Dual effects of digital agents: a three-tier metric system for assessing the impact on company competitiveness

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Abstract: Problem. The proliferation of digital agents in business processes of companies produces multidirectional effects on their competitiveness, ranging from productivity growth to the loss of employee skills. A measurement apparatus that covers both aspects simultaneously and explains their origins does not yet exist, and this gap becomes critical as implementations scale up. Aim. To develop a categorical framework and a three-tier metric system that allows measuring the dual effects of digital agents, based on the causal mechanism of their generation and on the distinction of who makes the decision – a human or a digital agent. Methods. The work is carried out in a theoretical-methodological genre; its foundation consists of the four-step transfer of established frameworks in the economics of innovation from humans to digital agents while preserving the core of each original approach. The conceptual basis is formed by agency theory and the triad of artificial intelligence properties (autonomy, learnability, and opacity), with the unifying distinction being who makes decisions in the business process (a human or a digital agent). Results. A causal scheme is constructed in which the positive and negative sides of each effect arise within the same link of the mechanism. Eight channels of influence of digital agents on company competitiveness are identified, ranging from productivity to the composition of innovation authors. Three tiers of metrics are proposed, which differ in the degree to which the outcome depends on the decision-making subject. Each channel is assigned a primary measurement tier, and when an agent transitions to independent action, some channels move to higher tiers. Conclusions. The duality of the effects of digital agents is demonstrated as a structural property of a single mechanism, rather than a balance of independent factors. A three-tier classification of metrics is proposed based on sensitivity to who makes the decision – a human or a digital agent – and eight channels of influence are linked to these tiers. The resulting framework creates a foundation for the quantitative assessment of the impact of digital agents on company competitiveness, taking into account the observed heterogeneity of their implementation.

About the authors

Anton V. Chebotarev

Patrice Lumumba Peoples’ Friendship University of Russia (RUDN University), Moscow Exchange PJSC

Author for correspondence.
Email: 1042250481@rudn.ru
ORCID iD: 0009-0004-5714-4192

postgraduate student of Chair of Mathematical Modeling and Information Technologies, Junior Product Manager of Artificial Intelligence Platform Group

Russian Federation, 117198, Russia, Moscow, Miklukho-Maklay Street, 6.; 125009, Russia, Moscow, Bolshoy Kislovsky Lane, 13.

References

  1. Cui K.Z., Demirer M., Jaffe S., Musolff L., Peng S., Salz T. The effects of generative AI on high-skilled work: evidence from three field experiments with software developers. Management Science, 2026, article number mnsc.2025.00535. doi: 10.1287/mnsc.2025.00535.
  2. Brynjolfsson E., Danielle Li, Raymond L.R. Generative AI at work. Quarterly Journal of Economics, 2025, vol. 140, no. 2, pp. 889–942. doi: 10.1093/qje/qjae044.
  3. Czarnitzki D., Fernández G.P., Rammer C. Artificial intelligence and firm-level productivity. Journal of Economic Behavior & Organization, 2023, vol. 211, pp. 188–205. doi: 10.1016/j.jebo.2023.05.008.
  4. Acemoglu D., Restrepo P. Automation and new tasks: how technology displaces and reinstates labor. Journal of Economic Perspectives, 2019, vol. 33, no. 2, pp. 3–30. doi: 10.1257/jep.33.2.3.
  5. Acemoglu D., Restrepo P. The wrong kind of AI? Artificial intelligence and the future of labour demand. Cambridge Journal of Regions, Economy and Society, 2020, vol. 13, no. 1, pp. 25–35. doi: 10.1093/cjres/rsz022.
  6. Babina T., Fedyk A., Alex He, Hodson J. Artificial intelligence, firm growth, and product innovation. Journal of Financial Economics, 2024, vol. 151, article number 103745. doi: 10.1016/j.jfineco.2023.103745.
  7. Mancuso I., Petruzzelli A.M., Panniello U., Vaia G. The bright and dark sides of AI innovation for sustainable development: understanding the paradoxical tension between value creation and value destruction. Technovation, 2025, vol. 143, article number 103232. doi: 10.1016/j.technovation.2025.103232.
  8. Enholm I.M., Papagiannidis E., Mikalef P., Krogstie J. Artificial intelligence and business value: a literature review. Information Systems Frontiers, 2022, vol. 24, no. 5, pp. 1709–1734. doi: 10.1007/s10796-021-10186-w.
  9. Rammer C., Fernández G.P., Czarnitzki D. Artificial intelligence and industrial innovation: evidence from German firm-level data. Research Policy, 2022, vol. 51, no. 7, article number 104555. doi: 10.1016/j.respol.2022.104555.
  10. Venturini F. Intelligent technologies and productivity spillovers: evidence from the Fourth Industrial Revolution. Journal of Economic Behavior & Organization, 2022, vol. 194, pp. 220–243. doi: 10.1016/j.jebo.2021.12.018.
  11. Berente N., Gu B., Recker J., Santhanam R. Managing artificial intelligence. MIS Quarterly, 2021, vol. 45, no. 3, pp. 1433–1450. doi: 10.25300/MISQ/2021/16274.
  12. Tsenzharik M.K., Krylova Yu.V., Steshenko V.I. Digital transformation in companies: strategic analysis, drivers and models. St Petersburg University Journal of Economic Studies, 2020, vol. 36, no. 3, pp. 390–420. doi: 10.21638/spbu05.2020.303.
  13. Raisch S., Krakowski S. Artificial intelligence and management: the automation–augmentation paradox. Academy of Management Review, 2021, vol. 46, no. 1, pp. 192–210. doi: 10.5465/amr.2018.0072.
  14. Anthony C., Bechky B.A., Fayard A.-L. "Collaborating" with AI: taking a system view to explore the future of work. Organization Science, 2023, vol. 34, no. 5, pp. 1672–1694. doi: 10.1287/orsc.2022.1651.
  15. Gavrilova T.A. “Stochastic parrot” as the servant of business: achievements and challenges of generative artificial intelligence. Russian management Journal, 2024, vol. 22, no. 3, pp. 461–482. doi: 10.21638/spbu18.2024.305.
  16. Smirnykh L.I. Artificial intelligence in Russian enterprises: what are the effects on employment? Voprosy Ekonomiki, 2025, no. 9, pp. 88–102. doi: 10.32609/0042-8736-2025-9-88-102.
  17. Skorobogatov A.S., Sviridov O.I. The artificial intelligence impact on Russian labor market. Voprosy Ekonomiki, 2025, no. 1, pp. 71–91. doi: 10.32609/0042-8736-2025-1-71-91.
  18. Ternikov A.A. Artificial intelligence and the demand for skills in Russia. Voprosy Ekonomiki, 2023, no. 11, pp. 65–80. doi: 10.32609/0042-8736-2023-11-65-80.
  19. Wooldridge M. An Introduction to MultiAgent Systems. 2nd ed. Chichester, John Wiley & Sons Publ., 2009. 484 p.
  20. Jensen M.C., Meckling W.H. Theory of the firm: managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 1976, vol. 3, no. 4, pp. 305–360. doi: 10.1016/0304-405X(76)90026-X.
  21. Teece D.J., Pisano G., Shuen A. Dynamic capabilities and strategic management. Strategic Management Journal, 1997, vol. 18, no. 7, pp. 509–533.
  22. Kleyner G.B. Systems paradigm and theory of the enterprise. Voprosy ekonomiki, 2002, no. 10, pp. 47–69. EDN: RSNTON.

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