This study examines the impact of integrating AI assistants in digital workplaces by focusing on employees’ willingness to continuously share data and their subsequent adoption intention. Data sharing with AI assistants is unique because it involves persistent, adaptive data collection embedded seamlessly into routine work. This continuous data flow creates significant trade-offs for employees, requiring a holistic understanding of these dynamics. Using a calculus decision-making framework, we show that employees weigh both benefits and risks of data sharing equally. In particular, factors like personalization, organizational value, and privacy invasion emerge as pivotal in data sharing decisions. This study contributes to theory by extending the privacy calculus framework into workplace contexts, focusing on AI tools and applying it to a continuous data sharing context. We offer actionable insights for organizations to foster the integration of AI assistants by better aligning AI capabilities with employee expectations.