An Interpretable Deep Learning Framework for Measuring Organizational Digital Transformation Readiness

Abstract

The accelerating pace of digital transformation (DT) across industries demands accurate, transparent, and adaptable maturity evaluation frameworks capable of capturing complex organizational behaviors. Conventional fuzzy logic and decision tree-based maturity models cannot effectively represent the nonlinear dependencies among DT indicators and often produce inconsistent, opaque assessments. To overcome these limitations, this study proposes the TUMI (Transformer TabNet Unified Maturity Intelligence) framework, a novel hybrid deep learning architecture specifically designed for DT maturity assessment. The framework uniquely integrates FT-Transformer and TabNet, enabling simultaneous modeling of global feature dependencies through attention mechanisms and localized sparse feature selection aligned with DT maturity metrics. This domain-tailored hybridization goes beyond existing hybrid or ensemble approaches by supporting real-time readiness estimation, accommodating heterogeneous organizational indica tors, and offering structured interpretability based on complementary attention weights and feature selection masks. The proposed model was trained using a multi-dimensional DT maturity dataset implemented in Python (PyTorch). Experimental results demonstrate strong predictive performance, with 97.0% accuracy, 96.0% precision, 95.0% recall, and an AUC of 98.2%, representing an 8.5% improvement over traditional fuzzy and decision tree models. The interpretability provided by the combined mechanisms offers clearer insight into the organizational determinants influencing maturity progression. Overall, TUMI enhances transparency, diagnostic capability, and scalability, providing an evidence-based, explainable, and cross-industry applicable solution for supporting organizations in evaluating and improving their digital transformation maturity.