Received:
2026-03-18 | Accepted:
2026-07-06 | Published:
2026-09-30
Title
AI-driven corporate organizational restructuring in the European Union
Abstract
This study examines whether the growing adoption of artificial intelligence (AI) technologies is associated with measurable organizational restructuring processes within enterprises across the European Union (EU). While existing research mainly focuses on the determinants and performance effects of AI adoption, the organizational consequences of AI integration remain insufficiently explored. To address this gap, the study develops a capability-based conceptual framework linking internal enterprise-level capabilities, AI adoption mechanisms, and organizational restructuring outcomes. The empirical analysis is based on comparative Eurostat datasets covering EU countries and includes indicators related to digital capabilities, organizational innovation, AI adoption, and middle-management employment structures. Methodologically, the study applies a two-stage quantitative approach combining K-means cluster analysis and linear regression analysis. The results reveal substantial heterogeneity among EU countries in terms of digital integration, organizational innovation, and AI adoption intensity. The findings further suggest that AI-driven organizational restructuring does not manifest itself through simple managerial displacement. Instead, enterprise analytical capabilities – particularly the diffusion of Business Intelligence (BI) software – are positively associated with changes in middle-management structures in more digitally advanced organizational environments. The study contributes to the literature by shifting attention from AI adoption itself toward its organizational implications and by providing an indirect quantitative assessment of AI-driven corporate organizational restructuring within the EU.
Keywords
digital and innovation capabilities of enterprises, artificial intelligence (AI) adoption, corporate organizational restructuring, cluster analysis, regression analysis, European Union
JEL classifications
O33
, M15
, O31
, J24
, L23
URI
http://jssidoi.org/jesi/article/6672
DOI
Pages
83-102
Funding
The article has been developed and funded within the Daugavpils University’s (Latvia) research project No 14-95/2026/17 “AI-Based Enterprise Transformation and Its Impact on Managerial Decision Quality”.
This is an open access issue and all published articles are licensed under a
Creative Commons Attribution 4.0 International License
References
Arroyabe, M. F., Arranz, C. F. A., Fernández de Arroyabe, I., & Fernández de Arroyabe, J. C. (2024). Analyzing AI adoption in European SMEs: A study of digital capabilities, innovation, and external environment. Technology in Society, 79, 102733 https://doi.org/10.1016/j.techsoc.2024.102733
Search via ReFindit
Bankins, S., Formosa, P., Griep, Y., & Richards, D. (2022). AI decision making with dignity? Contrasting workers’ justice perceptions of human and AI decision making in a human resource management context. Information Systems Frontiers, 24(3), 857-875 https://doi.org/10.1007/s10796-021-10223-8
Search via ReFindit
Birkstedt, T., Minkkinen, M., Tandon, A., & Mäntymäki, M. (2023). AI governance: Themes, knowledge gaps and future agendas. Internet Research, 33(7), 133-167 https://doi.org/10.1108/INTR-01-2022-0042
Search via ReFindit
Cheng, Y., Liu, X., Chen, J., & Wang, Y. (2025). Pathways analysis of different types platform enterprises’ digital innovation: From the perspective of digital capability and ambidextrous learning. Transformations in Business & Economics, 24(2), 336-354 https://doi.org/10.15388/Tibe.2025.24.2.15
Search via ReFindit
Cors, M. S., & Thiébaut, R. (2025). Artificial intelligence and the impact of the EU AI Act in business organizations. AI Magazine, 46(4), e70039 https://doi.org/10.1002/aaai.70039
Search via ReFindit
Daly, S. J., Wiewiora, A., & Hearn, G. (2025). Shifting attitudes and trust in AI: Influences on organizational AI adoption. Technological Forecasting and Social Change, 215, 124108 https://doi.org/10.1016/j.techfore.2025.124108
Search via ReFindit
Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709-1734 https://doi.org/10.1007/s10796-021-10186-w
Search via ReFindit
European Commission. (2025). The AI Continent Action Plan. Shaping Europe’s Digital Future https://digital-strategy.ec.europa.eu/en/library/ai-continent-action-plan
Search via ReFindit
European Parliament and Council. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng
Search via ReFindit
Eurostat. (2023). Community Innovation Survey 2020 (CIS2020) (inn_cis12). Eurostat https://ec.europa.eu/eurostat/cache/metadata/en/inn_cis12_esms.htm
