Human-AI Collaboration within Industrial Design: The Argyle Design Framework

Abstract

Generative Artificial Intelligence (AI) is increasingly influencing industrial design workflows, becoming a catalyst for creativity and efficiency. This paper explores how generative AI tools amplify the creative potential of designers and streamline the process from early conception to prototyping. We study the design workflow through the lens of the Double Diamond Framework (a traditional design process model), evaluating the effectiveness of AI at each stage and identifying challenges in managing the large volume of ideas generated by AI. To address these challenges, we propose an alternative Argyle Design Framework that integrates iterative divergence and convergence cycles to better align with AI-driven workflows. The main findings of our research propose that AI tools can significantly expand research conceptual exploration and enhance design efficiency (e.g., shorter design cycles and higher productivity (Surrao, 2024)). However, without a structured process, the vast output of AI can overwhelm designers, highlighting the necessity for human-guided convergence. The Argyle Design Framework aims to leverage the advantages of AI—high output and rapid iteration—while introducing systematic filtering and refinement. We propose that using the Argyle Design Framework’s iterative approach can enhance creative outcomes, make workflows more manageable, and provide direction for effectively integrating generative AI into product design practices.

Keywords: Generative AI, Human-AI Collaboration, Industrial Design, Argyle Design Framework, Double Diamond Model

DOI: 10.54941/ahfe1006425