At present, China’s development has entered a period in which strategic opportunities and risk challenges coexist, with an increasing number of uncertain and unpredictable factors, which poses new challenges to supply-chain security and stability. Capturing the “bull’s nose” of artificial intelligence (AI) to form new quality productive forces can help empower enterprise development and enhance the resilience of industrial and supply chains. Based on the data of China’s A-share listed companies from 2007 to 2019 and industrial robot data released by the International Federation of Robotics, this paper constructs an enterprise supply-chain embedding index from the perspective of shared business correlations, and empirically tests the impact of AI applications on the supply-chain embedding of Chinese manufacturing enterprises. The results show that: First, AI applications significantly promote enterprises to be embedded in the supply-chain network, and this conclusion is still valid after a series of robustness tests. Second, mechanism testing shows that the introduction of AI helps enterprises control the cost of supply-chain embedding and improve the degree of specialization and division of labor, and then promote more enterprises to be embedded in supply-chain cooperation through these two channels. Third, further study finds that under the influence of uncertain risks, the promoting effect of AI applications on enterprise supply-chain embedding has obvious differences in different dimensions. Among them, from the enterprise dimension, the supply-chain embedding effect of AI is more obvious in enterprises with lower resource advantages, in the non-start-up period, and with stronger perception of uncertainty. From the industry dimension, this effect is more prominent in enterprises with a higher degree of industry competition and located downstream of the industry chain. Considering the practical factors of the supply chain, the supply-chain embedding effect of AI is more effective in enterprises with a longer geographical distance of the supply chain and a higher degree of supply-chain dependence. This paper reveals the positive impact of AI applications on the establishment of supply-demand connections between enterprises and the heterogeneity bias under risk factors, and provides policy inspiration for further optimizing supply-chain configuration and enhancing supply-chain resilience.