Managing digital investments of enterprises using artificial intelligence
DOI:
https://doi.org/10.26906/EiR.2026.1%20(100).4395Keywords:
digital investments, artificial intelligence, investment management, digital transformation, machine learning, financial forecastingAbstract
The article explores the theoretical and methodological principles of managing digital investments of enterprises using artificial intelligence technologies in the context of the digital transformation of the economy. The relevance of the study is due to the growing role of digital technologies in the development of enterprises, the increasing complexity of investment decisions and the need to use intelligent tools to analyze large data sets and predict the economic results of investment activities. In the context of rapid changes in the market environment, traditional approaches to investment management often turn out to be insufficiently effective, which necessitates the introduction of modern digital technologies and artificial intelligence algorithms. The paper reveals the essence of digital investments of enterprises as investments of financial resources in the development of digital technologies, information systems, innovative platforms, artificial intelligence systems, cloud computing technologies and big data analysis systems. The main objects of digital investments are identified, and the key functions of digital investment management are substantiated, which include strategic planning of investment activities, assessment of economic efficiency of investment projects, investment risk management and monitoring of the implementation of investment programs. Particular attention is paid to the analysis of the possibilities of using artificial intelligence in the investment management of enterprises. It is shown that the use of machine learning algorithms and neural networks allows to increase the accuracy of financial forecasting, ensure a more effective allocation of investment resources and reduce the level of uncertainty in the process of making managerial decisions. The article proposes a conceptual model of digital investment management of an enterprise based on artificial intelligence, which includes a data collection module, an analytical information processing module, an artificial intelligence module, a decision support system and a module for monitoring and controlling the results of investment activities. An economic and mathematical model of digital investment management has been developed, which allows formalizing the process of assessing the effectiveness of investment projects and optimizing the structure of the enterprise's investment portfolio, taking into account the forecasted profitability and risk level.
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Black, S., Samson, D., & Ellis, A. (2024), “Moving beyond ‘proof points’: Factors underpinning AI-enabled business model transformation”, International Journal of Information Management, Vol. 77, Article 102796, DOI: https://doi.org/10.1016/j.ijinfomgt.2024.102796
Zhang, Z., Kang, Y., Lu, Y., & Li, P. (2025), “The role of artificial intelligence in business model innovation of digital platform enterprises”, Systems, Vol. 13, No. 7, Article 507, DOI: https://doi.org/10.3390/systems13070507
Mertzanis, C. (2025), “Artificial intelligence and investment management: Structure, strategy, and governance”, International Review of Financial Analysis, Vol. 107, Article 104599, DOI: https://doi.org/10.1016/j.irfa.2025.104599
Jang, J., & Seong, N.-Y. (2023), “Deep reinforcement learning for stock portfolio optimization by connecting with modern portfolio theory”, Expert Systems with Applications, Vol. 218, Article 119556, DOI: https://doi.org/10.1016/j.eswa.2023.119556
Jiang, Y., Olmo, J., & Atwi, M. (2024), “Deep reinforcement learning for portfolio selection”, Global Finance Journal, Vol. 62, Article 101016, DOI: https://doi.org/10.1016/j.gfj.2024.101016
Jeribi, F., Martin, R. J., Mittal, R., Jari, H., Alhazmi, A. H., Malik, V., et al. (2024), “A deep learning based expert framework for portfolio prediction and forecasting”, IEEE Access, Vol. 12, pp. 103810–103829, DOI: https://doi.org/10.1109/ACCESS.2024.3434528
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World Economic Forum, & Accenture (2025), Artificial Intelligence in Financial Services, World Economic Forum, Geneva, available at: https://reports.weforum.org/docs/WEF_Artificial_Intelligence_in_Financial_Services_2025.pdf
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