When Generative AI Meets Product Development

From ideation to user testing, large language models are allowing companies to explore more ideas and iterate faster.

- Tucker J. Marion, Mahdi Srour, and Frank Piller

As enterprises experiment with generative AI use cases, one promising area is emerging: incorporating image- and text-generation tools in the product development process. Generative AI is being used to enhance ideation and creativity, gain market and customer insights, and add user-friendly interfaces to sophisticated tools.

In our field research and interviews with managers, we have seen how GenAl can transform traditional innovation workflows. The three use cases described below show how these technologies can increase the productivity of innovation teams.

Use Case 1: Enhancing Creativity and Design Workflows

For a project last year, Boston design agency Loft used GPT-4 to suggest new product features by prompting it with known customer preferences. It then identified and refined the most promising ideas via additional prompts. Meanwhile, the designers began sketching product concepts and then uploaded the sketches into image generator Midjourney, where they could refine the visual designs with prompts in addition to reworking them on paper. In these creative stages of the innovation process, generative Al's tendency to produce hallucinations - text or images that defy facts or logic were of no concern as the team was just looking for ideas.

When the development process moves into design and engineering, tools must be trusted to produce reliable outputs. Publicly available generative AI platforms could have helped the Loft team conceptualize ideas and sketch early prototypes, but the company paused its use of generative AI tools at this stage while its engineers built prototypes based on the selected concepts.