Exploring Characteristics of High-Readiness AI–Digital Twin Studies in Mining: A Quantitative Analysis

Background: The mining sector’s digital transformation increasingly relies on AI-driven digital twins (AI-DTs) that integrate real-time data with intelligent analytics. A recent systematic literature review (SLR) of 68 studies identified a critical gap: which technical choices guarantee industrial success? Objective: This study extends that SLR by validating a Deployment Readiness Score (DRS) to identify which combinations of technical and methodological choices are associated with high readiness AI-DT studies in the literature. Methods: Each study was coded across eight dimensions and assigned to a DRS based on data source, validation method, and operational metric reporting. A random forest classifier was used as a consistency check for the DRS framework. Results: The model achieved 100% test accuracy as an internal consistency check within the coded dataset, confirming that the DRS scoring rules produce a coherent classification across the reviewed studies. The data source was the strongest association (41.2%), followed by publication year (25.6%) and validation method (22.7%). AI technique showed minimal association (0.9%). Studies using sensory data achieved 100% high readiness within the DRS framework; mixed data achieved 95.2%; and experimental validation achieved 95.7%. The proportion of high-readiness studies increased from 35.7% in 2024 to 93.5% in 2025. Conclusions: Within the reviewed literature, high-fidelity data and rigorous validation show stronger associations with high readiness than algorithmic complexity. We provide a Deployment Readiness Scorecard and propose minimal reporting standards, shifting focus from theoretical algorithms to practical data acquisition and validation.