This paper aims to support decision-making in lean management and increase the interpretability of Artificial Intelligence (AI)-generated recommendations in business operations. Existing explainable AI approaches provide useful guidance on which features should change, but they pay limited attention to the operational cost of implementing those changes and therefore do not adequately address the lean management trade-off between customer value enhancement and resource use. To address this problem, the study integrates sentiment analysis, topic modelling, and a Free Disposal Hull-based counterfactual model within a nonparametric production framework, in which customer satisfaction is treated as the service outcome and operational expenditures are treated as inputs. The framework shows that lean management in the service industry can be considered through the counterfactual analysis of AI-generated outcomes while considering three types of managerially relevant recommendations: the identification of critical service attributes for improvement, the specification of feasible adjustment targets, and the estimation of the minimum cost required to attain the desired performance level. Using the hotel service setting as an illustrative case, the study contributes a lean-oriented and managerially interpretable approach for linking explainable AI with operational decision making under cost-conscious service improvement.