FROM DOUBLE DIAMOND TO ARGYLE: RETHINKING DESIGN METHODOLOGIES IN THE AGE OF GENERATIVE AI
Since the widespread adoption of generative AI tools in 2022, industrial design educators and practitioners have been exploring how these technologies might reshape established design processes. One common reference framework is theĀ Double DiamondĀ model developed by the UK Design Council, which organizes the design process into four distinct phases:Ā Discover,Ā Define,Ā Develop, andĀ Deliver, each alternating between divergent and convergent thinking.
Initially, I hypothesized that generative AI could be overlaid onto this model as a set of supportive tools, augmenting specific tasks within each of the four phases. For example, AI might assist in synthesizing user research during the Discover phase or generating visual concepts in the Develop phase. However, as I continued exploring this relationship, it became clear that the influence of AI is more profound and structural than originally assumed.
ENLARGING THE VENN DIAGRAM OF INNOVATION
Generative AI doesnāt merely enhance discrete tasks; it expands theĀ overlap spaceĀ between traditionally siloed disciplines such asĀ design,Ā engineering, andĀ marketing. In practice, this means that generative AI allows designers to prototype engineering solutions earlier, integrate market constraints sooner, and consider user experiences more holistically. The resulting overlap is no longer a narrow āsweet spot,ā but a broader and more dynamic field of interdisciplinary creativity.
FROM DIAMONDS TO ARGYLE: A MORE GRANULAR MODEL
This expansion of possibility space prompted a reevaluation of the Double Diamond itself. Rather than viewing each of the four phases as singular diverge-converge events, we might instead conceptualize them as containingĀ multiple micro-cyclesĀ of exploration and decision-making. This shift suggests a new metaphor: anĀ Argyle pattern, where smaller diamonds are woven into each phase, reflecting the granular, iterative, and non-linear nature of working with generative AI.
This Argyle model is not just a visualization upgradeāit represents a pedagogical shift. Teaching students to work with AI requires them toĀ continuously synthesize, reflect, and recalibrateānot just at the conclusion of a phase, but throughout it. AI introduces a wealth of possibilities, and without structured moments of interpretation, synthesis, and curation, students risk being overwhelmed or misled by the output.
IMPLICATIONS FOR DESIGN EDUCATION
As educators, our role is not to merely train students in the use of AI tools, but to help themĀ critically navigateĀ the opportunities and limitations AI presents. Embracing an Argyle-like methodology encourages designers to maintainĀ creative agencyĀ while embracing the complexity AI introduces. It also encourages more interdisciplinary thinking, systems-level problem-solving, and a reflective approach to iteration.
In sum, integrating generative AI into the design process should not be viewed as a bolt-on enhancement to existing methods. Rather, it challenges us toĀ restructure our models,Ā rethink our pedagogy, andĀ reimagine the role of the designerĀ in a rapidly evolving technological landscape.