Search via ReFindit
Eurostat. (2024). Community Innovation Survey 2022 - key indicators. Statistics Explained https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Community_Innovation_Survey_2022_-_key_indicators
Search via ReFindit
Eurostat. (2025). Use of artificial intelligence in enterprises. Statistics Explained https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises
Search via ReFindit
Eurostat. (2026a). Enterprises having ERP software package to share information between different functional areas. Integration of Internal Processes by Size Class of Enterprise (isoc_eb_iip). Eurostat Data Browser https://ec.europa.eu/eurostat/databrowser/view/isoc_eb_iip/default/table?lang=en
Search via ReFindit
Eurostat. (2026b). Enterprises using Business Intelligence software. Integration of Internal Processes by Size Class of Enterprise (isoc_eb_iip). Eurostat Data Browser https://doi.org/10.2908/ISOC_EB_IIP
Search via ReFindit
Eurostat. (2026c). Enterprises using at least one of the AI technologies. Artificial Intelligence by Size Class of Enterprise (isoc_eb_ai). Eurostat Data Browser https://ec.europa.eu/eurostat/databrowser/view/isoc_eb_ai/default/table?lang=en
Search via ReFindit
Eurostat. (2026d). AI-adopting firms using AI for the organisation of business administration processes or management. Artificial Intelligence by Size Class of Enterprise (isoc_eb_ai). Eurostat Data Browser https://ec.europa.eu/eurostat/databrowser/view/isoc_eb_ai/default/table?lang=en
Search via ReFindit
Eurostat. (2026e). Employed persons by detailed occupation (ISCO-08 two digit level) (lfsa_egai2d). Eurostat Data Browser https://ec.europa.eu/eurostat/databrowser/view/lfsa_egai2d/default/table?lang=en
Search via ReFindit
Fan, G. (2025). Influence of AI on business strategies of music culture communication companies. Transformations in Business & Economics, 24(2), 355-376 https://doi.org/10.15388/Tibe.2025.24.2.16
Search via ReFindit
Filippucci, F., Gal, P., Jona-Lasinio, C., Leandro, Á., & Nicoletti, G. (2024). The impact of artificial intelligence on productivity, distribution and growth: Key mechanisms, initial evidence and policy challenges. OECD Artificial Intelligence Papers, No. 15. OECD Publishing https://doi.org/10.1787/8d900037-en
Search via ReFindit
Funding: The article has been developed and funded within the Daugavpils University’s (Latvia) research project No 14-95/2026/17 “AI-Based Enterprise Transformation and Its Impact on Managerial Decision Quality”. /jesi/article/6672
Search via ReFindit
Hansen, H. F., Lillesund, E., Mikalef, P., & Altwaijry, N. (2024). Understanding artificial intelligence diffusion through an AI capability maturity model. Information Systems Frontiers, 26(6), 2147-2163 https://doi.org/10.1007/s10796-024-10528-4
Search via ReFindit
He, X., Li, T., & Wang, L. (2026). How enterprise digital transformation reshapes risk sharing between banks and enterprises: Based on multiperiod DID analysis. Transformations in Business & Economics, 25(1), 383-417 https://doi.org/10.15388/Tibe.2026.25.1.20
Search via ReFindit
International Labour Organization (ILO). (2025). Generative AI and jobs: A refined global index of occupational exposure. ILO Working Paper https://www.ilo.org/publications/generative-ai-and-jobs-refined-global-index-occupational-exposure
Search via ReFindit
International Labour Organization (ILO). (2026). International Standard Classification of Occupations (ISCO). ILOSTAT https://ilostat.ilo.org/methods/concepts-and-definitions/classification-occupation/
Search via ReFindit
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586 https://doi.org/10.1016/j.bushor.2018.03.007
Search via ReFindit
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366-410 https://doi.org/10.5465/annals.2018.0174
Search via ReFindit
Klarna. (2024). Klarna AI assistant handles two-thirds of customer service chats in its first month. Klarna Press Release, 27 February https://www.klarna.com/international/press/klarna-ai-assistant-handles-two-thirds-of-customer-service-chats-in-its-first-month/
Search via ReFindit
Komarova, V., Čižo, E., Kudiņš, J., & Ostrovska, I. (2025). Conceptualizing IT/AI use and labor productivity: A framework and methodology for empirical research in Latvian enterprises. In Proceedings of the XVIII International Scientific and Practical Conference “The Terms of Economic Growth in Countries with a Market Economy”. Pereiaslav: Hryhorii Skovoroda University, pp. 109-111. /jesi/article/6672
Search via ReFindit
Lanne, M., Nieminen, M., & Leikas, J. (2025). Organisational tensions in introducing socially sustainable AI. AI & Society, 40, 5213-5233 https://doi.org/10.1007/s00146-025-02293-y
