Today, more and more systems are using AI to efficiently solve complex problems. While in many cases this increases the system’s performance and efficiency, developing such systems with AI functionality is a more difficult process due to the additional complexity. Thus, engineering practices are required to ensure the quality of the resulting software. Since the development of AI-based systems comes with new challenges, new engineering practices are needed for such development processes. Many practices have already been proposed for the development of AI-based systems. However, only a few practical experiences have been accumulated in applying these practices. This study aims to address this problem by collecting such experiences. Furthermore, our objective is to accumulate evidence of the effectiveness of these proposed practices. Additionally, we analyze challenges that occur during such a development process and provide solutions to overcome them. Lastly, we examine the tools proposed to develop AI-based systems. We aim to identify how helpful these tools are and how they affect the resulting system. We conducted a single case study in which we developed an autonomous stock trading system that uses machine learning functionality to invest in stocks. Before development, we conducted literature surveys to identify effective practices and useful tools for such an AI development process. During the development, we applied ten practices and seven tools. Using structured field notes, we documented the effects of these practices and tools. Furthermore, we used field notes to document challenges that occurred during the development and the solutions we applied to overcome them. After the development, we analyzed the collected field notes. We evaluated how the application of each practice and tool simplified the development and how it affected the software quality. Moreover, the experiences collected in applying these proposed practices and tools and the challenges encountered are compared with existing literature. Our experiences and the evidence we collected during this study can be used as advice to simplify the development of AI-based systems and to improve software quality.