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Generative AI for Bioinspired Product Ideation

Generative AI for Bioinspired Product Ideation | Innovation56K

By Tojin T. Eapen

The design of products, processes, and organizations guided by principles observed in living systems can be referred to as "Bioinspired System Design." In a series of posts, we delve into the potential of generative artificial intelligence (AI) to generate bioinspired product design concepts as a part of the idea management process. Specifically, we will look at how living organisms can serve as inspiration to redesign common products and human artifacts including bags, cars, bags, pens, tanks, trains, and umbrellas. In each of these articles, we will examine how the unique characteristics and behaviors of a particular living organism can be incorporated into the design of the bioinspired product.

In the above cases, the use of generative AI for product concept creation allows us to start with a description of a generic product concept and then use it to quickly generate of many potentially novel designs. 

Generative AI for Bioinspired Product Ideation | Innovation56K

These designs could then be evaluated and refined to select the most promising concepts to move to the next stage of the product development process. 

Generative AI for Bioinspired Product Ideation

The success of this approach depends on the quality of the prompts, the text-to-image employed, and the number of iterations. For some products, many of the ideas we generated were "usable" and potentially moved further along the new product development process. In other cases, many raw concepts had to be generated before a promising concept was obtained during the design generation phase.

Function Follows Form in AI-Enabled Product Ideation

Atypical designs generated by generative AI models that challenge the status quo can be valuable inputs during the early stages of new product development. These designs can inspire designers to think beyond their preconceptions of what is possible or desirable in a product, both in terms of form and function. An unusual or atypical form can encourage designers to consider the potential benefits of this novel design. This approach, which can be referred to as "function follows form," can help overcome biases such as design fixationfunctional fixedness, and the Einstellung effect, where previous experience can prevent new ideas from emerging. This can lead to creative solutions that may not have been discovered using a more traditional "form follows function" approach, where the functions are determined first, and the form is designed to accommodate them.

Reverse Ideation

In the realm of product design, the "function follows form" approach to idea generation allows for intentional vagueness in the early stages of the ideation process, a method we refer to as reverse ideation. The objective is to use generated images with vague functional features as a catalyst for further ideation. For instance, specifying that we desire the image of a "product" or "toy" without providing additional specifications can result in a plethora of design concepts, some of which may lack clarity in terms of function. The crucial aspect of this approach is inferring the functionality of the product from the abstract designs produced by text-to-image models. 

Generating a multitude of designs can stimulate creative thinking and the exploration of new possibilities. These designs can also serve as a valuable tool in market research or focus group exercises, yielding new perspectives on potential products. For an example of reverse ideation, see Crabbatoy: Reverse Ideation for Crab Inspired Toys.

Generative AI Business (GAIB) Models

Using a generative approach in AI allows for the creation of new, unique products or designs quickly with a reduced need for human input. This can be particularly useful in the context of businesses, as it allows for the rapid generation of a wide range of products, which can be quickly brought to market. We refer to such business models as Generative AI Businesses (GAIBs). An example of a GAIB would be a clothing company that uses generative AI to create new designs for t-shirts. These designs could then be quickly prototyped and brought to market, allowing the company to stay ahead of trends and offer a constantly-changing selection of products to customers. 

Another model for using Generative AI in Business (GAIB) would involve customers utilizing generative AI tools to generate design concepts for products. These concepts, which may be created using text-to-image models, allow users with novel ideas but without expertise in visualization or computer-aided design (CAD) tools to easily create visual representations of their ideas.

Once generated, these concepts can then be submitted to a business, who would then create a personalized version of the product based on the customer's design. Alternatively, user-submitted designs may be posted on a crowdsourcing platform, where they can be evaluated by the community and top designs selected for further development.

Democratizing Innovation

By using generative AI, businesses can quickly and easily generate a large number of design options, which can help them to uncover new and innovative designs that they may not have considered otherwise. This can help businesses to stay competitive and find new ways to differentiate themselves in the marketplace.

This approach of using generative AI also plays a role in "democratizing innovation" by giving individuals with unique ideas, but without specialized design skills, the ability to easily create visual representations of their ideas and have them considered by businesses or the public. It also allows businesses to tap into a wider pool of ideas and potentially uncover new and innovative designs that they may not have considered otherwise.

Crowdsourcing coupled with the capabilities of generative AI technology makes it possible for individual users with unique and innovative ideas, who may not have access to the resources or knowledge needed to create professional-looking designs, to have their ideas considered by businesses. This can also lead to the improvement of ideas in crowdsourcing challenges, which have been criticized for leading to mostly poor-quality ideas. This can help to level the playing field and give more people the opportunity to contribute to the innovation process.

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