Assessing Multimodal AI's Realized Value in Industrial Inspection with TEMAI

Multimodal Artificial Intelligence (AI) systems offer transformative potential for industrial inspection, yet translating their capabilities into tangible value is challenging. This paper utilizes the Translational Evaluation of Multimodal AI for Inspection (TEMAI) framework as a quantitative method to assess and compare AI implementation in realworld scenarios. Through detailed case studies in retail and photovoltaic (PV) inspection, we quantitatively analyze the impact of Capability, Adoption, and Utility dimensions on final value realization. Results reveal significant value erosion from initial technical capability to realized utility, primarily driven by adoption-related factors. For instance, the retail case, despite a strong organizational Technical Absorption Capacity, suffered from Scene Transfer Difficulty, while the PV case benefited from better Value Chain Optimization but faced challenges in its own Technical Absorption Capacity. This study demonstrates TEMAI's utility in identifying critical bottlenecks and provides empirical insights for optimizing multimodal AI deployment in diverse industrial contexts.