Many utilities globally are currently dealing with the deterioration of their buried infrastructure, especially within their water and wastewater conveyance and distribution systems. Specific to wastewater collection systems, utilities also need to accommodate anticipated increased sewer flows due to increased population densities and economic development. This presentation will focus on two digital approaches to maximizing the life of these important assets: condition assessment and optimization of operations and maintenance. Condition assessment has been a necessary process to cost-effectively identify which assets are the most deteriorated and in need of rehabilitation or replacement. Utilities are advised to adopt proactive strategies instead of reactive ones to help prioritize operational budgets and establish a reliable and resilient conveyance system. This portion of the presentation will focus on the advent and utilization of Artificial Intelligence (AI) and Machine Learning (ML) tools for the effective management of buried assets and providing prescriptive asset management guidance. Some of the key benefits of using AI are increasing accuracy and consistency and providing objective data to enable utilities to make informed data-driven decisions. In addition, AI is faster and more consistent in analyzing inspection videos and identifying trends than human data interpretation. The analysis speed also aids in lowering the cost of data interpretation. The presentation will describe a multi-stage AI-enabled process that performs defect analysis of closed circuit television (CCTV) inspection videos and provides guidance on what remediation steps a utility should take. The workflow offers a detailed defect summary using NASSCO standards, a compilation of defect images, and a dashboard of results comprising risk scores for each pipe, recommendations for rehabilitation or replacement of failing pipes, and AACE International Class V construction cost estimates. In addition to condition assessment, utilities are facing increasing pressures in operating and maintaining their collection and treatment systems, including staffing issues due to retirements and staff retention, shrinking budgets, and more stringent regulations. These pressures lead utilities to new ways of working, including using a smart sewers approach. The second half of the presentation will focus on a tool that provides a dashboard for smart sensors from multiple platforms and machine learning/predictive analytics to provide new and better ways of operating wastewater collection systems. This tool provides new and better insights for managing asset performance by integrating data sources in a secure manner, assisting with preventive and predictive maintenance. A case study to show the results of this modular approach will be presented.