Designing for Trust, Progress, and Dignity: A Conceptual Framework for Reliability, Responsiveness, and Relational Quality in AI-Enabled Service Systems

AI is now embedded in frontline service at scale, yet the design frameworks managers reach for were built around human agents and do not translate cleanly to systems that generate rather than retrieve, that automate rather than augment. This paper argues that three design challenges sit at the heart of the problem, though they are rarely treated as a connected set. Generative AI can produce fluent, confident outputs that are simply wrong, which is a qualitatively different kind of reliability failure from anything SERVQUAL was designed to address. AI can reply instantly while leaving the customer no closer to resolution, exposing a gap between speed and what we might call felt responsiveness. And it faces an awkward relational tension. Overclaiming warmth triggers distrust, yet there are genuine service contexts in which the non-human nature of the system is a feature rather than a liability. The RRR Design Framework developed here extends established service quality dimensions to the AI context, organising fifteen prescriptive design principles around reliability, responsiveness, and relational quality, each reconceptualised for AI-mediated service. The principles follow a prevent-and-recover logic within each dimension and are tied together by a single strategic proposition, which is to automate to protect relationships. Four empirically testable propositions are derived from the framework, each operationalised with measurable constructs, moderating conditions, and falsifiable null cases. The framework is most applicable to hybrid human-AI frontline systems where customers are actively working toward a resolution.