## 6 Conclusions  
This study set out to examine the key macro-level determinants of cloud adoption across 27 EU member states by integrating socio-economic, educational, and technological variables into a machine learning framework. Using two harmonized panels (2014-2021 and 2014-2024), we trained and validated several models, including Random Forest, XGBoost, and SVM, to predict national cloud uptake rates. The models were further interpreted using explainable AI techniques such as SHAP values, ICE curves, and clustering analysis, yielding both predictive insights and actionable interpretations. The findings consistently highlight the dominant role of digital human capital and infrastructure in enabling cloud transformation. Specifically, the presence of ICT specialistsand broadband access emerged as the most influential predictors of cloud adoption. Higher levels of tertiary education and GDP per capita also contributed positively, albeit with a more moderate effect. Conversely, higher unemployment rates were negatively associated with cloud readiness, likely reflecting reduced capacity for investment and innovation. Partial dependence and interaction plots revealed important nonlinearities and synergies, especially between digital skills and broadband infrastructure, while hierarchical clustering uncovered clear groupings of digitally advanced, mid-tier, and lagging countries. These insights carry strong policy relevance. First, they suggest that strengthening digital skills through vocational and higher education, particularly in ICT-related fields, remains a critical priority at the EU level. Second, investment in broadband infrastructure, especially in rural and underserved regions, is essential to unlock the full potential of cloud-based services. Third, the clustering analysis underscores the need for differentiated strategies across EU member states. For digitally lagging countries, simultaneous investment in infrastructure and human capital is vital, whereas mid-tier nations may benefit from targeted innovation policies and harmonized digital regulation. In contrast, advanced economies should focus on consolidating digital leadership and addressing institutional bottlenecks that may hinder further cloud scaling. Countries such as Romania and Bulgaria, despite strong technical indicators, may require broader socio-economic interventions to translate digital capacity into adoption outcomes. Nevertheless, this study is not without limitations. The absence of broadband data beyond 2021 constrained full-panel modeling, limiting the temporal horizon of some analyses. While machine learning models and explainability tools offer rich insights, they remain correlational in nature; causal inference would benefit from complementary approaches such as natural experiments or instrumental variable designs. Moreover, national-level analyses cannot capture the heterogeneity that exists within countries. Thus, future studies could incorporate firm-level or regional microdata to assess intra-country disparities. Finally, while our model captures cross-sectional temporal variation, future research could benefit from explicitly incorporating dynamic modeling frameworks to account for delayed policy impacts and feedback effects over time. Moreover, future research could extend the comparative scope beyond the EU27 by including EFTA countries or adjacent regions. Such a comparison would help assess divergence in structural readiness, regulatory environments, and digital policy implementation. Also, future studies could explore how national progress toward Digital Decade targets interacts with cloud adoption over time, potentially leveraging dynamic policy indicators or national digital investment scores. Conceptualization, C.T. ; methodology, C.T. ; software, C.T. ; validation, C.T. and M.F. ; formal analysis, C.T. ; investigation, C.T., M.F., P.S., V.V. and P.P. ; resources, C.T. ; data curation, C.T. ; writing--original draft preparation, C.T., M.F., P.S., V.V., P.P. and K.K. ; visualization, C.T. ; supervision, C.T.