This study explores the transformative potential of integrating Artificial Intelligence (AI) into SAP SuccessFactors Performance Management processes, with a focus on goal alignment and continuous feedback. By examining traditional challenges in performance evaluation—such as biased assessments, static goal setting, and delayed feedback—the paper presents an AI-augmented framework aimed at creating agile, real-time, and objective performance management. The methodology employs a mixed-methods approach combining expert interviews, system configuration analysis, and a case study on a global enterprise’s implementation. Key findings reveal that AI-driven algorithms significantly improve goal transparency, enable early identification of skill gaps, and foster a culture of continuous development through intelligent nudges and sentiment analysis. Furthermore, AI facilitates alignment between individual performance goals and broader organizational objectives using data-driven recommendations. The study concludes that while AI integration is still maturing, its application within SAP SuccessFactors marks a pivotal shift toward dynamic, fair, and forward-looking performance ecosystems. Limitations include data quality dependencies and ethical considerations. Future research is recommended on explainable AI models and long-term impact assessment.