Investigating the manufacturer’s decision to invest in generative AI: role of emission-sensitivity and quality enhancements

Generative AI (GenAI) represents a paradigm shift in business innovation, enabling manufacturers to process complex datasets, generate insights, and develop generative designs that often exceed traditional methods’ capabilities. While GenAI enhances product quality, its adoption comes with significant environmental concerns. The intensive computational requirements for training GenAI models result in increased energy consumption, creating sustainability challenges that influence customer preferences towards GenAI-enabled products and manufacturer strategies regarding GenAI implementation. Our study investigates this critical tension: how manufacturers can harness GenAI’s quality-enhancing potential while navigating the risk of alienating environmentally conscious customers. Thus, we develop a game-theoretic model in which a manufacturer decides whether to invest in GenAI. Customers’ emission-sensitivity plays a pivotal role in shaping manufacturers’ strategies towards GenAI investments. The manufacturer only benefits from investing in GenAI when the customers’ emission sensitivity remains low. Further, when emission-sensitive customers constitute a small segment or the customers’ sensitivity towards product quality is low, GenAI also enhances consumer surplus (CS). However, when the proportion of such customers and customers’ sensitivity towards product quality are high, GenAI enhances CS when the customers’ emission-sensitivity is high. We also find that GenAI adoption can achieve a “win-win” outcome for the manufacturer and consumers.