With the rapid development of artificial intelligence technology, its widespread application in the field of business management has become a significant issue faced by contemporary enterprises. Based on the Technology Acceptance Model, this study explores the impact of AI technology acceptance on organizational decision-making efficiency, performance, and the depth of technology application. It also reveals the driving mechanisms of top management support, perceived usefulness, and perceived ease of use on AI technology adoption through path analysis. To validate the research hypotheses, the study employed structural equation modeling (SEM) based on survey data collected from 420 respondents across various industries. The study found that top management support significantly enhances technology acceptance through perceived variables, while perceived usefulness is the core factor driving technology adoption. Although perceived ease of use has a weaker effect, it is equally important in lowering the psychological barriers during the initial stages of technology adoption. The adoption of AI technology has significantly improved organizational decision efficiency and overall performance, promoting the deep application of technology by optimizing resource allocation and enhancing scientific decision-making capabilities. This study further validates the applicability of the TAM theory in the context of AI technology, expanding its theoretical explanatory power in complex technology-adoption mechanisms. At the same time, the research provides practical guidance for enterprises in the introduction and application of technology, emphasizing that managers need to shape an open and innovative organizational culture at a strategic level and enhance employees’ willingness to accept technology through technical training and value transmission. Future research can incorporate cross-cultural and multi-level analytical frameworks to explore the dynamic adaptation paths of AI technology adoption and its potential risks in sustainable development.

AI-powered virtual humans are increasingly used as live streaming presenters, yet it remains unclear how consumers translate experiences with these embodied agents into trust and repurchase intention. Rather than tracing a complete multi-stage consumer journey, this study focuses on a recent virtual human live streaming session as a focal touchpoint that compresses product discovery, evaluation, interaction, and purchase support. Integrating consumer touchpoint logic with the Stimulus–Organism–Response (S-O-R) framework, we examine how perceptual touchpoint experience (active control, synchronicity, and two-way communication) and cognitive touchpoint experience (perceived expertise and perceived competence) shape consumer trust and repurchase intention. Using survey data from 305 consumers with virtual human live streaming experience, this study tested the proposed model via partial least squares structural equation modeling (PLS-SEM) and supplemented the findings with fuzzy-set qualitative comparative analysis (fsQCA). The results showed that both perceptual and cognitive touchpoint experiences significantly enhanced trust, which partially mediated their effects on repurchase intention; cognitive touchpoint experience also exerted a strong direct effect on repurchase intention. The fsQCA results revealed two equifinal configurations leading to high repurchase intention: a full-experience path (high perceptual experience + high cognitive experience + high trust) and a capability-dominant path (low perceptual experience + high cognitive experience + high trust). This study contributes to virtual human commerce research by distinguishing interaction-process cues from competence-based source cues and by showing when AI-powered live streaming can support post-touchpoint retention.