Cost estimation in the early phases of product development remains challenging due to high uncertainty, limited data, and a strong reliance on expert knowledge. To address these challenges, this paper presents and evaluates a proof-of-concept study for accurate and explainable cost estimation using artificial intelligence (AI) to support decision-making in product development. The proposed approach applies machine learning to generate cost estimates and employs methods from the field of explainable AI to identify cost drivers and quantify their influence on the estimated costs. In addition, a large language model is used to suggest product design improvements to reduce costs. The evaluation shows that the proposed approach can outperform both expert-based cost estimates and rule-based cost estimates generated by software tools in terms of accuracy and speed, while also improving explainability. Overall, the paper lays the groundwork for integrating accurate and explainable AI-assisted cost estimation into decision-making in product development.