This paper constructs a strategic framework based on the AISAS consumer decision-making model to compare the application of traditional tool-based AI and Agentic AI marketing. The framework identifies the critical psychological drivers in five stages of consumer decision-making process, respectively. attention (visual saliency and personalization), interest (emotional resonance and curiosity stimulation), search (information sufficiency and credibility), action (decision confidence and risk reduction), and share (social bonding and self-expression). Within this framework, traditional AI primarily fulfills mechanical tasks, such as data processing and standardized content creation, but struggles to respond adaptively to situational shifts and consumer emotions. In contrast, Agentic AI, characterized by autonomous goal-setting, adaptive learning, and empathic responsiveness, effectively addresses complex psychological consumer needs across the AISAS journey. The authors further propose the concept of "PsychologicalTechnological Fit," recommending that marketers prioritize the implementation of Agentic AI at Interest, Search, and Action stages, where psychological needs are highest. Additionally, companies should enhance AI marketing effectiveness by emphasizing transparency, emotional interaction design, and cross-functional collaboration.
Renjie Quan (Tue,) studied this question.