In today's critical domains, the predominance of black-box machine learning models amplifies the demand for Explainable AI (XAI). The widely used SHAP values, while quantifying feature importance, are often too intricate and lack human-friendly explanations. Furthermore, counterfactual (CF) explanations present `what ifs' but leave users grappling with the 'why'. To bridge this gap, we introduce XAIstories. Leveraging Large Language Models, XAIstories provide narratives that shed light on AI predictions: SHAPstories do so based on SHAP explanations to explain a prediction score, while CFstories do so for CF explanations to explain a decision. Our results are striking: over 90% of the surveyed general audience finds the narrative generated by SHAPstories convincing. Data scientists primarily see the value of SHAPstories in communicating explanations to a general audience, with 92% of data scientists indicating that it will contribute to the ease and confidence of nonspecialists in understanding AI predictions. Additionally, 83% of data scientists indicate they are likely to use SHAPstories for this purpose. In image classification, CFstories are considered more or equally convincing as users own crafted stories by over 75% of lay user participants. CFstories also bring a tenfold speed gain in creating a narrative, and improves accuracy by over 20% compared to manually created narratives. The results thereby suggest that XAIstories may provide the missing link in truly explaining and understanding AI predictions.

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