Translating Multimodal AI into Real-World Inspection: TEMAI Evaluation Framework and Pathways for Implementation
This work addresses the problem of bridging AI capabilities to real-world industrial inspection for practitioners, though it is incremental as it adapts existing translational research principles to a new domain.
The paper tackles the challenge of implementing multimodal AI in industrial inspection by introducing the TEMAI framework, which evaluates technical feasibility, organizational readiness, and value realization, showing that technical capability alone is insufficient for value creation. Empirical validation in retail and photovoltaic sectors revealed significant differences in value realization patterns despite similar capability reduction rates, confirming the framework's effectiveness across diverse industries.
This paper introduces the Translational Evaluation of Multimodal AI for Inspection (TEMAI) framework, bridging multimodal AI capabilities with industrial inspection implementation. Adapting translational research principles from healthcare to industrial contexts, TEMAI establishes three core dimensions: Capability (technical feasibility), Adoption (organizational readiness), and Utility (value realization). The framework demonstrates that technical capability alone yields limited value without corresponding adoption mechanisms. TEMAI incorporates specialized metrics including the Value Density Coefficient and structured implementation pathways. Empirical validation through retail and photovoltaic inspection implementations revealed significant differences in value realization patterns despite similar capability reduction rates, confirming the framework's effectiveness across diverse industrial sectors while highlighting the importance of industry-specific adaptation strategies.