The Crowdless Future? Generative AI and Creative Problem-Solving

Léonard Boussioux (), Jacqueline N. Lane (), Miaomiao Zhang (), Vladimir Jacimovic () and Karim R. Lakhani ()
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Léonard Boussioux: Michael G. Foster School of Business, University of Washington, Seattle, Washington 98195
Jacqueline N. Lane: Harvard Business School, Boston, Massachusetts 02163
Miaomiao Zhang: ContinuumLab.AI, San Francisco, California 94114
Vladimir Jacimovic: Harvard Business School, Boston, Massachusetts 02163; ContinuumLab.AI, San Francisco, California 94114
Karim R. Lakhani: Harvard Business School, Boston, Massachusetts 02163

Organization Science, 2024, vol. 35, issue 5, 1589-1607

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 generated by the human crowd (HC) and collaborative human-AI efforts using two alternative forms of solution search. The challenge attracted 125 global solvers from various industries, and we used strategic prompt engineering to generate the human-AI solutions. We recruited 300 external human evaluators to judge a randomized selection of 13 out of 234 solutions, totaling 3,900 evaluator-solution pairs. Our results indicate that while human crowd solutions exhibited higher novelty—both on average and for highly novel outcomes—human-AI solutions demonstrated superior strategic viability, financial and environmental value, and overall quality. Notably, human-AI solutions cocreated through differentiated search, where human-guided prompts instructed the large language model to sequentially generate outputs distinct from previous iterations, outperformed solutions generated through independent search. By incorporating “AI in the loop” into human-centered creative problem-solving, our study demonstrates a scalable, cost-effective approach to augment the early innovation phases and lays the groundwork for investigating how integrating human-AI solution search processes can drive more impactful innovations.

Keywords: generative AI; large language models; creative problem-solving; organizational search; AI-in-the-loop; crowdsourcing; prompt engineering (search for similar items in EconPapers)
Date: 2024
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