Teams may produce low-fidelity 'AI slop' by using simple prompts, which is ineffective for user research.
Product teams might fail to adapt from linear 'assembly line' workflows to the more collaborative 'jazz band' model required for AI-native development.
Without providing sufficient multi-dimensional context, AI prototyping tools will fail to generate high-quality, iterable outputs.
Opportunities Identified
Dramatically accelerate product development cycles by generating high-fidelity prototypes in hours instead of weeks.
Improve the quality of user feedback by testing realistic, data-rich prototypes that create a 'suspension of disbelief'.
Enable more robust product exploration by making it cheap and fast to create and discard multiple design variations.
Increase development efficiency by creating modular prototypes where data (JSON) is separate from the UI, allowing for easy content swapping.