Nina A. SERKINAKazan State Power Engineering University (KSPEU), Kazan, Republic of Tatarstan, Russian Federation ninaserkina@mail.ru ORCID id: not available
Subject. Implementation of generative artificial intelligence in production and design processes. Objectives. Assessment of the investment potential of generative artificial intelligence based on the analysis of stock market reaction and identification of institutional factors modifying the market assessment of digital initiatives. Methods. The study uses event analysis tools, correlation and regression analysis, and content analysis of regulatory and expert materials. The empirical base is based on data on corporate announcements of the introduction of generative artificial intelligence by Siemens AG, General Electric and Boeing in 2022–2025. Results. It has been established that there is a short-term positive market reaction to digital initiatives with subsequent correction in the absence of operational monetization. It is proved that the market valuation is determined not by the scale of digital investments as such, but by the degree of their strategic integration and institutional coherence. The author's classification of barriers to the introduction of generative artificial intelligence is proposed, taking into account their ESG sensitivity. Conclusions. The research results can be used in the development of investment strategies and the formation of corporate digital transformation programs. In particular, the identified key technological drivers, such as the industrial Internet of Things, cloud platforms, and predictive analytics, allow investors to differentiate companies not just by sector, but by the depth of integration of these solutions into core business processes. This forms the basis for targeted investment decisions.
Brynjolfsson E., Makafee A. Mashina, platforma, tolpa: kak obuzdat' nashe tsifrovoe budushchee [Machine, platform, crowd: harnessing our digital future]. Moscow, Mann, Ivanov i Ferber Publ., 2022, 304 p.
Perez C. Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages. Cheltenham, Edward Elgar Publishing, 2002, 224 p.
Teece D.J. Profiting from innovation in the digital economy: Enabling technologies, standards, and licensing models in the wireless world. Research Policy, 2018, vol. 47, iss. 8, pp. 1367–1387. DOI: 10.1016/j.respol.2017.01.015
Agrawal A., Gans J., Goldfarb A. Prediction, judgment and complexity: a theory of decision making and artificial intelligence. NBER Working Paper No. 24243, 2018, 26 p. URL: Link
Brynjolfsson E., Jin W., McElheran K. The power of prediction: predictive analytics, workplace complements, and business performance. Business Economics, 2021, vol. 56, pp. 217–239. DOI: 10.1057/s11369-021-00224-5 EDN: VWDYKA
Raj M., Seamans R. Artificial intelligence, labor, productivity, and the need for firm-level data. In: The economics of artificial intelligence: An agenda. University of Chicago Press, 2018, pp. 553–565.
Acemoglu D., Restrepo P. Automation and New Tasks: How Technology Displaces and Reinstates Labor. Journal of Economic Perspectives, 2019, vol. 33, iss. 2, pp. 3–30. DOI: 10.1257/jep.33.2.3
Aganbegyan A.G. [On the applicability of china’s experience to helping russia in the transition to socio-economic growth based on the development of technological and intellectual potential]. Ekonomicheskaya politika, 2024, no. 2, pp. 5–25. (In Russ.) DOI: 10.31857/S0207367624020017 EDN: XZTUNY
Myers S.C. Determinants of Corporate Borrowing. Journal of Financial Economics, 1977, vol. 5, no. 2, pp. 147–175. DOI: 10.1016/0304-405X(77)90015-0
Varian H. Artificial intelligence, economics, and industrial organization. NBER Working Paper No. 24839, 2018, 24 p. RL: Link
Davenport T.H., Ronanki R. Artificial Intelligence for the Real World. Harvard Business Review, 2016, vol. 96, iss. 1. pp. 108–116. URL: Link
Glaz'ev S.Yu. [Global transformations from the perspective of technological and economic world order change]. AlterEconomics, 2022, vol. 19, no. 1, pp. 93–115. (In Russ.) DOI: 10.31063/AlterEconomics/2022.19-1.6 EDN: MULEYG
Schumpeter J.A. Teoriya ekonomicheskogo razvitiya. Kapitalizm, sotsializm i demokratiya [Theorie der wirtschaftlichen Entwicklung]. Moscow, Eksmo Publ., 2007, 862 p.
Polterovich V.M. [Institutional traps and ways out]. Obshchestvennye nauki i sovremennost', 2004, no. 3, pp. 5–18. (In Russ.) EDN: OWOHPR
Kleiner G.B. [System economics and system-oriented modeling]. Ekonomika i matematicheskie metody, 2013, vol. 49, no. 4, pp. 71–93. (In Russ.) EDN: RIOKKF
Korinek A., Stiglitz J.E. Artificial Intelligence and Its Implications for Income Distribution and Unemployment. NBER Working Paper No. 24174, 2017, 45 p. DOI: 10.3386/w24174
Huang M.H., Rust R.T. A Strategic Framework for Artificial Intelligence in Marketing. Journal of the Academy of Marketing Science, 2021, vol. 49, pp. 30–50. DOI: 10.1007/s11747-020-00749-9 EDN: OSRCFY
