This chapter explores the application of artificial intelligence (AI) in optimizing manufacturing processes, with a focus on the slow adoption of such technologies in small and medium-scale industries. Specifically, it examines the use of reinforcement learning (RL) to control pump speed in a water bottling plant, a critical operation that influences overall plant efficiency. Smart manufacturing, leveraging technologies such as sensor-integrated systems, digital twins, and AI, promises significant improvements in real-time monitoring and decision-making. However, the widespread adoption of these technologies has been hindered by barriers such as high costs and a lack of technical skills, particularly in smaller enterprises. This chapter highlights these challenges and proposes the application of RL, a subcategory of machine learning, to address them. RL was chosen due to its unique capability to optimize operations by learning through trial-and-error interactions within dynamic environments, offering a viable solution to the stochastic nature of plant operations. The chapter provides an in-depth analysis of the water bottling plant's operations, identifying current limitations and setting the stage for the implementation of AI-driven optimization. A detailed methodology of the experimental setup is presented, followed by a discussion of the results achieved. The findings demonstrate the effectiveness of RL in optimizing pump speed, leading to improved efficiency and better decision-making within the plant. In conclusion, the chapter highlights the potential of AI, particularly RL, to bring about meaningful advancements in smart manufacturing, offering a pathway for smaller industries to overcome technological adoption challenges.