Introduction: Employing people with psychiatric disorders poses social and financial challenges, such as high turnover rates and increased onboarding costs. This study attempts to address the gap in evidence-based policies for risk mitigation in employment through the development of Smart Job Support, an AI model aimed at optimizing career rehabilitation and human capital investment.

Objective: The main objective is to evaluate economic risk after implementing AI-assisted recruitment and monitoring in active employment for individuals with psychiatric disorders, in order to reduce financial exposure and encourage employment.

Methods: The model combines psychometric data obtained from standardized assessments, biometric data from wearable devices, and immersive job simulations to capture behavioral data. Machine learning algorithms were created to allocate individual profiles to appropriate job positions. A pilot study with 50 diagnosed participants was conducted. Employability outcomes included job retention rate, productivity measures, absenteeism, and employer-perceived ROI.

Results: The first assessment indicated a 35% improvement in job placement satisfaction, a 28% reduction in early turnover, and a 21% decrease in onboarding costs. Employers noted improvements in the quality of work and a reduction in absenteeism due to lower stress levels, which suggests that AI-powered assistance helped in both socio-economic inclusion and cost optimization.

Conclusions: The research findings demonstrate the ability of Smart Job Support to augment occupational rehabilitation through assistive technologies by aligning economically driven inclusivity with strategic planning. The results justify the expansion of the model and its implementation in business settings focused on integrating marginalized groups with controlled financial risks.