Research on ROI Model of Empowering Enterprise Digital Transformation with Intelligent Computing Center ——Cost Benefit Analysis of AI Projects throughout Their Lifecycle in the Energy Industry
DOI: 10.23977/infse.2025.060205 | Downloads: 5 | Views: 232
Author(s)
Ma Yue 1
Affiliation(s)
1 SPIC Digital Technology Co., Ltd., Beijing, 102209, China
Corresponding Author
Ma YueABSTRACT
At present, the energy industry is in a critical stage of transitioning from traditional power generation to new energy generation, and the entire chain of power generation, transmission, and other businesses urgently need to achieve intelligent upgrades through digital technology. Energy companies represented by a power generation central enterprise face practical issues such as construction mode selection and cost-benefit evaluation when exploring the application of intelligent computing centers. Although existing research has explored the digital transformation of intelligent computing centers and enterprises, there is still a gap in systematic research on constructing ROI models based on the full lifecycle perspective of the energy industry characteristics. This article takes AI projects in the energy industry as the research object, analyzes the application scenarios of intelligent computing centers in energy enterprises, and constructs a cost-benefit analysis framework covering the entire process from planning, construction, operation to retirement. The aim is to quantify the value contribution of intelligent computing centers to the digital transformation of enterprises, and provide theoretical basis and practical reference for energy enterprise investment decision-making and industry policy formulation.
KEYWORDS
Intelligent Computing Center; Energy Industry; Digital Transformation; Cost EffectivenessCITE THIS PAPER
Ma Yue, Research on ROI Model of Empowering Enterprise Digital Transformation with Intelligent Computing Center——Cost Benefit Analysis of AI Projects throughout Their Lifecycle in the Energy Industry. Information Systems and Economics (2025) Vol. 6: 42-48. DOI: http://dx.doi.org/10.23977/infse.2025.060205.
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