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Integrating Generative Artificial Intelligence and Human Interaction In R&d Management

This study examines the collaboration strategies between Generative Artificial Intelligence (Gen-AI) and humans within Research and Development (R&D) in a real option setting. Four strategies are modelled: exclusive use of Gen-AI or humans, task allocation between both, and enhancing Gen-AI via the “Human in the Loop” approach. Despite lower Gen-AI costs, complete automation proves economically unviable when the market demand is low. I show that increased market uncertainty delays the adoption of different strategies. However, once the Gen-AI production model is introduced, the increase in uncertainty tends to stimulate the magnitude of its adoption. This study provides policy implications for policymakers and managers, shedding light on the need for collaborative policies and adaptive strategies in the contemporary labour market, especially in the context of innovation management under uncertainty.