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 organizations conduct operations and develop business strategies across various sectors.
Description
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.
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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.
Links
Where it is published
- DOI doi.org/10.17632/v44hrjw4tn.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/v44hrjw4tn.1 | 19 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 |