Mind the gap: aligning AI with how restaurant staff think

Purpose This practitioner paper translates peer-reviewed research on restaurant worker mental models into actionable strategies for implementing artificial intelligence (AI)-driven reservation and personalization systems. The paper aims to address the critical disconnect between AI technology capabilities and worker acceptance, which causes 60–70% of hospitality technology implementations to fail despite technical sophistication. Design/methodology/approach Drawing from pathfinder network analysis of 25 restaurant workers’ cognitive structures (Iyer, Reynolds, Nam, & Jeong, 2024), this paper synthesizes findings with current AI hospitality literature to develop a human-centered implementation framework. The mental model alignment matrix evaluates AI features against workers' cognitive frameworks across operational flow, interpersonal dynamics and risk management dimensions. Findings Restaurant workers organize their understanding through three interconnected domains that determine technology acceptance. Workers evaluate AI systems based on their impact on the rhythm of service. Furthermore, they perceive technology that reduces human interaction as a threat to their core professional identity and incorporate these new systems into complex risk calculations that extend beyond efficiency to include reputational and service quality concerns. Practical implications Managers should position AI as intelligence amplifiers rather than automation tools, involve frontline workers in workflow mapping to identify non-disruptive integration points and design training that develops interpretation skills over operational compliance. Implementation requires iterative refinement with genuine worker influence over system evolution and clear accountability frameworks that preserve professional judgment. Originality/value This paper uniquely bridges cognitive psychology and hospitality technology implementation, providing the first framework that aligns AI deployment with worker mental models. The mental model alignment matrix (Borders, Klein, & Besuijen 2024) offers managers a practical tool for prioritizing features and sequencing implementation to maximize adoption success.