This paper addresses the complexities inherent in AI product prototyping, focusing on the challenges posed by the probabilistic nature of AI behavior and the limited accessibility of prototyping tools to non-experts. A Design Science Research (DSR) approach is presented which culminates in a conceptual framework aimed at improving the AI prototyping process. Through a comprehensive literature review, key challenges were identified and no-code AutoML was analyzed as a solution. The framework describes the seamless incorporation of non-expert input and evaluation during prototyping, leveraging the potential of no-code AutoML to enhance accessibility and interpretability. A hybrid approach of combining naturalistic (case study) and artificial evaluation methods (criteria-based analysis) validated the utility of our approach, highlighting its efficacy in supporting AI non-experts and streamlining decision-making and its limitations. Implications for academia and industry, emphasizing the strategic integration of no-code AutoML to enhance AI product development processes, mitigate risks, and foster innovation, are discussed.

翻译：本文探讨了AI产品原型设计中固有的复杂性，重点关注AI行为的概率性特征以及原型设计工具对非专业人士可及性有限所带来的挑战。研究提出一种设计科学研究方法，并最终构建了一个旨在改进AI原型设计流程的概念框架。通过全面的文献综述，识别了关键挑战，并分析了无代码AutoML作为解决方案的潜力。该框架描述了在原型设计过程中无缝整合非专业用户输入与评估的机制，利用无代码AutoML提升可访问性与可解释性。通过结合自然主义方法（案例研究）与人工评估方法（基于标准的分析）的混合验证策略，证实了本方法的实用性，凸显了其在支持AI非专业人士、优化决策流程方面的效能及其局限性。研究进一步探讨了对学术界与工业界的启示，强调通过战略性地整合无代码AutoML来增强AI产品开发流程、降低风险并促进创新。