Digital transformation in the Water and Wastewater industries has become an important step into the future as plants become integrated into Smart Cities to support the sustainable use of water. Smart Water networks, plants, and equipment are designed to address these challenges by increasing water and energy efficiency, while building infrastructure resilience. Understandably, utilities across the country are at various stages as they continue to progress in their Smart Water journey. As utilities evolve along the Smart Water continuum, they are better able to implement systems that provide information instantaneously to make critical decisions ensuring reliable and continuous operation, leveraging the combined factors of Industrial Internet of Things (IIoT) and data analytics to unleash the smart water concept. The traditionally conservative water and wastewater sector needs to adapt to ensure sustainability improvements are made for their customers, partners, and stakeholders. This pressure will be compounded by a host of economic, environmental, regulatory, and cultural changes, which will challenge the utilities core business and operating models. Successfully adapting to these challenges will give their organizations the advantage to be leaders in their approaches to water, wastewater, and stormwater management. Advances in machine learning have made it possible to apply advanced analytics to operational data on a variety of processes throughout the plant. Robust learning from streaming data has made it possible to extract valuable insight from available operational data, making it possible to optimize processes utilizing a new or existing systems to drive operational improvements in whatever stage of the digital transformation journey the utility is in. We will highlight examples and use cases leveraging AI to consume available data (electrical signals, vibration data, or process measurements) on-site to automatically determine whether a pump, a motor, or a process is deviating from normal operation. Results show that the AI-enabled data-driven and condition-based monitoring has increased facility maintenance productivity and reduced unplanned downtime. Additionally, we will demonstrate how AI can monitor a controller, determine performance deteriorations of the operation being controlled, and by mining best past practices, recommend and deploy corrective action that restores optimal controller and process behavior. Unlike more rigid control optimization techniques, these continuous learning and AI-driven modifications to the control system can respond to the presence of uncontrolled variables that directly affect water and wastewater treatment. As technology advances, it is important to understand how it can be applied and interact with existing systems in the plant to increase efficiency and reliability. Please join us to learn more about how the digital transformation in the water and wastewater sector is advancing and how AI can be successfully implemented to improve sustainability and operational outcomes in energy usage, water savings, operator interventions, and reduction in waste across all aspects of a utility's operations, including pump optimization, reservoir and stormwater management, and aeration basin operations.