  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.
