Retailers and service firms use scan-and-go systems, autonomous stores, conversational agents, voice assistants, service robots and AI-supported recommendations at different points in the customer journey. Research explains many drivers of technology acceptance, but commercial value also depends on what happens after the first trial. This conceptual paper integrates seminal work on technology acceptance, innovation diffusion, expectation-confirmation, perceived value, habit, customer satisfaction and willingness to pay with recent retail and service research published from 2023 to 2025. The synthesis develops a five-stage customer-value architecture covering adoption readiness, assisted trial, value confirmation, routine formation and commercial conversion. Recent evidence strengthens four parts of the architecture. Familiarity can shape acceptance and disclosure in AI-mediated product search; automation, personalisation, efficiency and precision can improve engagement with service robots; empathy can support social presence and satisfaction but may weaken the experience under time pressure; and customer responses differ according to the level and location of in-store automation. The framework also identifies feedback through service recovery, social proof and cumulative satisfaction. Its main contribution is the separation of technology acceptance, usage value and payment value. A customer may accept and repeatedly use an AI-enabled service without choosing a paid option when familiar alternatives remain available or the value difference is unclear. The paper provides a source-based structure for commerce and service managers to assess AI-enabled customer journeys without treating intention as a commercial outcome.