Reengineering Succession Pipelines in SAP SuccessFactors: An AI-Driven Framework for Ethical, Predictive, and Inclusive Leadership Readiness

Succession planning has evolved from a static, managerial process into a dynamic, data-driven discipline shaped by advances in artificial intelligence. This study examines how predictive algorithms embedded within SAP SuccessFactors can systematically identify, evaluate, and prepare future leaders by analyzing multifactor workforce indicators. The research employs a mixed-method design that integrates system configuration analysis, predictive modeling, and expert validation to assess the operational and ethical viability of AI-enhanced succession pipelines. Using anonymized organizational data drawn from simulated SuccessFactors environments, decision tree and logistic regression models were tested to evaluate leadership readiness and mobility potential. Comparative evaluation with traditional nine-box frameworks revealed a 27 percent improvement in accuracy for identifying high-potential employees and a measurable reduction in bias across gender and department categories. Qualitative insights from HR technology experts further indicated that transparent model governance and explainable outputs strengthen user trust and managerial adoption. The findings establish that integrating AI into SAP SuccessFactors Succession and Development transforms leadership continuity planning from subjective appraisal to an evidence-based, ethically governed, and strategically aligned process. Beyond technical outcomes, this research contributes a replicable framework that aligns machine learning, organizational behavior, and ethical oversight, enabling enterprises to build inclusive leadership pipelines that support long-term agility and workforce equity.