AI Advertising-to-Service Inference: When AI Disclosure Triggers Inferences from Ad Production to Process Expectation

As artificial intelligence (AI) disclosure becomes platform-integrated, companies increasingly face “AI-generated” labels in advertising. Prior research has focused on ad-level responses, leaving open whether such cues transfer across touchpoints to shape expectations about the advertised offering. This research proposes an ad-to-service transfer mechanism whereby AI disclosure triggers two countervailing inferences: AI Advertising-to-Service Inference (AISI), a process inference reflecting the belief that firms using AI in advertising also deploy AI in service delivery, and Perceived Brand AI Expertise (BAIE), a capability inference reflecting the belief that firms possess AI-related knowledge, experience, and technical infrastructure to deploy AI effectively across business operations. In two experiments in service contexts, AI disclosure reduced expected service quality and, in Study 1, purchase intention (PI). Mediation analyses reveal a negative indirect pathway through AISI, whereas a positive, offsetting effect emerges via BAIE. Study 2 shows how AISI affects consumer evaluations when website information confirms or violates the expected AI integration in service delivery. These findings reconcile mixed AI disclosure effects as competing process- and capability-based inferences. Under mandatory AI labeling, firms should pair AI-generated advertising with credible signals of BAIE and clarify the role of AI in service delivery; otherwise, lower quality expectations and PI may outweigh production-cost savings.