AI-Driven Operational Excellence as a Catalyst for Sustainable Development in the Digital Economy
Description
Artificial Intelligence (AI) has transformed the way organizations conduct operations and develop business strategies across various sectors. The use of AI is no longer limited to automating work processes but has also become a crucial part of supporting digital transformation and sustainable development. This study aims to examine the role of AI-based operational excellence in supporting digital sustainability transformation through a Systematic Literature Review (SLR) method. The literature includes various studies on artificial intelligence, operational excellence, extinction, Industry 4.0, Industry 5.0, dynamic capabilities, and socio-technical systems. The study results show that the application of AI can improve operational efficiency, strengthen supply chain resilience, enhance organizational adaptability, and encourage innovation through intelligent automation, predictive analytics, and real-time operational monitoring. Furthermore, the use of AI also supports improved disruption performance through more efficient energy use, waste reduction, resource optimization, and the implementation of a circular economy. This study shows that the success of AI implementation is influenced by technological readiness, organizational culture, human resource competency, and the organization's ability to adapt to digital change. The research findings state that the integration of AI with operational excellence is a critical factor in realizing sustainable digital transformation while strengthening organizational competitiveness.
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Steps to reproduce
This dataset was developed through a Systematic Literature Review (SLR) to investigate the role of AI-based operational excellence in enabling sustainable digital transformation. The review followed the PRISMA framework, encompassing identification, screening, eligibility assessment, and final study selection. Relevant peer-reviewed articles published between 2018 and 2026 were retrieved from Scopus, ScienceDirect, Emerald Insight, Google Scholar, and IEEE Xplore using predefined search keywords. The selected studies were systematically analyzed through thematic synthesis to identify key themes, research trends, and theoretical perspectives. This dataset contains the complete SLR dataset, PRISMA flow, selection criteria, and extracted bibliographic and analytical information that supports the findings and conclusions presented in the associated research.
Institutions
- Binus UniversityJakarta, Jakarta