INTELLIGENT TECHNOLOGIES IN BUSINESS: THE IMPACT OF ARTIFICIAL INTELLIGENCE ON SUSTAINABLE DEVELOPMENT
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
The purpose of the paper is to evaluate the impact of artificial intelligence (AI) adoption on enterprise-level sustainability in the context of digital transformation. The study aims to determine how different levels of AI implementation and business process automation influence key sustainability indicators – namely, energy efficiency, CO₂ reduction, and cost savings. Metodology. The research is based on a synthetic dataset generated through a simulation approach using probabilistic distributions and literature-based assumptions. The dataset includes 250 observations and four independent variables: level of AI adoption, investment in AI, automation level, and degree of policy support. The dependent variable is an integrated sustainability index composed of three sub-indicators. Regression modeling was conducted using the Random Forest algorithm to detect both linear and nonlinear relationships, identify key drivers, and ensure model robustness. Model accuracy was evaluated through R², RMSE, MAE, and MAPE metrics. Results. The model demonstrated a high level of predictive performance (R² = 0.78), confirming its validity. The most influential factors were the actual use of AI technologies and business process automation. Investment in AI without concrete implementation and state policy support had a lower impact on sustainability outcomes. The feature importance analysis confirmed that energy efficiency, cost savings, and CO₂ reduction are directly correlated with digital implementation rather than with formal spending or subsidies. Practical implications. The findings can support enterprise-level strategic planning by highlighting the need for actionable AI integration instead of declarative investment. For policymakers, the study indicates that future support mechanisms should focus on incentivizing outcomes rather than inputs. The proposed model may also serve as a tool for evaluating the effectiveness of national digital and environmental policies. Value / Originality. The study provides a novel combination of simulation-based data generation and ensemble modeling to explore the relationship between AI and sustainable development. It offers a transferable methodology for countries with limited access to real enterprise data and contributes to a deeper understanding of digital sustainability transitions in emerging economies.
How to Cite
##plugins.themes.bootstrap3.article.details##
digital transformation of business, artificial intelligence, sustainable development of enterprises, circular business models, regression modeling
Ahmed, M. S., Kareem, S. A., et al. (2025, February). Exploring innovations in AI shaping transformative sustainable development goals: An overview of challenges and opportunities. ResearchGate. Available at: https://www.researchgate.net/publication/388861779
Chauhan, C., Parida, V., & Dhir, A. (2022). Linking circular economy and digitalisation technologies: A systematic literature review of past achievements and future promises. Technological Forecasting and Social Change, 177, 121508. DOI: https://doi.org/10.1016/j.techfore.2022.121508
Ellen MacArthur Foundation. (2019). Artificial intelligence and the circular economy – AI as a tool to accelerate the transition. Available at: https://www.ellenmacarthurfoundation.org/publications
González-Sánchez, M., Garcia-Muiña, F. E., Volpi, F., Ferrari, A. M., & Settembre-Blundo, D. (2019). Identifying the equilibrium point between sustainability goals and circular economy practices in an Industry 4.0 manufacturing context using eco-design. Social Sciences, 8(8), 241. DOI: https://doi.org/10.3390/socsci8080241
Holzinger, A., Kieseburg, P., Tjoa, A. M., et al. (2021). Digital transformation for sustainable development goals (SDGs): A security, safety and privacy perspective on AI. Lecture Notes in Computer Science. DOI: https://doi.org/10.1007/978-3-030-84060-0_1
Lu, S., & Serafeim, G. (2023, June 12). How AI will accelerate the circular economy. Harvard Business Review. Available at: https://hbr.org/2023/06/how-ai-will-accelerate-the-circular-economy
Madanaguli, A., Sjödin, D., Parida, V., & Mikalef, P. (2024). Artificial intelligence capabilities for circular business models: Research synthesis and future agenda. Technological Forecasting and Social Change, 200, 123189. DOI: https://doi.org/10.1016/j.techfore.2023.123189
Matouskova, D. (2022). Digitalization and its impact on business. Review of Business & Management, 18(2), 51–67. DOI: https://doi.org/10.18096/TMP.2022.02.03
Mboli, J. S., Thakker, D. K., & Mishra, J. L. (2020). An Internet of Things-enabled decision support system for circular economy business model. Software: Practice and Experience. DOI: https://doi.org/10.1002/spe.2825
Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Journal of Innovation & Knowledge, 6(4), 217–233. DOI: https://doi.org/10.1016/j.im.2021.103434
OECD (2024). Enhancing resilience by boosting digital business transformation in Ukraine. OECD Publishing. Available at: https://www.oecd.org/content/dam/oecd/uk/publications/reports/2024/05/enhancing-resilience-by-boosting-digital-business-transformation-in-ukraine_c2e06e50/5d9e86a7-uk.pdf
Parida, V., Sjödin, D., & Reim, W. (2019). Reviewing literature on digitalization, business model innovation, and sustainable industry: Past achievements and future promises. Sustainability, 11(2), 391. DOI: https://doi.org/10.3390/su11020391
Schott, M. (2019, April 25). Random forest algorithm for machine learning. Capital One Tech. Available at: https://medium.com/capital-one/random-forest-algorithm-for-machine-learning
Sjödin, D., Parida, V., & Kohtamäki, M. (2023). Artificial intelligence enabling circular business model innovation in digital servitization: Conceptualizing dynamic capabilities, AI capacities, business models and effects. Technological Forecasting and Social Change, 197, 122903. DOI: https://doi.org/10.1016/j.techfore.2022.122903
Varnalii, Z., Kulyk, P., Nikytenko, D., Cheberyako, O., & Hurochkina, V. (2023). Circular economy implementation as a strengthening factor in the economic security of Ukraine during the post-war period. IOP Conference Series: Earth and Environmental Science, 1126, Article 012006. DOI: https://doi.org/10.1088/1755-1315/1126/1/012006
Zomchak, L. (2023, December). Quantitative methods for studying processes in the circular economy: Empirical analysis at the regional level of Ukraine. Zenodo. DOI: https://doi.org/10.5281/zenodo.14796651
This work is licensed under a Creative Commons Attribution 4.0 International License.