Individual or group donations form an important aspect of disaster relief operations. Donation-based crowdfunding (DBC) tasks are often listed on crowdfunding platforms to attract donors to donate for a specific reason in a stipulated time. As the frequency and intensity of disasters has increased over time, these platforms have gained in popularity, and they need a constant and consistent flow of funds to achieve their targets. Artificial intelligence (AI) tools are often adopted by these channels to enhance their operational performance. We understand the process of adoption through uses and gratification theory, which is dominated by motivational factors, such as the utilitarian and symbolic benefits which DBC intends to achieve. The inflow of cash from multiple donors across the world, guided by AI tools, also gives rise to risks; therefore, we have used a moderating variable to better understand the operational performance of DBC. We collected empirical data through 293 responses from owners of DBC tasks in the context of disaster relief operations. We tested our hypotheses using partial least square structured equation modelling and controlled for intensity of disaster and crowdfunding task duration. Our results offer a significant extension to uses and gratification theory by understanding a positive relation between uses and gratification benefits and the adoption of AI tools for boosting operational performance.