The Crowdless Future? Generative AI and Creative Problem-Solving

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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. Funding: This work was supported by Harvard Business School (Division of Research and Faculty Development) and the Laboratory for Innovation Science at Harvard (LISH) at the Digital Data and Design (D3) Institute at Harvard. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2023.18430 .

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