories in the context of developing countries and provides practical implications for the industry in formulating more adaptive and contextual technology adoption strategies.

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