FROM CLASSROOM TO ENTERPRISE: AI ADOPTION AND DIGITAL EDUCATION
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The European Union's 2030 target of 75% enterprise AI adoption faces a structural paradox: adoption rates remain well below the trajectory, yet citizen surveys reveal broad awareness of the need for digital transformation. This paper argues that the adoption gap cannot be adequately explained by firm-level factors alone. Its objective is to determine the extent to which educational pipeline conditions constrain enterprise AI adoption in the EU and to extend the Technology-Organization-Environment (TOE) framework accordingly. Drawing on the TOE framework and integrating evidence from DESI 2025, the State of the Digital Decade 2025 report, and Flash Eurobarometer 564 on Future Needs in Digital Education (May 2025), we map a systemic constraint linking the educational pipeline to organizational AI capacity and environmental policy effectiveness. Using Portugal as an illustrative case, the analysis shows that a high degree of citizen-level awareness and institutional willingness to embrace AI in education coexist with material infrastructure deficits, gaps in teacher preparation, and low AI literacy in the adult workforce. These findings enrich the TOE framework by surfacing the educational-pipeline dimension as a structurally prior condition for enterprise AI adoption. Policy implications are drawn for the EU's Digital Decade agenda and for member states seeking to accelerate AI take-up among enterprises and SMEs. The paper contributes to TOE-based AI adoption research by linking organizational readiness to upstream education and skills formation processes.
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Dados de financiamento
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Fundação para a Ciência e a Tecnologia
Números do Financiamento UIDB/00685/2025 -https://doi.org/10.54499/UID/00685/2025 -
Secretaria Regional do Mar, Ciência e Tecnologia
Números do Financiamento CEEAplA | School of Business and Economics of the University of the Azores, from the Regional Directorate for Science, Innovation and Development - Azores Govern-ment through the research grant M1.1. A/FUNC.UI&D/018/2025 (PROSCIENTIA).
Referências
Awa, H. O., Ojiabo, O. U., & Emecheta, B. C. (2015). Integrating TAM, TPB and TOE frameworks and expanding their characteristic constructs for e-commerce adoption by SMEs. Journal of Science and Technology Policy Management, 6(1), 76–94. https://doi.org/10.1108/JSTPM-04-2014-0012
Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review Digital Articles, 1–20.
Dai, C., & Ke, F. (2022). Educational applications of artificial intelligence in simulation-based learning: A systematic mapping review. Computers and Education: Artificial Intelligence, 3, 100087. https://doi.org/10.1016/j.caeai.2022.100087
Draghi, M. (2024). The future of European competitiveness: Part A: A competitiveness strategy for Europe. European Commission.
European Commission. (2025a). State of the Digital Decade 2025: Keep building the EU’s sovereignty and digital future. Annex 1: State of the EU digital transformation in 2025: Progress and horizontal recommendations (COM(2025) 290 final).
European Commission. (2025b). State of the Digital Decade 2025: Short country reports 2025 – Portugal (Annex 23, COM(2025) 290 final).
European Commission. (2025c). Flash Eurobarometer 564: Future needs in digital education. Data annex (May 2025). https://europa.eu/eurobarometer
Fosso Wamba, S., Queiroz, M. M., Pappas, I. O., & Trinchera, L. (2024). Artificial intelligence capability and firm performance: A sustainable development perspective by the mediating role of data-driven culture. Information Systems Frontiers, 26, 2189–2203. https://doi.org/10.1007/s10796-023-10460-z
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). https://doi.org/10.1145/3313831.3376727
Malerba, F. (2002). Sectoral systems of innovation and production. Research Policy, 31(2), 247–264. https://doi.org/10.1016/S0048-7333(01)00139-1
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041
Oliveira, T., & Martins, M. F. (2010). Understanding e-business adoption across industries in European countries. Industrial Management & Data Systems, 110(9), 1337–1354. https://doi.org/10.1108/02635571011087428
Tiago, F., & Almeida, A. (2026). Environmental, organizational, and individual determinants of AI adoption: A multilevel analysis. Journal of Innovation & Knowledge, 13, 100934. https://doi.org/10.1016/j.jik.2025.100934
Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
Tursunbayeva, A., & Ben Gal, H. C. (2024). Adoption of artificial intelligence: A TOP framework-based checklist for digital leaders. Business Horizons, 67(4), 357–368. https://doi.org/10.1016/j.bushor.2024.04.006