Data · dataset · 2026
AI-Driven Operational Excellence as a Catalyst for Sustainable Development in the Digital Economy
Listed in Teesside University Research Data Repository
Artificial intelligence (AI) has transformed the way businesses operate and formulate strategies across various sectors.
Description
AI is no longer merely a tool for simplifying processes, but has become a major driving force for continuous digital sustainability and development. This study aims to examine the role of AI-driven operational excellence in achieving sustainable digital transformation through a systematic literature review methodology.
The reviewed studies encompass literature on artificial intelligence, operational excellence, sustainability, Industry 4.0, Industry 5.0, dynamic capabilities, and socio-technical systems. The analysis reveals that the implementation of AI can enhance operational efficiency, supply chain resilience, organizational adaptability, and innovation capability through intelligent automation, predictive analytics, and real-time operational monitoring.
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AI also contributes significantly to sustainability performance by optimizing energy consumption, reducing waste, improving resource efficiency, and supporting the circular economy. This study concludes that the successful implementation of AI depends on technological readiness, organizational culture, human resource competencies, and organizational agility in adapting to digital transformation. The findings emphasize that the integration of AI and operational excellence is essential for the sustainability and competitiveness of modern organizations.\
Links
Where it is published
- DOI doi.org/10.17632/h3nhxtrjjh.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Artificial intelligence · Computer Science & AI · Earth & Environmental Science
Related
Provenance · 1 source records, 8 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/h3nhxtrjjh.1 | 17 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:group:4602 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Artificial Intelligence'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |