AI-Driven High-Risk Implementation Prediction for Enterprise Digital Transformation Governance Using Machine Learning

Enterprise digital transformation projects are among the most complex and costly initiatives in modern organizations, yet conventional governance frameworks rely predominantly on reactive, lagging indicators of project health. This paper presents an empirical binary classification study applying machine learning to identify high-risk implementations in enterprise digital transformation portfolios. A publicly available dataset of 4,000 project records encoding 49 governance-relevant features is used; an original four-level risk taxonomy is collapsed into two governance-actionable classes: High-Risk and Not High-Risk Implementation. This study proposes a governance-actionable machine learning framework that converts multi-level project risk into binary escalation decisions and combines cross-validated model selection with direct SHAP attribution for enterprise transformation portfolios. Seven classifiers are benchmarked against a majority-class baseline across six metrics. Model selection incorporates five-fold stratified cross-validation and grid-search hyperparameter optimization for the two strongest classifiers. Five-fold stratified cross-validation confirms generalization stability across all models: AdaBoost achieves the highest cross-validation performance—85.85% ± 1.46% accuracy, 85.56% ± 1.32% balanced accuracy, 84.02% ± 1.36% F1-score, and 0.9380 ± 0.0070 ROC-AUC—and also leads on the held-out test set (90.87% accuracy, 90.69% balanced accuracy, 89.76% F1-score, 0.9766 ROC-AUC). All seven classifiers exhibit standard deviations below the 3.0 percentage-point instability threshold, confirming robust generalization without overfitting. SHAP-based explainability, applied directly to the AdaBoost model via TreeExplainer, identifies Complexity_Score, Org_Process_Maturity, and Technology_Familiarity as the primary drivers of high-risk classification, consistent with digital transformation governance literature. Practical integration scenarios for portfolio dashboards and governance escalation workflows are discussed.