Innovating with Generative AI: A Human Bottleneck Framework
We propose a human bottleneck perspective for understanding how generative AI transforms the innovation process. The central premise is that many constraints traditionally plaguing the innovation process are cognitive and social in origin, rooted in how people generate ideas, evaluate novelty, and communicate through social systems. Generative AI does not act uniformly on these constraints. At each stage, it can deepen some bottlenecks while alleviating others, and predicting these outcomes requires understanding the underlying mechanisms of the constraint itself. We identify bottlenecks in four stages of the innovation process: ideation, screening and testing, preference measurement and ...
| Authors | Julian De Freitas, Ayelet Israeli, Gideon Nave et al. |
|---|---|
| Primary category | cs.HC |
| Published | 23 June 2026 |
| Last updated at source | 23 June 2026 |
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