Search via ReFindit
Maknickienė, N., Maknickas, A., & Tvaronavičienė, M. (2026). Choosing approaches towards investment decisions: A case study of artificial intelligence (AI) based method use. Economics & Sociology, 19(1), 276–291 https://doi.org/10.14254/2071-789X.2026/19-1/14
Search via ReFindit
Menshikov, V., Komarova, V., Bolakova, I., & Radionovs, A. (2024). Artificial intelligence and artificial sociality: Sociological interpretation and interdisciplinary approach. Filosofija. Sociologija, 35(2), 216-226 https://doi.org/10.6001/fil-soc.2024.35.2.8
Search via ReFindit
Minotakis, A. (2025). Regulating AI in the workplace: A critique of the EU AI Act and the Platform Work Directive through a worker-centred lens. Platforms & Society, 2, 1-14 https://doi.org/10.1177/29768624251396248
Search via ReFindit
Murire, O. T. (2024). Artificial intelligence and its role in shaping organizational work practices and culture. Administrative Sciences, 14(12), 316 https://doi.org/10.3390/admsci14120316
Search via ReFindit
Nguyen, T., & Elbanna, A. (2025). Understanding human-AI augmentation in the workplace: A review and a future research agenda. Information Systems Frontiers https://doi.org/10.1007/s10796-025-10591-5
Search via ReFindit
Papagiannidis, E., Enholm, I. M., Dremel, C., Mikalef, P., & Krogstie, J. (2023). Toward AI governance: Identifying best practices and potential barriers and outcomes. Information Systems Frontiers, 25, 123-141 https://doi.org/10.1007/s10796-022-10251-y
Search via ReFindit
Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. Journal of Strategic Information Systems, 34(2), 101885 https://doi.org/10.1016/j.jsis.2024.101885
Search via ReFindit
Radavičius, T., & Tvaronavičienė, M. (2022). Digitalisation, knowledge management and technology transfer impact on organisations’ circularity capabilities. Insights into Regional Development, 4(3), 76-95 https://doi.org/10.9770/IRD.2022.4.3(5
Search via ReFindit
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation-augmentation paradox. Academy of Management Review, 46(1), 192-210 https://doi.org/10.5465/amr.2018.0072
Search via ReFindit
Rizana, A. F., Wiratmadja, I. I., & Akbar, M. (2025). Exploring capabilities for digital transformation in the business context: Insight from a systematic literature review. Sustainability, 17(9), 4222 https://doi.org/10.3390/su17094222
Search via ReFindit
SAP. (2024). SAP updates its ambition 2025 and announces transformation program for 2024. SAP News Center, 23 January https://news.sap.com/2024/01/sap-updates-its-ambition-2025-and-announces-transformation-program-for-2024/
Search via ReFindit
Stark, D., & Vanden Broeck, P. (2024). Principles of algorithmic management. Organization Theory, 5(2) https://doi.org/10.1177/26317877241257213
Search via ReFindit
Talaei-Khoei, A., Yang, A. T., & Masialeti, M. (2024). How does incorporating ChatGPT within a firm reinforce agility-mediated performance? The moderating role of innovation infusion and firms’ ethical identity. Technovation, 132, 102975 https://doi.org/10.1016/j.technovation.2024.102975
Search via ReFindit
Vacek, D. (2026). Responsible artificial intelligence? AI & Society, 41, 843-855 https://doi.org/10.1007/s00146-025-02604-3
Search via ReFindit
Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation. International Data Privacy Law, 7(2), 76-99 https://doi.org/10.1093/idpl/ipx005
Search via ReFindit
Walmart. (2025). Walmart’s U.S. supply chain playbook goes global — and it’s reinventing retail at scale. Walmart Corporate News, 17 July. Retrieved from https://corporate.walmart.com/news/2025/07/17/walmarts-us-supply-chain-playbook-goes-global-and-its-reinventing-retail-at-scale
Search via ReFindit
Wamba, S. F. (2022). Impact of artificial intelligence assimilation on firm performance: The mediating effects of organizational agility and customer agility. International Journal of Information Management, 67, 102544 https://doi.org/10.1016/j.ijinfomgt.2022.102544
Search via ReFindit
Wamba, S. F., Queiroz, M. M., Pappas, I. O., & Sullivan, Y. (2024). Artificial intelligence capability and firm performance: A sustainable development perspective by the mediating role of data-driven culture. Information Systems Frontiers, 26(6), 2185-2203 https://doi.org/10.1007/s10796-024-10531-9
Search via ReFindit
Weber, M., Engert, M., Schaffer, N., Weking, J., & Krcmar, H. (2023). Organizational capabilities for AI implementation—Coping with inscrutability and data dependency in AI. Information Systems Frontiers, 25(4), 1549-1569 https://doi.org/10.1007/s10796-022-10297-y
Search via ReFindit
RSS 1.0