Strategic deployment and adoption for AI streamer on live commerce platforms

The integration of AI streamers into live commerce platforms represents a transformative shift in product promotion and consumer engagement strategies. This paper develops a sequential game-theoretic model to examine the strategic interactions between platforms and sellers. With the platform acting as the Stackelberg leader determining AI deployment and technological investment, and the seller following with streamer adoption and pricing decisions, we analyze how outcomes are shaped by commission rates, consumer schedule flexibility, and baseline trust in AI. Our results show that the seller's profit under AI streaming follows an inverted U pattern with respect to the commission rate. Furthermore, consumer flexibility and a non-monotonic trust effect jointly govern the strategic alignment between both parties. Human streaming is optimal when the flexible segment is large, whereas a mutually beneficial agreement on AI adoption emerges only when time-constrained consumers prevail and baseline trust is either sufficiently low or sufficiently high. Under moderate trust, the equilibrium defaults to traditional human streaming. This research provides a structured analytical framework to guide platforms and sellers in aligning their operational strategies within the evolving live commerce ecosystem.