# Generative AI for Product Design: Getting the Right Design and the Design Right

arXiv: https://arxiv.org/abs/2306.01217 · HTML: https://mlsanity.com/paper/2306.01217/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2023.

## Built on by (most cited first, 6 of 6)

- [User Experience Design Professionals' Perceptions of Generative Artificial Intelligence](https://mlsanity.com/paper/2309.15237/) (2023)
- [A New Era in Human Factors Engineering: A Survey of the Applications and Prospects of Large Multimodal Models](https://mlsanity.com/paper/2405.13426/) (2024)
- [What's Next? Exploring Utilization, Challenges, and Future Directions of AI-Generated Image Tools in Graphic Design](https://mlsanity.com/paper/2406.13436/) (2024)
- [DeepArt: A Benchmark to Advance Fidelity Research in AI-Generated Content](https://mlsanity.com/paper/2312.10407/) (2023)
- ["She was useful, but a bit too optimistic": Augmenting Design with Interactive Virtual Personas](https://mlsanity.com/paper/2508.19463/) (2025)
- [Does AI Save Time on Product Design? A Randomized Controlled Experiment of AI Prompt-to-Design Workflows](https://mlsanity.com/paper/2609.26725/) (2026)

## Abstract (arXiv)

Generative AI (GenAI) models excel in their ability to recognize patterns in existing data and generate new and unexpected content. Recent advances have motivated applications of GenAI tools (e.g., Stable Diffusion, ChatGPT) to professional practice across industries, including product design. While these generative capabilities may seem enticing on the surface, certain barriers limit their practical application for real-world use in industry settings. In this position paper, we articulate and situate these barriers within two phases of the product design process, namely "getting the right design" and "getting the design right," and propose a research agenda to stimulate discussions around opportunities for realizing the full potential of GenAI tools in product design.