Sep 2026· International Research Journal on Advanced Engineering and Management (IRJAEM)
Abstract
Purpose: The global shift towards clean energy necessitates robust business strategies to address challenges related to intermittent supply, operational inefficiencies, and evolving market conditions. This study fills a gap in strategic management literature by exploring the role of Artificial Intelligence (AI) and Digital Transformation (DT) in enhancing sustainable business performance within renewable energy enterprises. Design/Methodology/Approach: Utilizing the Resource-Based View (RBV) and Dynamic Capabilities Theory, this study conducts a qualitative conceptual synthesis and systematic analysis of recent literature (2018–2026) to formulate an integrated strategic management framework. Findings: The study presents the AI-Driven Digital Transformation for Sustainable Energy (ADT-SE) framework. This model demonstrates that the alignment of technological drivers—such as predictive analytics, IoT, smart grids, and block chain—with core organizational capabilities significantly enhances financial performance, operational efficiency, and environmental stewardship. Practical Implications: The study offers renewable energy executives and policy planners a practical roadmap for capital allocation, digital risk management, and the development of cross-functional capabilities. Originality/Value: This research connects technical AI applications with corporate sustainability strategies within the Commerce and Management domain.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
Jinhe Bi, Yifan Wang, Danqi Yan et al.· arXiv.org· 73 citations· ⚡4
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.
Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al.· Advances in Neural Informati...· 59 citations· ⚡8
An empirical study on the current state of practice in artificial intelligence ethics is conducted by means of a multiple case study of five case companies, which indicates a gap between research and practice in the area.
Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al.· arXiv.org· 56 citations· ⚡6