The use of AI-enabled decision support systems for personnel selection is on the rise. However, users’ understanding of the technology’s underlying processes is often limited, combined with low user acceptance and trust. Explainable AI-enabled systems offer a solution to mitigate this discrepancy by providing additional information to recipients. By simplifying their outputs or reducing the complexity of the underlying AI model, explainable AI-enabled systems make their operations easier to understand. In our study with N = 328 participants, we investigated how the type of advice (no advice vs. AI advice without explanation vs. AI advice with visualized explanation) and the accuracy of advice (80% correct advice) impacted decision performance, perceived advice quality, and participants' confidence in their decision in a personnel selection task. In line with our hypothesis, receiving correct advice had a significant positive effect on the performance, the advice quality and participants’ confidence in their decision. Against our hypothesis, explainable advice resulted in non-significant effects. The results show that the quality of the AI system had a strong impact on participants’ performance. However, providing AI advice along with visual explanations for its prediction did not help the participants to recognize incorrect advice more easily. As we did not find a simple solution to reduce overreliance in incorrect advice, further implications are discussed.