AI-Powered Talent Mobility Optimization in SAP SuccessFactors for Strategic Workforce Planning

Strategic workforce planning is increasingly dependent on intelligent talent mobility systems capable of aligning employee competencies with evolving organizational needs. This study explores the integration of artificial intelligence (AI) within SAP SuccessFactors to optimize internal talent mobility, leveraging machine learning, predictive analytics, and skills inference models. The proposed AI-powered framework analyzes workforce data to identify skill gaps, forecast future talent demands, and recommend optimal role transitions, succession paths, and development opportunities. By applying AI-driven decision support, the system enhances visibility into workforce capabilities, enabling proactive redeployment of internal talent, minimizing hiring costs, and improving employee engagement through personalized career development. The framework also incorporates sentiment analytics and performance indicators to support fair, bias-aware talent recommendations. Experimental evaluation using simulated enterprise HR datasets demonstrates significant improvements in prediction accuracy, workforce utilization, and role fit alignment when compared to conventional optimization methods. The findings underscore the potential of AI-embedded SAP SuccessFactors to drive scalable, data-driven talent management strategies that support organizational agility in dynamic business environments.