As artificial intelligence continues to permeate working life, the integration of AI in truck UX design is gaining prominence. While the majority of AI research, especially in the field of Explainable AI (XAI), is rooted in a technical perspective, this work explores and unpacks the user perspective by addressing the research question: “How do UX designers of truck HCI systems perceive AI explanations in an AI-powered data analytics platform?”. To address this question, a prototype of such a platform was co-designed and evaluated by 17 experts in truck UX design. Findings highlight that for AI explanations to be perceived as useful, they need to be understandable, contextually relevant, and verifiable, with the ability to dynamically adapt to users’ evolving knowledge and objectives. These findings extend prior research by emphasizing the importance of contextual and human-centered values in designing and developing AI-enabled systems with explainability, and by calling for future transdisciplinary collaboration to address evolving contextual user needs in truck UX design and beyond.