Understanding the drivers of workforce outcomes requires analytical methods capable of distinguishing correlation from true causal influence. Traditional predictive models commonly used in HR systems can forecast attrition, performance, or engagement shifts, yet they offer limited visibility into the underlying mechanisms that produce these changes. This paper introduces a causal AI approach that integrates SAP SuccessFactors operational data, SAP Analytics Cloud workforce metrics, and diverse multi source HR signals to estimate the effects of organizational interventions on measurable employee outcomes. The proposed framework combines structural causal modeling, treatment effect estimation, mediation analysis, and counterfactual reasoning to evaluate how learning pathways, compensation adjustments, managerial behaviors, mobility opportunities, and work environment conditions contribute to changes in performance, retention, and development trajectories. A unified data architecture harmonizes information from SuccessFactors modules with analytical layers in SAP Analytics Cloud to construct causal ready datasets that isolate confounders and quantify both direct and indirect effects. Empirical evaluation across representative HR scenarios demonstrates that causal models provide more actionable insight than conventional predictive methods by clarifying which interventions meaningfully influence workforce outcomes and under what conditions. The study argues that embedding causal AI within enterprise HR ecosystems supports evidence informed decision making, strengthens workforce planning accuracy, and enhances the strategic value of people analytics in complex organizational environments.