<p>This study investigates how responsible AI signals (RAS) such as autonomy, justice, beneficence, explainability, and nonmaleficence enhance employees’ career sustainability, with particular focus on dynamic capability, AI emotional response, and AI knowledge management (knowledge sharing, acquisition, and application). Grounded in the Cognition–Affect–Conation (CAC) framework, the study extends its scope from psychology to human resource management by explaining how cognitive, emotional, and behavioral mechanisms jointly shape employees’ responses to responsible AI practices. A quantitative research design was employed using an online questionnaire, gathering responses from a sample of 717 employees in Vietnam and analyzed using PLS-SEM. The findings reveal that RAS strongly promotes AI emotional response, dynamic capability, and knowledge management processes, including knowledge sharing, acquisition, and application. In turn, AI emotional response, dynamic capability, and the application and sharing of knowledge exert significant positive effects on employee well-being and innovation performance, whereas knowledge acquisition shows no meaningful impact. The study advances theory by integrating the CAC framework with Responsible AI principles to explain how employees adapt and collaborate with AI in organizations. Practically, the findings indicate that adopting responsible AI principles can enhance employee creativity, emotional engagement, and adaptability by promoting knowledge sharing, supportive policies, and transparent AI practices. Managers are encouraged to design learning-oriented environments, continuous AI ethics training, and participatory mechanisms that allow employees to engage with AI fairly and autonomously, thereby fostering well-being, innovation, and long-term career sustainability.</p>