Explainable Machine Learning for Predicting Organizational AI Adoption: Strategic Alignment, Organizational AI Readiness, and Technology-Organization-Environment Factors

Artificial intelligence (AI) is no longer only a technical issue. For many organizations, deciding whether and how to use AI also depends on strategy, internal resources, employee readiness, governance, and the external business environment. This study develops an explainable machine learning framework to examine these factors using enterprise survey data from 359 private organizations in Slovakia. The analysis brings together measures of organizational AI maturity, business sentiment, and Technology–Organization–Environment (TOE) factors. Logistic Regression, Random Forest, and XGBoost were evaluated using an 80/20 stratified train-test split, five-fold cross-validation, three random seeds, and 5,000 bootstrap resamples. SHAP values and permutation importance were used to understand which variables contributed most to the model predictions. Under the primary definition of adoption, 199 organizations were classified as adopters and 160 as non-adopters. Across the three random seeds, mean ROC-AUC values were 0.823 for Logistic Regression, 0.840 for Random Forest, and 0.851 for XGBoost. The corresponding mean PR-AUC values were 0.856, 0.870, and 0.871. In the primary analysis, TOE organization, technology, and environment together represented about 71.97% of the aggregated mean absolute SHAP contribution. Strategic alignment, perceived measurable AI benefits, privacy maturity, organizational resources, and several technological and environmental factors were among the more influential predictors. Additional analyses using a stricter adoption definition, the cleaned dataset, different random seeds, and a reduced predictor set showed broadly similar patterns. Overall, the study illustrates how explainable machine learning can help examine organizational AI adoption while keeping predictive relationships clearly separate from claims about causation.