Integrating Generative Artificial Intelligence and Humans Under Uncertainty

This study explores how uncertainty in Generative Artificial Intelligence (Gen-AI) performance and market conditions affects strategic decision-making in AI-human collaboration using a real options framework. Four strategies are modelled: human only, AI-exclusive use, task distribution between humans and AI, and the Human-in-the-loop approach. The research shows that higher chances of AI success encourage earlier adoption of AI-inclusive strategies, while greater market uncertainty delays transitions to more AI-driven approaches and highlights the need for human involvement. The findings suggest that fully relying on AI is suboptimal, with the Human-in-the-loop strategy offering the most benefits. This study provides a dynamic AI adoption model and offers valuable managerial insights for optimizing AI-human collaboration under uncertainty.