<p>Metrology plays a crucial role in ensuring equitable trade and economic stability through reliable measurement traceability. As industries advance, the systematic digitalization of calibration processes becomes increasingly vital. This study proposes a comprehensive framework for digitalizing the metrology domain, focusing on strengthening enablers and mitigating barriers based on the responses of researchers and industry professionals. The study first compared the group’s perceptions using independent t-tests, then identified key enablers and barriers using Grey Relational Analysis, and finally, established causal relationships between the factors by utilizing Structural Equation Modeling to facilitate the digitalization of metrology. For causal relations, the initial model was developed by leveraging a locally developed Large Language Model (FLAN-T5-LARGE) on the available research literature, followed by rigorous evaluation based on fitness statistics. However, to construct a more robust model and compare results with the large language model, multiple combinations of enablers and barriers were systematically evaluated in view of approx. 72 quadrillion scenarios for each group based on fitness statistics. The results indicate that while both groups recognize the importance of digitalization in metrology, researchers prioritize metrological performance and addressing technical limitations, whereas industry professionals emphasize efficiency and cost-effectiveness for successful implementation. The LLM-based model showed a slight performance gap compared to the systematic approach, validating its utility. These insights provide a robust foundation for bridging perception gaps and fostering digital metrology adoption.</p>