AI Alchemy
Exploring opportunities for generative AI in new product development
F.P.E. Prevot (TU Delft - Industrial Design Engineering)
R.S.K. Chandrasegaran – Mentor (TU Delft - Creative Processes)
P.A. Lloyd – Graduation committee member (TU Delft - Creative Processes)
Ozlem Mandira – Mentor (Bugaboo International B.V.)
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Abstract
This thesis examines the integration of generative artificial intelligence (AI) into industrial design practice, using Bugaboo's new product development as a case study. It explores the opportunities for generative AI to augment new product development, how these opportunities can be scaled, and the insights gained from its practical application in real-world design processes. Grounded in theories such as bounded rationality and expandable rationality, the work introduces the Frame–Propose–Evaluate (FPE) simplified model of design, structuring design into iterative cycles of framing, proposing, and evaluating, each supported by uncertainty-driven actions: information, representation, and reflective action.
Emerging evidence suggests that generative AI can accelerate product development by expanding the problem–solution space and compressing iteration cycles. However, it also raises concerns about increasing design fixation, limiting originality, and blurring human–AI collaboration. Its application in live, design-led organizations remains largely unexplored, offering new opportunities to study integration into uncertainty-driven workflows.
Through an Action Design Research (ADR) approach, three bespoke generative AI tools were co-developed and embedded into live design projects: InsightGPT (supporting information action during framing), CreAIte (enhancing representation action during proposing), and RulesGPT (facilitating reflective action during evaluation).
The evaluation showed that integrating AI into the slower, uncertainty-driven phases around design improved the speed and richness of iteration cycles, acting as provocation engines without disrupting the intuitive rhythm of design. In the research sessions, designers spent less time on manual research and visualization and more on framing questions, refining prompts, interpreting outputs, and aligning stakeholders. Generative AI increased design agility, stimulated divergence, and accelerated iteration.
However, important limitations emerged, including decision fatigue from an overwhelming number of AI-generated options and a critical dependence on prompt design to ensure useful outputs. Human oversight remained indispensable for interpreting AI contributions and safeguarding the quality of design decisions. New literacies, particularly in prompt crafting and AI output validation, surfaced as essential competencies for effective use.
Importantly, while AI expanded the breadth of exploration, it did not resolve the core uncertainties inherent to design; instead, it amplified designers’ ability to act amidst ambiguity. This reinforces the view that uncertainty is not a flaw to be eliminated but a creative resource to be navigated.
From these insights, the thesis formulates ten guiding principles and proposes a strategic roadmap for AI adoption in design-led organizations. Ultimately, it advocates for a mode of critical augmentation, where AI reshapes the rhythm of design while preserving its creative, empathetic, and judgment-driven roots.