The integration of Artificial Intelligence (AI) into the workplace prompts complex and heterogeneous employee responses regarding job security, which are often obscured by traditional analytical methods. To address this complexity, we adopt a person-centered approach, using Latent Class Analysis (LCA) on survey data from 2,603 employees in large global enterprises to identify distinct psychological trajectories based on current and expected job security. Our analysis reveals three distinct groups: a majority "Resiliently Optimistic" cohort, a "Stagnant Neutral" group, and a significant "Anxiously Declining" minority, demonstrating that perceptions of AI's impact are highly stratified. We then employ a multinomial logistic regression, using Elastic Net for feature selection, to identify the specific organizational policies, cultural attributes, and affective dispositions that predict membership in these latent classes. Membership in the "Resiliently Optimistic" class is strongly associated with structural enablers that provide employees with agency and tangible value, such as direct involvement in AI development and non-monetary incentives like peer recognition and learning certifications. Conversely, membership in the "Anxiously Declining" class is driven by deterrents such as fear of job loss and privacy concerns, which overwhelm the potential benefits of organizational support. These findings indicate that fostering psychological stability amidst technological change hinges not on abstract commitments to training, but on implementing participatory, incentive-aligned frameworks that empower employees and decouple AI-driven task evolution from perceived job displacement.