AI Assistance and Service Usage

As companies increasingly integrate artificial intelligence (AI) into their services, it is becoming crucial to understand AI's effect on service users. In this paper, we investigate how AI assistance impacts consumer service usage and how this impact varies according to users' experience levels. We address these questions through a collaboration with an international on-demand car-sharing platform that recently underwent a technology upgrade involving an AI-powered driver monitoring and feedback system. Combining theoretical insights with empirical analyses, we uncover a heterogeneous effect characterized by a U-shaped relationship: users with low or high experience levels increase their service usage due to AI assistance, while users with medium experience levels do not. Furthermore, we find that the motivations behind increased service usage differ between users with low versus high experience levels: low-experience users tend to benefit more from AI as they find greater marginal value in its feedback, while high-experience users tend to benefit more from AI likely due to their proficiency in internalizing AI's feedback and improving their behavior. On the other hand, medium-experience users are less likely to benefit from AI because they derive a lower marginal value from AI's feedback compared to their less experienced peers, and unlike their more experienced peers, they are less capable of enhancing the value of the AI by adaptively reacting to it. These results highlight the need for customized solutions to maximize the value that AI can create for different segments of users