PreprintCited by 343Design Education and PracticeOpen Source Software InnovationsArtificial Intelligence Applications

The Crowdless Future? Generative AI and Creative Problem Solving

Open access
Boussioux, Leonard, N. Lane, Jacqueline, Zhang, Miaomiao, Jacimovic, Vladimir, Lakhani, Karim R.·2023·Organization Science

Abstract

The rapid advances in generative artificial intelligence (AI) open up attractive opportunities for creative problem-solving through human-guided AI partnerships. To explore this potential, we initiated a crowdsourcing challenge focused on sustainable, circular economy business ideas and assessed the novelty, value, and creativity of solutions created by both the human crowd and the collaborative efforts of one human and AI. The challenge attracted 125 global solvers from various industries and the human-prompted AI solutions were generated using strategic prompt engineering. We recruited 300 evaluators to judge a randomized selection of 13 out of 234 solutions, totaling 3,900 evaluator–solution pairs. Our findings demonstrate that the solutions generated through human-AI collaboration matched the creativity of those from the human solvers. Whereas the human-AI solutions provided more value, the human-only solutions were more innovative—both on average and for highly novel outcomes. Our study explores the potential for incorporating “AI-in-the-loop” into creative problem-solving, offering a scalable and cost-efficient method for enhancing the early phases of innovation. Our research paves the way for future exploration of how AI can be integrated into creative processes to foster more effective innovation.

Integrity signals

Registry

Crossref · 10.2139/ssrn.4533642

Cites

120 of 120 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Although several studies have compared AI-generated creativity against a human benchmark, a surprisingly limited amount of work has examined the creative output of human-AI dyads and the collaborative processes that undergird this.

    “Furthermore, although several studies have compared AI generated creativity against a human benchmark8,16, a surprisingly limited amount of work has examined the creative output of human-AI dyads and the collaborative processes that undergird this.”

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Boussioux, L., N. Lane, J., Zhang, M., Jacimovic, V., & Lakhani, K. R.
Affiliations
Harvard University (US)
Venue
Organization Science
Publisher
Elsevier BV
Published
2023
Volume
35
Issue
5
Pages
1589-1607
ISSN
1047-7039 (ISSN-L)
OpenAlex
W4401533174
Cited by
343
References
97
Citations by year
2026: 178 · 2025: 124 · 2024: 35 · 2023: 6
FWCI
45.14
Type
journal article
Language
en
Open access
Locations
  • Organization Science (publishedVersion)
  • SSRN Electronic Journal (acceptedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
Topics
  • Physical Sciences › Engineering › Mechanical Engineering › Design Education and Practice
  • Physical Sciences › Computer Science › Computer Science Applications › Open Source Software Innovations
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
generative AI, human-AI collaboration, creative problem solving, crowdsourcing, circular economy, business idea generation, prompt engineering, large language models, solution search, novelty, early-stage innovation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review