Human-Centered AI Product Prototyping with No-Code AutoML: Conceptual
  Framework, Potentials and Limitations
Mario Truss Marc Schmitt
This paper evaluates No-Code AutoML as a solution for challenges in AI
product prototyping, characterized by unpredictability and inaccessibility to
non-experts, and proposes a conceptual framework. This complexity of AI
products hinders seamless execution and interdisciplinary collaboration crucial
for human-centered AI products. Relevant to industry and innovation, it affects
strategic decision-making and investment risk mitigation. Current approaches
provide limited insights into the potential and feasibility of AI product
ideas. Employing Design Science Research, the study identifies challenges and
integrates no-code AutoML as a solution by presenting a framework for AI
product prototyping with No-code AutoML. A case study confirms its potential in
supporting non-experts, offering a structured approach to AI product
development. The framework facilitates accessible and interpretable
prototyping, benefiting academia, managers, and decision-makers. Strategic
integration of no-code AutoML enhances efficiency, empowers non-experts, and
informs early-stage decisions, albeit with acknowledged limitations.