“This study investigates the capability of generative artificial intelligence (AI) in creating innovative business solutions compared to human crowdsourcing methods. We initiated a crowdsourcing challenge focused on sustainable, circular economy business opportunities. The challenge attracted a diverse range of solvers from a myriad of countries and industries. Simultaneously, we employed GPT-4 to generate AI solutions using three different prompt levels, each calibrated to simulate distinct human crowd and expert personas. 145 evaluators assessed a randomized selection of 10 out of 234 human and AI solutions, a total of 1,885 evaluator-solution pairs. Results showed comparable quality between human and AI-generated solutions. However, human ideas were perceived as more novel, whereas AI solutions delivered better environmental and financial value. We use natural language processing techniques on the rich solution text to show that although human solvers and GPT-4 cover a similar range of industries of application, human solutions exhibit greater semantic diversity. The connection between semantic diversity and novelty is stronger in human solutions, suggesting differences in how novelty is created by humans and AI or detected by human evaluators. This study illuminates the potential and limitations of both human and AI crowdsourcing to solve complex organizational problems and sets the groundwork for a possible integrative human-AI approach to problem-solving.”