Explainability has become a crucial aspect of AI-based systems, particularly in safety-critical and regulated domains. Researchers have proposed various frameworks and approaches to support explainability, but there is a lack of empirical understanding of how existing Requirements Engineering (RE) practices address explainability requirements throughout the RE lifecycle. A recent study evaluated RE practices at Daimler Truck, synthesizing insights into a proposed Explainable RE Framework. This framework aims to bridge the gap between RE practices and explainability requirements, providing a foundation for developing more transparent and trustworthy AI systems. The study's findings highlight the need for a more nuanced understanding of explainability in RE, emphasizing the importance of integrating explainability into existing RE practices. So what matters to practitioners is that this proposed framework can inform the development of more explainable AI systems, ultimately enhancing their reliability and adoption in safety-critical domains1.