Awareness of the Impact of IT/AI on Energy Consumption in Enterprises: A Machine Learning-Based Modelling Towards a Sustainable Digital Transformation

Jolanta Słoniec, Monika Kulisz (), Marta Małecka-Dobrogowska, Zhadyra Konurbayeva () and Łukasz Sobaszek
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Jolanta Słoniec: Department of Organisation of Enterprise, Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
Monika Kulisz: Department of Organisation of Enterprise, Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
Marta Małecka-Dobrogowska: Department of Management, Economy and Finance, Faculty of Engineering Management, Bialystok University of Technology, Wiejska 45A, 15-351 Bialystok, Poland
Zhadyra Konurbayeva: Business of School, D. Serikbayev East Kazakhstan Technical University, Ust-Kamegorsk 070004, Kazakhstan
Łukasz Sobaszek: Department of Information Technology, Faculty of Mathematics and Information Technology, Lublin University of Technology, 20-618 Lublin, Poland

Energies, 2025, vol. 18, issue 21, 1-24

Abstract: The integration of artificial intelligence (AI) and information technology (IT) is transforming business operations while increasing energy demand. A scalable and nonintrusive method for assessing the adoption of energy-conscious IT governance without direct measurements of energy use is lacking. To address this gap, a machine learning framework is developed and validated that infers the presence of energy-conscious IT governance from five indicators of digital maturity and AI adoption. Enterprise survey data were used to train five classification algorithms—support vector machine, logistic regression, decision tree, neural network, and k-nearest neighbors—to identify organizations implementing energy-efficient IT/AI management. All models achieved strong predictive performance, with SVM achieving 90% test accuracy and an F1 score of 89.8%. The findings demonstrate that an enterprise’s technological profile can serve as a reliable proxy for assessing sustainable IT/AI practices, enabling rapid assessment, benchmarking, and targeted support for green digital transformation. This approach offers significant implications for policy design, ESG reporting, and managerial decision-making in energy-conscious governance, supporting the alignment of digital innovation with environmental objectives.

Keywords: AI technologies; energy consumption; machine learning; digital transformation; enterprises; sustainability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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