Faculty of Science and Information Technology > Industrial Model

Industrial Design in the AI Era: New Findings and Critical Gaps

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S. M. Monowar Kayser:
Industrial design is undergoing a structural transformation under the influence of artificial intelligence. The shift is not merely about automation or faster rendering. It reflects a deeper change in how design knowledge is generated, interpreted, and applied across the product development lifecycle. AI is no longer confined to downstream visualization tasks; it is increasingly involved in upstream activities such as problem framing, semantic translation of user needs, concept generation, and early evaluation.

Recent research highlights that AI is becoming an active participant in design cognition rather than a passive tool. A comprehensive review of AI applications in industrial design demonstrates that machine learning, large language models, and generative systems are now embedded across ideation, optimization, and decision-making processes. This integration is redefining how designers approach complexity and uncertainty in product development.
Source: https://www.sciencedirect.com/science/article/pii/S1877050925020587

One of the most significant findings is that AI can enhance not only the quantity but also the quality of design ideation when used within structured collaboration frameworks. A study published in AI EDAM shows that human and AI co-creation, particularly through customized GPT systems, leads to higher novelty and improved concept quality compared to traditional methods. This suggests that AI contributes meaningfully to creative reasoning rather than simply accelerating output generation.
Source: https://www.cambridge.org/core/journals/ai-edam/article/enhancing-designer-creativity-through-humanai-coideation-a-cocreation-framework-for-design-ideation-with-custom-gpt/BCC2CBE43EECE6F0D937BBC0D2F44868

Another important development is the emergence of integrated multimodal workflows. A recent study on automotive frontal form design demonstrates how AI can connect linguistic inputs, visual generation, geometric modeling, and evaluation into a unified process. This convergence indicates a transition from fragmented tool usage toward orchestrated design ecosystems where language, form, and perception are interconnected.
Source: https://www.sciencedirect.com/science/article/abs/pii/S147403462400483X

Equally notable is the progress toward manufacturability-aware generative design. Earlier AI systems often produced visually compelling but impractical forms. Recent work on deep generative design addresses this limitation by embedding constraints relevant to industrial processes such as injection molding and die casting. This development signals a shift from conceptual exploration toward production-ready design intelligence.
Source: https://arxiv.org/abs/2403.12098

Despite these advances, the field remains immature in several critical dimensions. A systematic review presented at NordDesign reveals that research on generative AI within product development is still limited, with only a small number of directly relevant studies identified. This indicates that current knowledge is fragmented and lacks comprehensive validation across real-world industrial contexts.
Source: https://www.designsociety.org/download-publication/47595/Evaluating%2Bthe%2BRole%2Bof%2BGenerative%2BAI%2Bin%2BProduct%2BDevelopment%2Band%2BDesign%2B-%2BA%2BSystematic%2BReview

From an expert perspective, the most critical gap lies in end-to-end integration. Most existing studies focus on isolated tasks such as ideation or visualization rather than examining complete workflows from initial research to manufacturing and market deployment. This fragmentation limits the practical impact of AI, as industrial design requires continuity across multiple interconnected stages.

Another major limitation is the lack of domain-specific grounding. While general-purpose AI systems demonstrate fluency and adaptability, they often lack deep knowledge of materials, manufacturing constraints, engineering standards, and regulatory requirements. This gap restricts their reliability in professional design environments where precision and feasibility are essential.
Multimodality also remains underdeveloped. Industrial design inherently involves the interaction of sketches, CAD models, material specifications, user data, and technical documentation. Current AI systems still struggle to operate seamlessly across these diverse formats, which reduces their effectiveness in real-world applications.

Trust and interpretability represent additional challenges. Designers and engineers require systems that produce consistent, explainable, and verifiable outputs. Current AI models often lack transparency, making it difficult to assess the validity of their recommendations. This limitation is particularly critical in safety-sensitive or highly regulated industries.
Intellectual property concerns further complicate adoption. Ongoing debates about data ownership, authorship, and the legality of training datasets create uncertainty around the use of AI-generated designs. For industrial designers, this directly affects issues such as design rights, brand identity, and competitive differentiation.
Source: https://academic.oup.com/policyandsociety/article/44/1/23/7606572

There is also a broader strategic misalignment between efficiency-driven Industry 4.0 models and the emerging human-centered and sustainability-focused vision of Industry 5.0. Recent research suggests that AI should not only enhance productivity but also support ethical, environmental, and user-centered design practices.
Source: https://www.mdpi.com/2227-9717/13/4/1174

In conclusion, AI is reshaping industrial design by expanding the scope of what designers can explore and evaluate. However, the transformation is uneven and incomplete. The most meaningful progress lies in human-AI collaboration, multimodal integration, and manufacturability-aware design. At the same time, significant gaps remain in workflow integration, domain knowledge, trust, and governance.

The future of industrial design will depend less on the raw capabilities of AI and more on how effectively these systems are embedded within coherent, reliable, and ethically grounded design processes. Designers who can navigate this complexity and orchestrate AI as part of a broader system will define the next phase of the discipline.



S. M. Monowar Kayser
Lecturer, Department of Multimedia & Creative Technology (MCT)
Faculty of Science & Information Technology
Daffodil International University (DIU)
Daffodil Smart City, Savar, Dhaka, Bangladesh
Visit: https://monowarkayser.com/

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