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Artificial intelligence has gradually become an important tool for organizational strategy and research and development activities, with applications involving human–machine collaboration, process transformation, and the dynamic process of technology adoption. However, AI usage behavior of research and development personnel in professional contexts has not yet been thoroughly investigated. Grounded in social cognitive theory, this study examines the mechanisms through which organizational support, social influence, performance expectancy, and personal innovativeness affect AI usage. Using purposive sampling, data were collected from 238 valid responses of technology R&D personnel in Kaohsiung, and structural equation modeling was employed for analysis. The results indicate that the direct effects of organizational support and social influence on AI usage are not significant, but both indirectly influence AI usage through performance expectancy, highlighting performance expectancy as the core driver of AI adoption. In addition, personal innovativeness demonstrates a significant moderating effect, as individuals with higher innovativeness are better able to translate performance beliefs into actual behavior. These findings suggest that in knowledge-intensive environments, external support and social norms must be integrated with employees’ perceptions of AI benefits to facilitate actual adoption. Theoretically, this study extends the application of social cognitive theory to AI usage, addressing the limitations of prior research that focused solely on external factors; practically, it reminds managers to strengthen employees’ performance expectancy and design promotion strategies tailored to individual differences in order to foster organizational digital transformation.