Innovating with Generative AI: A Human Bottleneck Framework

Julian De Freitas, Ayelet Israeli, Gideon Nave, Artem Timoshenko and Olivier Toubia

Papers from arXiv.org

Abstract: 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 consumer insight, diffusion, and market learning. By grounding analysis in human behavior rather than rapidly changing AI capabilities, we offer a framework for assessing whether new developments alleviate or intensify the bottlenecks that matter most at each stage. We also distinguish bottlenecks likely to narrow as capabilities improve from those rooted in enduring human constraints. We further discuss AI-related issues that cut across the entire innovation pipeline, challenging the very existence and structure of the traditional innovation process.

Date: 2026-06
References: Add references at CitEc
Citations:

Downloads: (external link)
https://arxiv.org/pdf/2608.07504 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2608.07504

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().