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<p>The bottleneck in developing key components for new products often lies in lengthy research and development (R&amp;D) cycles. To address this issue, manufacturers increasingly collaborate with artificial intelligence (AI) service providers to expedite R&amp;D processes, facilitating earlier market releases of new products. This study examines how two competing manufacturers strategically deploy AI services to accelerate new product launches. Compared to existing (old) products, new products offer superior quality but incur higher production costs and are constrained by limited key component capacities. We characterize the equilibrium AI technology adoption strategy for the competitive manufacturers. Our findings indicate that when the capacity for new products is limited, the constrained capacity mitigates competitive intensity, allowing both manufacturers to enhance their profits by utilizing AI technologies to aid R&amp;D. Conversely, as capacity increases, competition intensifies, potentially leading to a prisoner’s dilemma scenario where the AI deployment may not yield mutual benefits. We further reveal that the AI service provider can strategically serve a single manufacturer to mitigate inter-manufacturer competition and intra-manufacturer product cannibalization. Specifically, when the new product capacity is constrained, the limited supply can naturally curb competition. In such scenarios, exclusive service to one manufacturer can alleviate the substitution effect between the old and new products. With ample new product capacity, exclusive service can not only mitigate the substitution effect but also reduce inter-manufacturer rivalry. In addition, we analyze the impact of AI-assisted R&amp;D on supply chain efficiency. Our result indicates that, due to competition, an increase in the number of manufacturers adopting AI technology does not necessarily enhance supply chain profitability. Finally, we relax some assumptions of the base model to demonstrate the robustness of our conclusions.</p>

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Technology innovation of key components under competitive markets with an AI service supplier

  • 期刊论文

  • 发表日期：

    2025年10月30日

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  • Beibei Shang,
  • Jiajia Nie,
  • Qiang Guo,
  • Liang Li

摘要

The bottleneck in developing key components for new products often lies in lengthy research and development (R&D) cycles. To address this issue, manufacturers increasingly collaborate with artificial intelligence (AI) service providers to expedite R&D processes, facilitating earlier market releases of new products. This study examines how two competing manufacturers strategically deploy AI services to accelerate new product launches. Compared to existing (old) products, new products offer superior quality but incur higher production costs and are constrained by limited key component capacities. We characterize the equilibrium AI technology adoption strategy for the competitive manufacturers. Our findings indicate that when the capacity for new products is limited, the constrained capacity mitigates competitive intensity, allowing both manufacturers to enhance their profits by utilizing AI technologies to aid R&D. Conversely, as capacity increases, competition intensifies, potentially leading to a prisoner’s dilemma scenario where the AI deployment may not yield mutual benefits. We further reveal that the AI service provider can strategically serve a single manufacturer to mitigate inter-manufacturer competition and intra-manufacturer product cannibalization. Specifically, when the new product capacity is constrained, the limited supply can naturally curb competition. In such scenarios, exclusive service to one manufacturer can alleviate the substitution effect between the old and new products. With ample new product capacity, exclusive service can not only mitigate the substitution effect but also reduce inter-manufacturer rivalry. In addition, we analyze the impact of AI-assisted R&D on supply chain efficiency. Our result indicates that, due to competition, an increase in the number of manufacturers adopting AI technology does not necessarily enhance supply chain profitability. Finally, we relax some assumptions of the base model to demonstrate the robustness of our conclusions.