Building trustworthiness in AI has become a critical task of AI governance to secure trustworthiness of AI systems/services. Since the trustworthiness concept is highly broad, its evaluation and implementation are tough tasks both for AI system/service providers and users. The authors previously developed a process to evaluate AI systems’ triste worthiness in terms of quality-in-use components. Using the systems’ functional requirements, but the mapping design was complicated and application specific. In this paper we propose an improved method to evaluate the quality-in-use components, utilizing quality components and these measurement indices based on ISO/IEC software quality standards. The method provides a unified, cross-application process by mapping vendor-implemented product/service quality components to quality-in-use components to derive quantitative evaluation results. We apply this method to an autonomous drone case study and analyze its effectiveness.