Joint source channel coding (JSCC) has attracted increasing attentions due to its robustness and high efficiency. However, the existing research on JSCC mainly focuses on minimizing the distortion between the transmitted and received information under the constraint of the required data rate. Therefore, the transmitted bits may be far more than the minimal threshold according to the rate-distortion (RD) theory even though the transmitted information is well recovered. In this paper, we propose an adaptive Information Bottleneck (IB) guided JSCC (AIB-JSCC), which aims at achieving the theoretically maximal compression ratio for a given reconstruction quality. In particular, we first derive a mathematically tractable form of loss function for AIB-JSCC. To trade off compression and reconstruction quality, we further propose an adaptive algorithm that adjusts the hyperparameter of the proposed loss function dynamically according to the distortion during the training. Experimental results show that AIB-JSCC can significantly reduce the required amount of the transmitted data and improve the reconstruction quality and downstream artificial-intelligent task performance.