Translating Multimodal AI into Real-World Inspection: TEMAI Evaluation Framework and Pathways for Implementation
Zehan Li, Jinzhi Deng, Haibing Ma, Chi Zhang, Dan Xiao
TL;DR
Industrial inspection suffers from fragmented AI deployments and uncertain ROI for multimodal systems. The TEMAI framework translates multimodal AI from lab to practice using three core dimensions—Capability, Adoption, and Utility—and specialized metrics such as the Value Density Coefficient to quantify value concentration. The methodology combines Translational Research principles, expert weighting via Delphi, and a standardized value model $Man-hour = BaseRate \\times AIEfficiency \\times RiskWeight$ to enable ROI calculation, validated through Retail and PV case studies that reveal domain-specific adoption and value patterns. The results show that technology capability must be paired with organizational readiness and ecosystem context to realize meaningful value, and TEMAI provides practical implementation pathways for cross-domain adoption.
Abstract
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.