Artificial Intelligence for Human-in-the-Loop Decision Intelligence and Autonomous Operations in Quick-Service Restaurants

This research examines how artificial intelligence can support human-in-the-loop decision intelligence and bounded autonomous operations in quick-service restaurants (QSRs). The study proposes a QSR Decision Intelligence (QSR-DI) framework that connects restaurant operational data, AI-based prediction, operational constraints, automated recommendations, and human managerial oversight. A computational proof-of-concept is used to demonstrate how machine-learning predictions can be translated into operational actions such as maintaining normal workflow, queue intervention, crew reallocation, queue reprioritization, and manager or equipment escalation. The study emphasizes that effective restaurant automation should be evaluated not only by predictive accuracy, but also by the quality and appropriateness of the decisions produced from those predictions. McDonald’s digital transformation and restaurant automation initiatives are discussed as real-world motivation for the research. However, this study is independent and is not affiliated with, sponsored by, or endorsed by McDonald’s Corporation. The computational experiment does not use proprietary McDonald’s data. Synthetic restaurant-operation data are used solely for research demonstration and reproducibility. The study contributes to research at the intersection of artificial intelligence, decision intelligence, digital transformation, human-in-the-loop systems, machine learning, and operational automation.